Association between the preoperative C-reactive protein-albumin-lymphocyte index and prognosis after hepatic resection for hepatocellular carcinoma

Tomonari Shimagaki; Atsushi Maeda; Tomoya Harima; Akihiro Sakai; Rumi Matono; Naotaka Hashimoto; Shohei Yamaguchi; Kozo Konishi; Kenkichi Hashimoto; Takashi Maeda

doi:10.1371/journal.pone.0358776

. 2026 Sep 30;21(9):e0358776. doi: 10.1371/journal.pone.0358776

Association between the preoperative C-reactive protein-albumin-lymphocyte index and prognosis after hepatic resection for hepatocellular carcinoma

Tomonari Shimagaki ^1,^*, Atsushi Maeda ¹, Tomoya Harima ¹, Akihiro Sakai ¹, Rumi Matono ¹, Naotaka Hashimoto ¹, Shohei Yamaguchi ¹, Kozo Konishi ¹, Kenkichi Hashimoto ¹, Takashi Maeda ¹

Editor: Takehiko Hanaki²

PMCID: PMC13626314 PMID: 42814752

Abstract

Background

Postoperative recurrence after hepatic resection for hepatocellular carcinoma (HCC) remains frequent. Readily available biomarkers that integrate host inflammatory, immune, and nutritional statuses may improve risk stratification. In this study, we evaluated the prognostic significance of the preoperative C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index in patients who underwent resection for primary HCC.

Methods

This retrospective, single-center study included 611 patients who underwent hepatic resection for primary HCC between 2004 and 2022. The CALLY index was defined as (albumin × lymphocytes)/(CRP × 10⁴). Patients were classified into high (≥ 2.2) and low (< 2.2) CALLY index groups. Clinicopathological factors, disease-free survival (DFS), and overall survival (OS) were compared. Prognostic factors were assessed using Cox proportional hazards models, and propensity-score matching was performed.

Results

The low CALLY index group had lower albumin levels, higher CRP and alpha-fetoprotein levels, larger tumors, and more severe postoperative complications than the high CALLY index group. Patients with low CALLY index had significantly worse DFS and OS than those with high CALLY index. Multivariate analysis identified that the low CALLY index was independently associated with worse DFS (hazard ratio (HR), 1.86; 95% confidence interval (CI), 1.40–2.47; P < 0.0001) and OS (HR, 1.93; 95% CI, 1.37–2.72; P = 0.0002). After propensity-score matching, the low CALLY index group continued to have significantly poorer DFS and OS rates than the high CALLY index group.

Conclusions

The preoperative CALLY index was independently associated with DFS and OS after hepatic resection for primary HCC. This simple biomarker may help identify patients at a high risk of recurrence and poor survival.

Introduction

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide. Hepatic resection remains a potentially curative treatment for select patients with preserved liver function and technically resectable disease [1]. However, postoperative recurrence is common, even after resection with curative intent, and is a major determinant of long-term survival [2,3]. Therefore, accurate perioperative risk stratification is clinically important for tailoring surveillance intensity, selecting candidates for adjuvant or clinical trial strategies, and counseling patients on prognosis [3–5].

Prognosis after HCC resection is determined not only by tumor burden and pathological aggressiveness, but also by host-related factors, including hepatic functional reserve, systemic inflammation, nutritional status, and antitumor immunity [4,6,7]. Conventional liver function assessments, such as the Child-Pugh classification [6], model for end-stage liver disease (MELD) score, fibrosis-4 (FIB-4) index, indocyanine green retention rate at 15 min (ICG R15), and albumin–bilirubin (ALBI) grade [8] are widely used to guide treatment selection and estimate prognosis [9]. However, these measures do not fully capture the systemic inflammatory and immunonutritional milieu, which may influence recurrence and survival.

The C-reactive protein (CRP)-albumin-lymphocyte (CALLY) index is a simple composite marker that is calculated using routinely available laboratory parameters [10]. Because CRP reflects systemic inflammation, albumin reflects hepatic synthetic function and nutritional status, and lymphocyte count reflects host immune competence, the CALLY index may provide an integrated assessment of the biological conditions that influence postoperative outcomes [11,12]. Previous studies have suggested the prognostic utility of the CALLY index in patients with HCC and other gastrointestinal malignancies. However, further validation in independent surgical cohorts remains warranted [13,14].

In this study, we aimed to evaluate the prognostic significance of the preoperative CALLY index in patients who underwent hepatic resection for primary HCC. We compared the clinicopathological features and long-term outcomes between patients with high and low CALLY index values, examined the independent prognostic value of the CALLY index for disease-free survival (DFS) and overall survival (OS), compared its prognostic discrimination with established liver function indices, and performed propensity-score matching to reduce baseline imbalance between the groups.

Materials and methods

Study design

In this retrospective cohort study, we reviewed 1089 consecutive patients who underwent hepatic resection for liver tumors at Hiroshima Red Cross Hospital and Atomic-bomb Survivors Hospital between 2004 and 2022. Among 1,089 consecutive patients who underwent hepatic resection for liver tumors between 2004 and 2022, 478 patients were excluded for the following reasons: non-HCC liver tumors (n = 68), recurrent HCC (n = 381), and missing laboratory data required for calculation of the CALLY index (n = 29). Finally, 611 patients who underwent initial hepatic resection for primary HCC were included in the analysis. The detailed patient selection process is shown in Fig 1. This retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics and Indications Committee of Hiroshima Red Cross Hospital and Atomic-bomb Survivors Hospital (approval No. 2021−029). Data were accessed for research purposes on June 24, 2025. Because of the retrospective observational design, the requirement for written informed consent was waived by the ethics committee. Information about the study was disclosed through an opt-out approach, and patients or their representatives were given the opportunity to decline participation. For patients who had died or could not be contacted, the opt-out procedure approved by the ethics committee was applied. Authorized investigators accessed identifiable clinical records only for data collection. After data extraction, direct identifiers were removed, and statistical analyses were performed using a de-identified dataset.

Fig 1 — Among 1,089 patients who underwent hepatic resection for liver tumors between 2004 and 2022, 478 were excluded because of non-HCC liver tumors (n = 68), recurrent HCC (n = 381), or missing laboratory data required for calculation of the CALLY index (n = 29). Finally, 611 patients who underwent initial hepatic resection for primary HCC were included. Patients were divided into the high CALLY index group (≥ 2.2; n = 535) and low CALLY index group (< 2.2; n = 76), and propensity-score matching generated 66 matched pairs.

All relevant data underlying the findings of this study are provided in the Supporting Information files. The de-identified minimal dataset used to reproduce the baseline tables, survival analyses, time-dependent ROC analyses, and propensity-score matching analysis is provided as S1 Dataset.

Preoperative evaluation and treatment

Preoperative evaluations included laboratory tests, liver function assessment, and tumor marker and imaging studies. Hepatic functional reserve was assessed using standard clinical parameters, including serum albumin, bilirubin, prothrombin time, platelet count, ICG R15, ALBI score, easy-ALBI (EZ-ALBI) score [15], Child-Pugh classification, MELD score, and FIB-4 index, as appropriate [8]. The extent of hepatic resection and operative approach were determined according to the tumor location and extent, liver functional reserve, and general condition of the patient. Open and laparoscopic resections were performed.

Definition of the CALLY index

The preoperative CALLY index was calculated using laboratory values obtained before surgery according to the following formula: CALLY index = (serum albumin (g/dL) × lymphocyte count (cells/μL))/(CRP (mg/dL) × 10⁴) [10]. The cutoff value of the CALLY index was determined using receiver operating characteristic curve analysis with overall survival as the outcome. The optimal cutoff value was selected using the Youden index, and the resulting cutoff value was 2.2. The ROC curve used for cutoff selection is shown in Supplementary Figure 1. Because this cutoff was derived from the present cohort and was not prespecified from an external dataset, it was considered exploratory. To reduce the limitations associated with dichotomization, the CALLY index was also analyzed as a continuous variable in Cox proportional hazards models.

Laboratory measurements and calculation of the CALLY index

Preoperative laboratory data were obtained within 7 days before hepatic resection. The CALLY index was calculated using serum albumin, peripheral lymphocyte count, and CRP values measured before surgery. Serum albumin was expressed in g/dL, lymphocyte count in cells/μL, and CRP in mg/dL. Patients with clinically apparent active infection or acute inflammatory disease at the time of preoperative blood sampling were excluded where identified from the medical records. The lower reporting limit for CRP at our institution was 0.01 mg/dL. No patient had a CRP value of zero. Therefore, all CALLY index values could be calculated directly.

Outcomes and follow-up

The primary outcomes were OS and DFS after hepatic resection. OS was defined as the interval from the date of surgery to death from any cause or last follow-up. DFS was defined as the interval between the date of surgery and the first documented recurrence or death. Early recurrence was defined as recurrence within 1 year after surgery. Postoperative surveillance was performed using tumor marker assessment and imaging examinations according to institutional practice. The patients were followed up every 3–6 months until death or loss to follow-up. Follow-up duration was calculated from the date of surgery to the date of last follow-up or death.

Sensitivity analysis according to treatment era

Because the study period extended from 2004 to 2022, temporal changes in surgical techniques, imaging modalities, surveillance strategies, antiviral treatment, and treatment after recurrence may have influenced postoperative outcomes. Therefore, we performed a sensitivity analysis according to treatment era. Patients were divided into two groups according to the year of surgery: 2004–2013 and 2014–2022. OS and DFS were compared between the high and low CALLY index groups within each treatment era using the Kaplan–Meier method and log-rank test.

Treatment after recurrence

Treatment after recurrence was reviewed from the medical records and classified according to the initial treatment administered for the first recurrence. Treatment categories included repeat hepatic resection, radiofrequency ablation, transarterial chemoembolization or transarterial embolization, systemic therapy, radiotherapy, best supportive care, and unknown or unavailable treatment information.

Clinicopathological variables

Clinicopathological variables included sex, age, hypertension, diabetes mellitus, viral hepatitis status, liver function parameters, inflammatory markers, tumor markers such as alpha-fetoprotein (AFP) and des-γ-carboxy prothrombin (DCP), maximum tumor size, tumor number, operative variables, postoperative complications, vascular invasion, tumor differentiation, and intrahepatic metastasis. Postoperative complications were graded according to the Clavien–Dindo classification, and grade III or higher complications were considered severe.

Variables were selected for multivariable Cox models based on clinical relevance and statistical significance in univariate analysis. To avoid model overfitting, the number of covariates was considered in relation to the number of observed events. Variables that were components of the CALLY index were not entered into the same model as the CALLY index. In addition, ALBI and EZ-ALBI scores include albumin-related information; therefore, models including the CALLY index and albumin-based liver function indices were interpreted with caution because of potential collinearity.

Missing data were observed for diabetes mellitus in one patient. Complete-case analysis was performed for variables included in each model.

Propensity score matching analysis

Propensity-score matching was performed as a sensitivity analysis to reduce measured baseline imbalance between patients with high and low CALLY index values. The propensity score was estimated using a logistic regression model in which the dependent variable was the CALLY index group. The covariates included liver function, tumor multiplicity, viral etiology, AFP level, DCP level, maximum tumor size, and vascular invasion. These variables were selected based on clinical relevance and established prognostic importance after hepatic resection for HCC. The individual components of the CALLY index, including albumin, CRP, and lymphocyte count, were not included in the propensity-score model to avoid overadjustment.

One-to-one nearest-neighbor matching was performed without replacement using a caliper width of 0.2 of the standard deviation of the logit of the propensity score. Patients outside the region of common support were not matched. Complete-case analysis was performed for the variables included in the propensity-score model. Covariate balance before and after matching was assessed using standardized mean differences, and an absolute standardized mean difference <0.1 was considered to indicate adequate balance. Survival outcomes in the matched cohort were evaluated using Kaplan–Meier curves and compared using the log-rank test. Because the CALLY index is a prognostic biomarker rather than a treatment exposure, the matched analysis was interpreted as a sensitivity analysis of prognostic association rather than a causal effect analysis.

Statistical analysis

Continuous variables are presented as median (interquartile range), and categorical variables are presented as numbers and percentages. Differences between the groups (high vs. low CALLY index group) were assessed using the Mann–Whitney U test. Associations between variables were determined using Fisher’s exact test or the chi-square test, as appropriate. Survival curves were estimated using the Kaplan–Meier method and compared using the log-rank test. Cox proportional hazards regression models were used to identify factors associated with DFS and OS. The proportional hazards assumption was assessed graphically using survival plots and residual diagnostics available. Variables with clinical relevance or statistical significance in the univariate analysis were used in multivariate models. Some continuous variables were categorized using clinically relevant or cohort-derived thresholds for Cox regression analyses; however, the CALLY index was additionally analyzed as a continuous variable in sensitivity analyses. Receiver operating characteristic (ROC) curves were used to compare the prognostic discrimination of the CALLY index with the established liver function indices for predicting DFS, early recurrence, and OS. Statistical analyses were performed using JMP Pro software (version 19.0.1; SAS Institute, Cary, NC, USA). Statistical significance was set at P < 0.05.

Because DFS and OS are censored time-to-event outcomes, conventional ROC analysis was not used as the primary method for evaluating prognostic discrimination for these endpoints. Time-dependent ROC analyses were performed using EZR (version 1.70; Jichi Medical University, Tochigi, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria) at prespecified time points of 1, 3, and 5 years after hepatic resection. Time-dependent AUCs were calculated for the CALLY index and established liver function indices, including ALBI score, EZ-ALBI score, FIB-4 index, MELD score, and Child-Pugh classification. Conventional ROC analysis was retained only for early recurrence within 1 year after surgery, which was evaluated as a binary outcome.

Results

Patient characteristics

The flowchart of this study is shown in Fig 1. Of the 1,089 patients who underwent hepatic resection for liver tumors during the study period, 478 were excluded: 68 had non-HCC liver tumors, 381 had recurrent HCC, and 29 had missing laboratory data required for calculation of the CALLY index. The final cohort consisted of 611 patients with primary HCC. The cohort included 422 men and 189 women, with a median age of 71 years (interquartile range, 64–77 years) (Table 1). The underlying liver diseases included hepatitis B virus infection (101 patients), hepatitis C virus infection (349 patients), and non-B, non-C disease (161 patients). Based on a preoperative CALLY index cutoff of 2.2, 535 patients were classified into the high CALLY index group and 76 into the low CALLY index group. During follow-up, DFS events occurred in 369 patients and 242 patients were censored for DFS. For OS, death occurred in 233 patients and 378 patients were censored.

Table 1. Comparative analysis of clinicopathological parameters between the high and low CALLY index groups.

Variable	Total (n = 611)	High CALLY index group (n = 535)	Low CALLY index group (n = 76)	P-value
Sex (male/female)	422/189	368/167	54/22	0.6890
Age (years)	71 (64–77)	71 (64–77)	71 (62–77)	0.5767
Hypertension (yes/no)	349/262	302/233	47/29	0.3834
DM (yes/no)	225/385	194/340	31/45	0.4509
HBV/HCV/NBNC	101/349/161	89/317/129	12/32/32	0.0030
Serum albumin (g/dL)	4.1 (3.7-4.3)	4.1 (3.7-4.4)	3.8 (3.5-4.1)	< 0.0001
Total bilirubin (mg/dL)	0.7 (0.6-1.0)	0.7 (0.6-0.9)	0.8 (0.5-1.1)	0.1939
AST (IU/L)	36 (25-52)	35 (25-52)	41 (28-57)	0.5055
ALT (IU/L)	33 (23-51)	33 (23-51)	35 (21-52)	0.9367
PT (%)	92 (83-101)	92 (83-101)	90 (78-97)	0.1791
Platelet (×10⁴/μL)	14.4 (10.1-18.1)	13.9 (10.0-17.7)	16.6 (10.5-21.9)	0.0043
ICG R15 (%)	13.9 (9.0-21.7)	13.7 (8.9-21.3)	15.3 (9.8-22.6)	0.2085
CRP (mg/dL)	0.06 (0.05-0.16)	0.05 (0.05-0.11)	0.62 (0.40-1.90)	< 0.0001
ALBI score	−2.7 (−3.0--2.4)	−2.7 (−3.0--2.5)	−2.5 (−2.8--2.2)	< 0.0001
ALBI grade, 1/2/3	380/231/0	352/183/0	28/48/0	< 0.0001
EZ-ALBI score	−35.6 (−38.2--32.7)	−36.1 (−38.4--32.8)	−32.8 (−36.1--30.1)	< 0.0001
EZ-ALBI grade, 1/2/3	379/232/0	350/185/0	29/47/0	< 0.0001
AFP (ng/mL)	11.1 (4.5–47.0)	10.9 (4.4–47.0)	12.7 (4.9–61.0)	0.0005
DCP (mAU/mL)	50 (24–172)	48 (23–160)	106 (26–892)	< 0.0001
Maximum tumor size (cm)	2.4 (1.7-3.5)	2.3 (1.7-3.3)	3.4 (1.8-5.9)	< 0.0001
Tumor nodules (single/multiple)	445/166	390/145	55/21	0.9227
Operation time (min)	222 (172-294)	220 (172-293)	246 (169-311)	0.0585
Blood loss (mL)	270 (115-553)	265 (118-557)	311 (87-533)	0.9568
Open surgery/laparoscopic surgery	560/51	494/41	66/10	0.1051
Postoperative complication CD (0–2/ ≥ 3)	561/50	496/39	65/11	0.0325
Vascular invasion (vp, vv, va) (yes/no)	227/384	197/338	30/46	0.6545
Differentiation (well/moderate/poor)	46/413/152	42/367/126	4/46/26	0.1181
IM (yes/no)	85/526	69/466	16/60	0.0546

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CALLY, C-reactive protein-albumin-lymphocyte; DM, diabetes mellitus; HBV, hepatitis B virus; HCV, hepatitis C virus; NBNC, non-B non-C hepatitis; AST, aspartate aminotransferase; ALT, alanine aminotransferase; PT, prothrombin time; ICG R15, indocyanine green retention rate at 15 min; ALBI score, Albumin-Bilirubin score; EZ-ALBI score, Easy Albumin-Bilirubin score; AFP, alpha-fetoprotein; DCP, des-γ-carboxy prothrombin; CD, Clavien–Dindo classification; vp, portal vein invasion; vv, hepatic vein invasion; va, hepatic artery invasion; IM, intrahepatic metastasis

Continuous variables are presented as median (interquartile range), and categorical variables are presented as numbers. P values were calculated using the Mann–Whitney U test, chi-square test, or Fisher’s exact test, as appropriate.

Boldface p-values indicate statistical significance.

Data were missing for diabetes mellitus in one patient. The number of patients with available data is shown where applicable.

Missing data were minimal. Diabetes mellitus status was missing in one patient, whereas data for albumin, CRP, lymphocyte count, AFP, DCP, tumor size, vascular invasion, and survival outcomes were available for all 611 patients. The details of missing data are shown in Supplementary Table 1.

Treatment after recurrence

Among patients who developed recurrence, treatment after recurrence varied according to recurrence pattern, hepatic functional reserve, general condition, and treatment era. The initial treatment for recurrence included repeat hepatic resection, radiofrequency ablation, transarterial chemoembolization or transarterial embolization, systemic therapy, radiotherapy, and best supportive care. The distribution of initial post-recurrence treatment is shown in Supplementary Table 2.

Table 2. Univariate and multivariate Cox regression analyses of the clinicopathological variables related to disease-free survival after hepatic resection for hepatocellular carcinoma.

Variable	Univariate analysis			Multivariate analysis
	HR	95% CI	P-value	HR	95% CI	P-value
Sex (male)	1.05	0.85–1.32	0.6228
Age (≥ 69.8 vs. < 69.8)	1.20	0.97–1.47	0.0871
Hypertension (yes/no)	1.03	0.84–1.26	0.7947
DM (yes/no)	0.94	0.76–1.16	0.5415
Serum albumin (≦ 3.5 vs. > 3.5)	1.52	1.10–2.10	0.0116
Total bilirubin (≦ 0.8 vs. > 0.8)	0.92	0.75–1.13	0.4404
Platelet (≦ 15.1 vs. > 15.1 × 10⁴)	1.23	0.84–1.49	0.1816
AST (≦ 44 vs. > 44)	0.80	0.65–0.98	0.0335	0.89	0.64–1.22	0.4599
ALT (≦ 42 vs. > 42)	0.81	0.66–0.99	0.0485	0.96	0.69–1.33	0.7971
ICG R15 (≦ 16.4 vs. > 16.4%)	0.79	0.64–0.97	0.0249	0.84	0.68–1.04	0.1181
AFP (≦ 11.1 vs. > 11.1)	0.65	0.53–0.80	< 0.0001	0.78	0.63–0.97	0.0280
DCP (≦ 50 vs. > 50)	0.59	0.48–0.72	< 0.0001	0.70	0.56–0.87	0.0011
Maximum tumor size (≦ 3.1 vs. > 3.1 cm)	0.74	0.59–0.92	0.0064	0.84	0.67–1.06	0.1368
Tumor nodules (single/multiple)	0.54	0.44–0.68	< 0.0001	0.64	0.51–0.81	0.0002
Open surgery/laparoscopic surgery	0.78	0.53–1.15	0.2086
Postoperative complication CD (0–2/ ≥ 3)	0.80	0.54–1.19	0.2694
Vascular invasion (vp, vv, va) (yes/no)	1.38	1.12–1.70	0.0024	1.15	0.91–1.44	0.2392
Differentiation (poorly)	1.34	1.06–1.68	0.0144	1.09	0.86–1.40	0.4713
IM (yes/no)	2.06	1.55–2.72	< 0.0001	1.46	1.08–1.97	0.0126
ALBI grade 1/2	0.71	0.58–0.87	0.0011
EZ-ALBI grade 1/2	0.68	0.55–0.83	0.0002
CALLY index (< 2.2 vs. ≥ 2.2)	1.94	1.47–2.57	< 0.0001	1.86	1.40–2.47	< 0.0001
CALLY index, continuous	0.98	0.97-0.99	< 0.0001

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HR, hazard ratio; CI, confidence interval; DM, diabetes mellitus; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ICG R15, indocyanine green retention rate at 15 min; AFP, alpha-fetoprotein; DCP, des-γ-carboxy prothrombin; CD, Clavien–Dindo classification; vp, portal vein invasion; vv, hepatic vein invasion; va, hepatic artery invasion; IM, intrahepatic metastasis; ALBI, Albumin-Bilirubin; EZ-ALBI, Easy Albumin-Bilirubin; CALLY, C-reactive protein-albumin-lymphocyte

Bold p-values are statistically significant

Comparison of clinicopathological characteristics according to the CALLY index

The clinicopathological characteristics are summarized in Table 1. Compared with the high CALLY index group, the low CALLY index group had significantly lower serum albumin levels (P < 0.0001), higher CRP levels (P < 0.0001), higher platelet counts (P = 0.0043), worse ALBI and EZ-ALBI scores, higher AFP levels, and larger tumors (P < 0.0001). Severe postoperative complications were also more frequent in the low CALLY index group (P = 0.0325). The high and low CALLY index groups showed no significant differences in terms of sex, age, diabetes mellitus, total bilirubin, transaminases, ICG R15, operative blood loss, tumor multiplicity, vascular invasion, differentiation, or intrahepatic metastasis.

Poor DFS and OS in the low CALLY index group

The low CALLY index group had significantly worse OS and DFS than the high CALLY index group (both, P < 0.0001) (Fig 2A, B). These results indicated that the low CALLY index group had a high HCC recurrence rate and poor prognosis.

Sensitivity analysis by treatment era

To evaluate the possible influence of temporal changes in treatment and surveillance, survival analyses were performed separately according to treatment era. In the 2004–2013 cohort, the low CALLY index group had significantly worse OS and DFS than the high CALLY index group (OS, P = 0.0003; DFS, P = 0.0012). Similarly, in the 2014–2022 cohort, the low CALLY index group had significantly worse OS and DFS than the high CALLY index group (OS, P = 0.0024; DFS, P = 0.0002). These findings suggest that the association between low CALLY index and poor postoperative outcomes was consistent across treatment eras (Supplementary Figure 2).

Prognostic factors for DFS

Univariate Cox regression analysis identified several factors associated with DFS, including serum albumin, aspartate aminotransferase (AST), alanine aminotransferase, ICG R15, AFP, DCP, maximum tumor size, tumor multiplicity, vascular invasion, poor differentiation, intrahepatic metastasis, ALBI grade, EZ-ALBI grade, and CALLY index. In the multivariate analysis, AFP, DCP, tumor multiplicity, intrahepatic metastasis, and a low CALLY index were independently associated with DFS. A low CALLY index was associated with an approximately 1.9-fold higher risk of recurrence (hazard ratio [HR], 1.86; 95% confidence interval [CI], 1.40–2.47; P < 0.0001; Table 2).

Prognostic factors for OS

Univariate Cox regression analysis showed that age, serum albumin, AST, ICG R15, AFP, DCP, maximum tumor size, tumor multiplicity, severe postoperative complications, vascular invasion, poor differentiation, intrahepatic metastasis, ALBI grade, EZ-ALBI grade, and CALLY index were associated with OS. In multivariate analysis, age, ICG R15, AFP, DCP, maximum tumor size, tumor multiplicity, severe postoperative complications, poor differentiation, intrahepatic metastasis, and low CALLY index remained independently associated with OS. A low CALLY index was independently associated with worse OS (HR, 1.93; 95% CI, 1.37–2.72; P = 0.0002; Table 3).

Table 3. Univariate and multivariate Cox regression analyses of the clinicopathological variables related to overall survival after hepatic resection for hepatocellular carcinoma.

Variable	Univariate analysis			Multivariate analysis
	HR	95% CI	P-value	HR	95% CI	P-value
Sex (male)	1.17	0.89–1.54	0.2696
Age (≥ 69.8 vs. < 69.8)	1.68	1.29–2.20	0.0002	1.71	1.30–2.25	0.0001
Hypertension (yes/no)	1.05	0.81–1.36	0.7221
DM (yes/no)	1.01	0.78–1.32	0.9227
Serum albumin (≦ 3.5 vs. > 3.5)	1.98	1.37–2.87	0.0003
Total bilirubin (≦ 0.8 vs. > 0.8)	0.82	0.64–1.06	0.1356
Platelet (≦ 15.1 vs. > 15.1 × 10⁴)	1.26	0.79–1.46	0.2582
AST (≦ 44 vs. > 44)	0.73	0.56–0.94	0.0169	0.88	0.67–1.15	0.3516
ALT (≦ 42 vs. > 42)	0.95	0.73–1.24	0.7167
ICG R15 (≦ 16.4 vs. > 16.4%)	0.59	0.45–0.76	< 0.0001	0.64	0.49–0.84	0.0012
AFP (≦ 11.1 vs. > 11.1)	0.49	0.37–0.64	< 0.0001	0.62	0.46–0.82	0.0009
DCP (≦ 50 vs. > 50)	0.50	0.39–0.66	< 0.0001	0.68	0.51–0.89	0.0062
Maximum tumor size (≦ 3.1 vs. > 3.1 cm)	0.60	0.46–0.79	0.0002	0.75	0.56–0.99	0.0489
Tumor nodules (single/multiple)	0.61	0.47–0.81	0.0004	0.75	0.56–0.99	0.0475
Open surgery/laparoscopic surgery	0.83	0.43–1.63	0.5970
Postoperative complication CD (0–2/ ≥ 3)	0.42	0.28–0.62	< 0.0001	0.61	0.40–0.92	0.0180
Vascular invasion (vp, vv, va) (yes/no)	1.55	1.19–2.00	0.0010	1.03	0.77–1.37	0.8358
Differentiation (poorly)	1.94	1.48–2.55	< 0.0001	1.53	1.14–2.07	0.0051
IM (yes/no)	2.66	1.94–3.66	< 0.0001	1.90	1.35–2.67	0.0002
ALBI grade 1/2	0.49	0.38–0.63	< 0.0001
EZ-ALBI grade 1/2	0.48	0.37–0.62	< 0.0001
CALLY index (< 2.2 vs. ≥ 2.2)	2.10	1.50–2.93	< 0.0001	1.93	1.37–2.72	0.0002
CALLY index, continuous	0.97	0.96-0.98	< 0.0001

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HR, hazard ratio; CI, confidence interval; DM, diabetes mellitus; AST, aspartate aminotransferase; ALT, alanine aminotransferase; ICG R15, indocyanine green retention rate at 15 min, AFP, alpha-fetoprotein; DCP, des-γ-carboxy prothrombin; CD, Clavien–Dindo classification; vp, portal vein invasion; vv, hepatic vein invasion; va, hepatic artery invasion; IM, intrahepatic metastasis; ALBI, Albumin-Bilirubin; EZ-ALBI, Easy Albumin-Bilirubin; CALLY, C-reactive protein-albumin-lymphocyte.

Bold p-values are statistically significant.

In addition to the cutoff-based analysis, the CALLY index was analyzed as a continuous variable. Lower CALLY index values were associated with poorer DFS and OS in Cox proportional hazards models (Table 2 and 3).

Prognostic discrimination of the CALLY index

Because DFS and OS were censored time-to-event outcomes, prognostic discrimination was evaluated using time-dependent ROC analysis. The time-dependent AUCs of the CALLY index for DFS at 1, 3, and 5 years were 0.523, 0.529, and 0.509, respectively. The corresponding AUCs for OS were 0.543, 0.541, and 0.548, respectively (Table 4). The CALLY index showed numerically favorable discrimination for OS (Table 4) and early recurrence within 1 year after surgery (Table 5) compared with several liver function indices; however, the absolute AUC was modest. Overall, the CALLY index showed modest prognostic discrimination. Therefore, the results suggest that the CALLY index may provide adjunctive prognostic information rather than serving as a stand-alone predictor.

Table 4. Time-dependent area under the curve (AUC) for disease-free survival (DFS) and overall survival (OS).

Index	1-year DFS AUC	3-year DFS AUC	5-year DFS AUC	1-year OS AUC	3-year OS AUC	5-year OS AUC
CALLY index	0.523	0.529	0.509	0.543	0.541	0.548
ALBI score	0.569	0.544	0.570	0.444	0.474	0.508
EZ-ALBI score	0.564	0.550	0.566	0.449	0.486	0.500
Child-Pugh classification	0.541	0.527	0.508	0.486	0.476	0.487
MELD score	0.507	0.538	0.550	0.400	0.506	0.539
FIB-4 index	0.636	0.636	0.616	0.530	0.508	0.502

Open in a new tab

AUC, area under the curve; DFS, disease-free survival; OS, overall survival; CALLY, C-reactive protein-albumin-lymphocyte; ALBI, Albumin-Bilirubin; EZ-ALBI, Easy Albumin-Bilirubin; MELD, model for end-stage liver disease; FIB-4, fibrosis-4.

Table 5. Diagnostic accuracy of liver function assessment for predicting early recurrence (within 1 year after surgery) of resected hepatocellular carcinoma (n = 611).

Index	AUC	Sensitivity (%)	Specificity (%)
CALLY index	0.60121	53.5	63.8
EZ-ALBI score	0.56061	91.3	18.6
ALBI score	0.56991	90.6	23.6
Child-Pugh classification	0.55823	29.1	83.1
MELD score	0.51324	81.9	25.6
FIB-4 index	0.54309	23.6	86.2

Open in a new tab

AUC, area under the curve; CALLY, C-reactive protein-albumin-lymphocyte; EZ-ALBI, Easy Albumin-Bilirubin; ALBI, Albumin-Bilirubin; MELD, model for end-stage liver disease; FIB-4, fibrosis-4.

Propensity-score matched analysis

Propensity-score matching generated 66 matched pairs. After matching, baseline covariates were better balanced between the high and low CALLY index groups. The absolute standardized mean differences of the matched covariates were reduced after matching, and most variables showed an absolute standardized mean difference <0.1 (Table 6). In the matched cohort, Kaplan–Meier curves showed significant separation between the low and high CALLY index groups for OS (P = 0.0234; Fig 3A) and DFS (P = 0.0317; Fig 3B). Because the CALLY index is a prognostic biomarker rather than a treatment exposure, this matched analysis was interpreted as a sensitivity analysis of prognostic association.

Table 6. Patient characteristics in the high and low CALLY index groups after propensity-score matching.

Variable	High CALLY index group (n = 66)	Low CALLY index group (n = 66)	P-value
Sex (male/female)	51/15	49/17	0.6846
Age (years)	70 (62-77)	70 (62-78)	0.8197
DM (yes/no)	27/39	26/40	0.8591
HBV/HCV/NBNC	12/29/25	11/29/26	0.9689
Performance status (0–1/ ≥ 2)	66/0	65/1	0.3155
Total bilirubin (mg/dL)	0.7 (0.5-1.1)	0.7 (0.6-1.0)	0.6827
AST (IU/L)	39 (28-52)	36 (25-53)	0.4290
ALT (IU/L)	33 (21-52)	32 (24-50)	0.3790
PT (%)	90 (77-97)	95 (85-101)	0.3608
Platelet (×10⁴/μL)	16.6 (10.5-20.6)	16.8 (11.2-22.7)	0.3192
ICG R15 (%)	15.3 (10.5-22.3)	12.5 (10.0-19.6)	0.3108
AFP (ng/mL)	14.7 (4.3–77.0)	11.1 (4.4–28.9)	0.5641
DCP (mAU/mL)	60 (24–450)	64 (24–233)	0.5907
Maximum tumor size (cm)	3.0 (1.8-4.5)	3.3 (2.0-5.1)	0.3749
Tumor nodules (single/multiple)	48/18	49/17	0.8437
Operation time (min)	229 (164-297)	246 (175-320)	0.5301
Blood loss (mL)	284 (84-502)	263 (145-635)	0.5024
Postoperative complication CD (0–2/ ≥ 3)	63/3	58/8	0.1154
Vascular invasion (vp, vv, va) (yes/no)	31/35	26/40	0.3796
Differentiation (poorly)	18/48	20/46	0.7006
IM (yes/no)	8/58	12/54	0.3315

Open in a new tab

CALLY, C-reactive protein-albumin-lymphocyte; DM, diabetes mellitus; HBV, hepatitis B virus; HCV, hepatitis C virus; NBNC, non-B non-C hepatitis; AST, aspartate aminotransferase; ALT, alanine aminotransferase; PT, prothrombin time; ICG R15, indocyanine green retention rate at 15 min; AFP, alpha-fetoprotein; DCP, des-γ-carboxy prothrombin; CD, Clavien–Dindo classification; vp, portal vein invasion; vv, hepatic vein invasion; va, hepatic artery invasion; IM, intrahepatic metastasis

Boldface p-values indicate statistical significance.

Fig 3 — (A) Overall survival. (B) Disease-free survival. P values were calculated using the log-rank test. Tick marks indicate censored observations.

Discussion

In this retrospective study that included 611 patients who underwent hepatic resection for primary HCC, the preoperative CALLY index was significantly associated with long-term outcomes. Patients with a low CALLY index had worse inflammatory and nutritional profiles, larger tumors, and poorer postoperative survival than those with high CALLY index. Importantly, a low CALLY index remained independently associated with both DFS and OS after adjusting for established tumor- and liver-related prognostic factors. The adverse prognostic association of a low CALLY index was confirmed after propensity-score matching, suggesting that this marker provides clinically relevant prognostic information beyond conventional clinicopathological variables.

The CALLY index has several advantages as a prognostic marker [13,16]. Elevated CRP is a marker of systemic inflammation that promotes tumor progression through cytokine-mediated tumor proliferation, angiogenesis, immune suppression, and cancer cachexia [12,17,18]. Albumin reflects the hepatic synthetic capacity and nutritional reserve, both of which are essential for tolerating major surgery and maintaining the antitumor defense [19–21]. Lymphocyte count reflects host cellular immunity, and lymphopenia may indicate impaired immune surveillance against residual micrometastatic disease [22]. By combining these three parameters, the CALLY index can capture the interactions between tumor-related inflammation, host immune competence, nutritional status, and hepatic functional reserve.

The present findings are consistent with those of previous reports showing that the CALLY index is associated with the prognosis after hepatectomy for HCC [10]. However, this study adds clinically useful information by evaluating a large single-center cohort of patients with primary HCC, comparing the CALLY index with multiple liver function indices, and confirming survival differences after propensity-score matching. The persistent prognostic separation after matching suggests that the CALLY index is not merely a surrogate for tumor size, liver function, or viral status but may represent an integrated host-response phenotype that influences postoperative recurrence and survival.

Risk stratification after HCC resection remains challenging because recurrence occurs through heterogeneous mechanisms, including intrahepatic metastasis from the primary tumor and de novo multicentric carcinogenesis in the diseased liver remnant [23,24]. Traditional prognostic models primarily focus on tumor burden, vascular invasion, and liver function [6]. The CALLY index may complement these parameters because it can be easily obtained from routine preoperative blood tests and does not require specialized assays [25]. Clinically, patients with low CALLY index may warrant closer postoperative surveillance, optimization of nutritional and inflammatory status, and consideration of adjuvant strategies or clinical trials, when available.

In the present analysis, we used time-dependent ROC analysis because DFS and OS are censored time-to-event outcomes. The CALLY index showed modest prognostic discrimination for OS. Therefore, the CALLY index should not be interpreted as a stand-alone prognostic tool. Rather, it may serve as an adjunctive biomarker that complements established tumor- and liver-related factors. Further studies are needed to determine whether the CALLY index improves discrimination, calibration, and clinical decision-making when added to comprehensive prognostic models.

In this study, propensity-score matching was used as a sensitivity analysis to reduce measured baseline imbalance between patients with high and low CALLY index values. However, the CALLY index is a prognostic biomarker rather than a treatment exposure; therefore, the matched analysis should not be interpreted causally. In addition, propensity-score methods can adjust only for measured covariates, and residual confounding due to unmeasured variables, such as frailty, sarcopenia, antiviral therapy, and detailed treatment after recurrence, cannot be excluded.

This study has several limitations. First, this was a retrospective study from a single institution, which may limit generalizability and introduce selection bias by the exclusion of a substantial proportion of the initially screened cohort. Second, treatment strategies, perioperative management, imaging modalities, and postoperative surveillance may have changed during the study period from 2004 to 2022. The present study included patients treated over a long period from 2004 to 2022. During this period, surgical techniques, perioperative management, imaging modalities, surveillance strategies, antiviral therapy, and treatment options for recurrent HCC changed substantially. To address this issue, we performed a sensitivity analysis stratified by treatment era and found that the association between low CALLY index and worse OS and DFS was consistent in both the 2004–2013 and 2014–2022 cohorts (Supplementary Figure 2). However, this analysis does not fully eliminate residual confounding related to temporal changes, and detailed information on antiviral therapy and post-recurrence treatment was not uniformly available throughout the entire study period. Third, treatment after recurrence was heterogeneous and may have influenced overall survival. Because post-recurrence treatment was determined after the occurrence of recurrence and was influenced by recurrence pattern, liver function, patient condition, and treatment era, it was not included in the primary baseline prognostic model. Therefore, residual confounding related to post-recurrence treatment cannot be fully excluded. Fourth, previous studies have reported different cutoff values for the CALLY index according to cancer type, treatment setting, endpoint, and statistical method, and no universally accepted threshold has been established. Therefore, the cutoff value of 2.2 used in this study was exploratory and requires external validation. To reduce the limitations associated with dichotomization, we additionally analyzed the CALLY index as a continuous variable. Fifth, information regarding the patients’ general condition and cause of death was collected retrospectively from medical records. Consequently, the precise cause of death could not be determined for all cases, and such unmeasured or incompletely measured factors may have influenced postoperative recurrence and survival.

In conclusion, the preoperative CALLY index was independently associated with DFS and OS after hepatic resection for primary HCC. Because it is inexpensive, objective, and readily available from routine laboratory data, the CALLY index may serve as an adjunctive marker for postoperative risk stratification.

Supporting information

S1 Fig. Receiver operating characteristic curve of the CALLY index for survival-group stratification after HCC resection.

HCC: hepatocellular carcinoma.

(PDF)

pone.0358776.s001.pdf^{(86.8KB, pdf)}

S2 Fig. Kaplan–Meier survival curves according to the CALLY index stratified by treatment era.

Patients were divided into two cohorts according to the year of surgery: 2004–2013 and 2014–2022. (A) Overall survival in the 2004–2013 cohort. (B) Disease-free survival in the 2004–2013 cohort. (C) Overall survival in the 2014–2022 cohort. (D) Disease-free survival in the 2014–2022 cohort. P values were calculated using the log-rank test. Tick marks indicate censored observations.

(PDF)

pone.0358776.s002.pdf^{(178.2KB, pdf)}

S1 Table. Missing data by variable.

(DOCX)

pone.0358776.s003.docx^{(38.4KB, docx)}

S2 Table. The distribution of initial post-recurrence treatment.

(DOCX)

pone.0358776.s004.docx^{(38.1KB, docx)}

S1 File. Dataset.

(XLSX)

pone.0358776.s005.xlsx^{(620KB, xlsx)}

Acknowledgments

We wish to thank all of the patients who participated in this study. We would like to thank Editage (www.editage.jp) for English language editing.

Abbreviations

HCC: hepatocellular carcinoma
MELD: model for end-stage liver disease
FIB-4: fibrosis-4
ICG R15: indocyanine green retention rate at 15 min
ALBI: albumin-bilirubin
CRP: C-reactive protein
CALLY: C-reactive protein-albumin-lymphocyte
DFS: disease-free survival
OS: overall survival
EZ-ALBI: easy albumin-bilirubin
AFP: alpha-fetoprotein
DCP: des-γ-carboxy prothrombin
AST: aspartate aminotransferase
HR: hazard ratio
CI: confidence interval
AUC: area under the curve.

Data Availability

Funding Statement

The author(s) received no specific funding for this work.

References

1.Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. [DOI] [PubMed] [Google Scholar]
2.Erridge S, Pucher PH, Markar SR, Malietzis G, Athanasiou T, Darzi A, et al. Meta-analysis of determinants of survival following treatment of recurrent hepatocellular carcinoma. Br J Surg. 2017;104(11):1433–42. doi: 10.1002/bjs.10597 [DOI] [PubMed] [Google Scholar]
3.Qin S, Chen M, Cheng A-L, Kaseb AO, Kudo M, Lee HC, et al. Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): A randomised, open-label, multicentre, phase 3 trial. Lancet. 2023;402(10415):1835–47. doi: 10.1016/S0140-6736(23)01796-8 [DOI] [PubMed] [Google Scholar]
4.Reig M, Sanduzzi-Zamparelli M, Forner A, Rimola J, Ferrer-Fàbrega J, Burrel M, et al. BCLC strategy for prognosis prediction and treatment recommendations: The 2026 update. J Hepatol. 2026;84(3):631–54. doi: 10.1016/j.jhep.2025.10.020 [DOI] [PubMed] [Google Scholar]
5.Iseda N, Itoh S, Toshima T, Yoshiya S, Bekki Y, Tsutsui Y, et al. Outcome of hepatectomy after systemic therapy for hepatocellular carcinoma: A Japanese multicenter study. Surg Today. 2025;55(4):510–7. doi: 10.1007/s00595-024-02930-x [DOI] [PubMed] [Google Scholar]
6.Singal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922–65. doi: 10.1097/HEP.0000000000000466 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Rimassa L, Chan SL, Sangro B, Lau G, Kudo M, Reig M, et al. Five-year overall survival update from the HIMALAYA study of tremelimumab plus durvalumab in unresectable HCC. J Hepatol. 2025;83(4):899–908. doi: 10.1016/j.jhep.2025.03.033 [DOI] [PubMed] [Google Scholar]
8.Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, et al. Assessment of liver function in patients with hepatocellular carcinoma: A new evidence-based approach-the ALBI grade. J Clin Oncol. 2015;33(6):550–8. doi: 10.1200/JCO.2014.57.9151 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Panettieri E, Campisi A, De Rose AM, Mele C, Giuliante F, Vauthey J-N, et al. Emerging prognostic markers in patients undergoing liver resection for hepatocellular carcinoma: A narrative review. Cancers (Basel). 2024;16(12):2183. doi: 10.3390/cancers16122183 [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Iida H, Tani M, Komeda K, Nomi T, Matsushima H, Tanaka S, et al. Superiority of CRP-albumin-lymphocyte index (CALLY index) as a non-invasive prognostic biomarker after hepatectomy for hepatocellular carcinoma. HPB (Oxford). 2022;24(1):101–15. doi: 10.1016/j.hpb.2021.06.414 [DOI] [PubMed] [Google Scholar]
11.Tsunematsu M, Haruki K, Taniai T, Tanji Y, Shirai Y, Furukawa K, et al. The impact of C-reactive protein-albumin-lymphocyte (CALLY) index on the prognosis of patients with distal cholangiocarcinoma following pancreaticoduodenectomy. Ann Gastroenterol Surg. 2022;7(3):503–11. doi: 10.1002/ags3.12637 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.She S, Shi J, Zhu J, Yang F, Yu J, Dai K. Impact of inflammation and the immune system on hepatocellular carcinoma recurrence after hepatectomy. Cancer Med. 2024;13(4):e7018. doi: 10.1002/cam4.7018 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Wu B, Liu J, Shao C, Yu D, Liao J. Integrating inflammation, nutrition, and immunity: the CALLY index as a prognostic tool in digestive system cancers - a systematic review and meta-analysis. BMC Cancer. 2025;25(1):672. doi: 10.1186/s12885-025-14074-3 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Matsui S, Kato Y, Ohgi K, Ashida R, Yamada M, Otsuka S, et al. Prognostic impact of the CALLY index in patients with resectable pancreatic cancer. Surgical Oncology Insight. 2025;2(1):100119. doi: 10.1016/j.soi.2024.100119 [DOI] [Google Scholar]
15.Shimagaki T, Sugimachi K, Tomino T, Onishi E, Toshima T, Itoh S, et al. The EZ-ALBI score as a prognostic tool for outcomes after resection of small hepatocellular carcinoma (</=3 cm ). Hepatol Res. 2025;55(8):1159–71. [DOI] [PubMed] [Google Scholar]
16.Zhao H, Yin B, Li X-R, Liu X-Y, Bu Z-T, Shi H-P. The CRP-albumin-lymphocyte index provides enhanced prognostic value in liver cancer compared to the TNM staging system. Sci Rep. 2025;15(1):20090. doi: 10.1038/s41598-025-03985-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Yamamoto M, Kobayashi T, Kuroda S, Hamaoka M, Okimoto S, Honmyo N, et al. Verification of inflammation-based prognostic marker as a prognostic indicator in hepatocellular carcinoma. Ann Gastroenterol Surg. 2019;3(6):667–75. doi: 10.1002/ags3.12286 [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Shimagaki T, Sugimachi K, Mano Y, Onishi E, Iguchi T, Nakashima Y, et al. Cachexia index as a prognostic predictor after resection of pancreatic ductal adenocarcinoma. Ann Gastroenterol Surg. 2023;7(6):977–86. doi: 10.1002/ags3.12686 [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Hiraoka A, Kumada T, Michitaka K, Toyoda H, Tada T, Ueki H, et al. Usefulness of albumin-bilirubin grade for evaluation of prognosis of 2584 Japanese patients with hepatocellular carcinoma. J Gastroenterol Hepatol. 2016;31(5):1031–6. doi: 10.1111/jgh.13250 [DOI] [PubMed] [Google Scholar]
20.Akaoka M, Haruki K, Yamahata Y, Okazaki K, Furukawa K, Tsunematsu M, et al. The prognostic impact of perioperative dynamic changes in cachexia index in patients with hepatocellular carcinoma. Ann Gastroenterol Surg. 2024;8(5):917–26. doi: 10.1002/ags3.12804 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Matsumoto T, Kitano Y, Imai K, Kinoshita S, Sato H, Shiraishi Y, et al. Clinical significance of preoperative inflammation-based score for the prognosis of patients with hepatocellular carcinoma who underwent hepatectomy. Surg Today. 2022;52(7):1008–15. doi: 10.1007/s00595-021-02427-x [DOI] [PubMed] [Google Scholar]
22.Seyhan D, Allaire M, Fu Y, Conti F, Wang XW, Gao B, et al. Immune microenvironment in hepatocellular carcinoma: from pathogenesis to immunotherapy. Cell Mol Immunol. 2025;22(10):1132–58. doi: 10.1038/s41423-025-01308-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Tampaki M, Papatheodoridis GV, Cholongitas E. Intrahepatic recurrence of hepatocellular carcinoma after resection: an update. Clin J Gastroenterol. 2021;14(3):699–713. doi: 10.1007/s12328-021-01394-7 [DOI] [PubMed] [Google Scholar]
24.Yang S-L, Luo Y-Y, Chen M, Zhou Y-P, Lu F-R, Deng D-F, et al. A systematic review and meta-analysis comparing the prognosis of multicentric occurrence and vs. intrahepatic metastasis in patients with recurrent hepatocellular carcinoma after hepatectomy. HPB (Oxford). 2017;19(10):835–42. doi: 10.1016/j.hpb.2017.06.002 [DOI] [PubMed] [Google Scholar]
25.Shiraishi T, Nonaka T, Tominaga T, Takamura Y, Oishi K, Hashimoto S, et al. The C-reactive protein-albumin-lymphocyte (CALLY) index is a useful predictor of postoperative complications in patients with a colonic stent for obstructive colorectal cancer: A Japanese multicenter study. Surg Today. 2025;55(4):502–9. doi: 10.1007/s00595-024-02924-9 [DOI] [PubMed] [Google Scholar]

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Reviewer #1: 1. Excluding 44% of the original cohort may increase the risk of bias.

2. There is a marked imbalance between the two groups, which may reduce the precision of the results.

3. What were the postoperative interventions, like TACE, RF, or chemotherapy, because all of these might affect the outcome

4. What was the cause of death, recurrence, liver failure, or any other cause?

5. Potential residual confounding despite propensity-score matching, as important variables such as frailty, sarcopenia, antiviral therapy, and performance status were not included.

Reviewer #2: Major comments

1. The manuscript should not currently be described as a validation study.

The CALLY cutoff of 2.2 appears to have been selected using ROC analysis in the same cohort in which its prognostic performance was subsequently evaluated. This represents cutoff derivation and apparent performance assessment rather than independent validation. The authors should clarify whether 2.2 was prespecified from a previous study or derived from the present data. If it was derived from this cohort, the title and conclusions should be revised, or appropriate internal validation using bootstrap resampling or cross-validation should be performed. Ideally, a previously published cutoff should be evaluated without data-driven modification.

2. The cutoff-selection procedure is insufficiently described.

The authors state that ROC analysis was used for “survival-group stratification,” but they do not specify the outcome, time horizon, censoring method, or criterion used to select the cutoff. It is unclear whether the cutoff was based on overall survival, disease-free survival, early recurrence, the Youden index, or another criterion. These details must be reported. The supplementary ROC figure should also be included and accessible to reviewers.

The authors should additionally analyze the CALLY index as a continuous variable rather than relying exclusively on dichotomization. Restricted cubic splines would help assess whether the association is linear and whether 2.2 represents a clinically meaningful threshold.

3. Standard ROC analysis is not adequate for censored survival outcomes.

Overall survival and disease-free survival are time-to-event endpoints with censoring. The reported AUC values for “predicting DFS” and “predicting OS” have no specified time horizon, and it is unclear whether censoring was handled. The authors should use time-dependent ROC analysis at prespecified time points, such as 1, 3, and 5 years, or report Harrell’s C-index. Confidence intervals should be provided for all discrimination measures.

Comparisons between the CALLY index and ALBI, EZ-ALBI, FIB-4, MELD, and Child-Pugh should include confidence intervals and formal statistical comparisons. Simply identifying the numerically highest AUC does not establish superior prognostic performance.

4. The discrimination of the CALLY index is modest and should not be overstated.

The reported AUC values are approximately 0.60 for DFS and early recurrence. This represents limited discrimination. Statements concerning “highest diagnostic accuracy,” clinical usefulness, and added prognostic value should therefore be moderated. The term “prognostic discrimination” is more appropriate than “diagnostic accuracy.”

To support claims of added value, the authors should compare a baseline clinicopathological model with the same model plus the CALLY index. Changes in the C-index, model likelihood, calibration, and, if clinical implementation is claimed, decision-curve analysis should be considered.

5. The Cox regression strategy requires substantial clarification and revision.

The numbers of recurrence events and deaths are not reported. These numbers are necessary to assess the number of events per model parameter and the risk of model overfitting. The authors should report:

Number of DFS events, recurrences, deaths, and censored observations.

Median follow-up calculated using an appropriate method, preferably reverse Kaplan-Meier.

Proportional-hazards assumption testing for each Cox model.

The complete prespecified covariate-selection strategy.

Missing-data handling and the number of patients included in each model.

Several continuous variables were dichotomized using cohort-derived means or thresholds, including age, AFP, DCP, tumor size, AST, ALT, and ICG R15. This causes loss of information and may produce unstable or biased estimates. These predictors should preferably be analyzed continuously, with transformations or spline functions where appropriate.

Potential collinearity must also be addressed because the CALLY index contains albumin, CRP, and lymphocyte count, while other models include albumin-related measures such as ALBI or EZ-ALBI.

6. The propensity-score matching analysis is inadequately reported.

The manuscript should specify the exact propensity-score model, all included covariates, matching ratio, matching algorithm, whether matching was performed with or without replacement, whether the caliper was applied to the propensity score or its logit, and how common support and missing values were handled. “Caliber” should be corrected to “caliper.”

Balance should not be assessed using nonsignificant P values. Standardized mean differences before and after matching should be reported, preferably with a balance or Love plot. A standardized mean difference below a prespecified threshold should be used to assess balance.

Because the observations are matched, the survival analyses should account for matched pairs, for example through a stratified Cox model or cluster-robust standard errors. The authors should report matched hazard ratios with 95% confidence intervals, not only log-rank P values.

The authors should also clarify the estimand and purpose of propensity-score matching. The CALLY index is a prognostic biomarker rather than a treatment exposure, and causal terms such as “effect” or “impact” should be avoided.

7. Important clinical confounders and temporal changes need consideration.

The study covers the period from 2004 to 2022, during which surgical techniques, antiviral treatment, imaging, surveillance, and management of recurrent HCC changed substantially. Year of surgery or treatment era should be examined in sensitivity analyses.

The authors should clarify whether established prognostic variables were available, including tumor stage, cirrhosis, portal hypertension, performance status, resection extent, surgical margin, antiviral treatment, and treatment after recurrence. The absence of these variables may cause residual confounding.

8. Laboratory measurement and calculation of the CALLY index require clarification.

The authors should report:

The permitted interval between blood collection and surgery.

Whether patients with active infection or acute inflammatory disorders were excluded.

The CRP assay and lower detection limit.

How CRP values of zero or below the detection limit were handled, because CRP appears in the denominator.

The units used for CRP and lymphocyte count.

The distribution of lymphocyte count and the CALLY index in the total cohort and both groups.

The definitions “high >2.2” and “low <2.2” leave patients with a value exactly equal to 2.2 unclassified. One group should use an inclusive boundary.

9. Patient selection and missing data should be reported transparently.

The flow diagram shows a reduction from 1,089 patients undergoing hepatic resection to 611 included patients, but it does not provide the number excluded for each reason. The authors should report separate counts for non-HCC tumors, recurrent HCC, missing laboratory data, and insufficient follow-up.

Missingness is already apparent in Table 1, where diabetes mellitus totals 610 rather than 611 patients. All missing values should be reported by variable, and the handling of missing data should be described. Complete-case analysis should be justified or multiple imputation considered where appropriate.

10. Descriptive statistics and table reporting need revision.

Mean ± standard error is generally not appropriate for describing patient-level baseline distributions. Normally distributed variables should be reported as mean ± standard deviation, while skewed variables should be reported as median and interquartile range. Several variables, including CRP, blood loss, tumor size, and operative duration, may be skewed and require assessment.

Please verify the value “total bilirubin 0.81 ± 1.1 mg/dL” in Table 5, which appears to be a typographical or reporting error. CRP units should also be added to Table 1. The DCP distributions and corresponding P value should be rechecked due to the markedly asymmetric values.

11. The data-availability statement is incomplete.

The statement that all data are available without restriction does not indicate where the data can be accessed. The authors should provide a repository name, permanent DOI or accession link, and a de-identified minimal dataset sufficient to reproduce the tables, survival analyses, and propensity-score analysis. Statistical code should also be shared where possible.

12. Ethics and consent reporting should be clarified.

The authors should specify whether informed consent was written, oral, or obtained through an opt-out procedure. Given the retrospective inclusion of patients treated from 2004 onward, the procedure for deceased or unavailable patients should be explained. The manuscript should also clarify whether the research dataset was anonymized and whether investigators had access to identifiable information.

Minor comments

The abstract should report the principal adjusted hazard ratios and 95% confidence intervals rather than only stating statistical significance.

Replace causal wording such as “impact” and “effect” with “association” unless a causal analysis is justified.

Figure 1 should present all exclusion categories and numbers.

Figure legends should state the statistical test used and explain the censoring marks.

The manuscript is generally intelligible, but minor grammatical and terminology corrections are required throughout

Reviewer #3: The manuscript is clearly written in good English.

Comments:

1. High and Low CALLY group is classified as value ><2.2. What is the rational for considering 2.2 as demarcating value?

2.Line 170. check the spelling-"caliper"

3. Conclusion in abstract: The perioperative CALLY Index is an independent prognostic factor after hepatic resection for primary HCC. Conclusion in Full Text: The perioperative CALLY Index is independently associated with DFS and OS after primary HCC resection. Could this study alone establish CALLY Index as independent prognostic factor??

**********

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Reviewer #1: Yes: Ahmed M. Abdelsalam

Reviewer #2: Yes: M. Abass

Reviewer #3: No

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PLoS One. 2026 Sep 30;21(9):e0358776. doi: 10.1371/journal.pone.0358776.r002

Author response to Decision Letter 1

24 Aug 2026

Answers to the Reviewer #1:

We are grateful for the reviewer’s thoughtful comments and suggestions. Our responses to them are as follows. We changed the manuscript accordingly.

Q1.

Excluding 44% of the original cohort may increase the risk of bias.

Our response to Q1:

We thank the reviewer for this important comment. We agree that the exclusion of a substantial proportion of the initially screened cohort may introduce selection bias and reduce the generalizability of the findings. In the revised manuscript, we have expanded the study flow diagram to provide the detailed reasons for exclusion from the original cohort of 1,089 patients. Specifically, patients were excluded because of non-HCC liver tumors, recurrent HCC, and missing laboratory data required for calculation of the CALLY index. Furthermore, we have explicitly acknowledged this issue as a limitation in the Discussion.

The Methods, Figure 1, and Discussion sections have been revised accordingly.

Q2.

There is a marked imbalance between the two groups, which may reduce the precision of the results.

Our response to Q2:

We agree with the reviewer. In the original manuscript, substantial baseline imbalances were observed between the high and low CALLY index groups. To address this concern, we revised the propensity-score analysis by incorporating clinically relevant covariates associated with prognosis after HCC resection, including age, sex, viral etiology, liver functional reserve, tumor markers, tumor burden, and pathological factors, as appropriate (Table 5).

The above is described in the results section and in Table 5.

Q3.

What were the postoperative interventions, like TACE, RF, or chemotherapy, because all of these might affect the outcome.

Our response to Q3:

Thank you for this important comment. We agree that treatment after recurrence may influence postoperative outcomes, particularly overall survival. In the revised manuscript, we added information regarding the initial treatment administered for the first recurrence, including repeat hepatic resection, radiofrequency ablation, transarterial chemoembolization/transarterial embolization, systemic therapy, radiotherapy, best supportive care, and unknown or unavailable treatment information.

Because post-recurrence treatment was selected according to recurrence pattern, residual liver function, patient condition, treatment era, and physician judgment, the treatment strategies were heterogeneous. In addition, these treatments were post-baseline variables that occurred after surgery and after recurrence, and therefore were not included in the primary Cox regression model evaluating the prognostic association of the preoperative CALLY index. However, we agree that post-recurrence treatment may have affected overall survival. We have therefore added the treatment distribution as Supplementary table 2 and acknowledged this issue as a limitation in the Discussion.

Q4.

What was the cause of death, recurrence, liver failure, or any other cause?

Our response to Q4:

Thank you for this important comment. We agree that the cause of death is important for interpreting overall survival. We attempted to review the causes of death from the available medical records. However, because this was a retrospective study including patients treated over a long period, detailed cause-of-death information was not available for all deceased patients. Therefore, we could not perform a reliable cause-specific survival analysis. We have added this point as a limitation in the revised Discussion.

Q5.

Potential residual confounding despite propensity-score matching, as important variables such as frailty, sarcopenia, antiviral therapy, and performance status were not included.

Our response to Q5:

Thank you for this important comment. We agree that residual confounding may remain despite propensity-score matching, particularly because host-related factors such as frailty, sarcopenia, antiviral therapy, and performance status may influence long-term outcomes after hepatic resection for HCC.

In response to this comment, we reviewed the available clinical data and added performance status to the baseline variables. Performance status has now been included in Table 5 to better characterize the matched cohort. In addition, where applicable, performance status was incorporated into the revised adjustment strategy.

However, frailty, sarcopenia, and antiviral therapy were not uniformly available for all patients throughout the study period, particularly because this retrospective cohort included patients treated over a long period from 2004 to 2022. Therefore, these variables could not be reliably incorporated into the propensity-score model or multivariable analyses. We have acknowledged this issue more explicitly in the revised Discussion as a limitation.

Answers to the Reviewer #2:

We appreciate your thoughtful comments and suggestions. Our responses to the comments are as follows.

Major comments

Q1.

The manuscript should not currently be described as a validation study.

Our response to Q1:

Thank you for this important comment. We agree with the reviewer that the original wording may have inappropriately implied that the present study was an independent validation study. The cutoff value of 2.2 was derived from the present cohort using ROC analysis and was not prespecified from an independent external cohort. Therefore, this analysis represents exploratory cutoff derivation and apparent performance assessment rather than independent external validation.

In response to this comment, we have removed the term “validation study” from the title and manuscript. We have also revised the Methods section to clarify that the cutoff value of 2.2 was derived from the present dataset and should be interpreted as exploratory. To reduce reliance on dichotomization, we additionally analyzed the CALLY index as a continuous variable in Cox proportional hazards models.

The Results and Discussion sections have been revised accordingly. We have moderated the conclusions and now state that the preoperative CALLY index was independently associated with DFS and OS, rather than claiming that this study provides definitive validation of the CALLY index as a prognostic biomarker. We have also added that further external validation is required to determine the optimal cutoff value and confirm clinical utility.

Q2.

The cutoff-selection procedure is insufficiently described.

Our response to Q2:

Thank you for this important comment. We agree that the cutoff-selection procedure was insufficiently described in the original manuscript. In the revised manuscript, we have clarified that the cutoff value of 2.2 for the CALLY index was derived from receiver operating characteristic curve analysis using overall survival as the outcome. The optimal cutoff was selected according to the Youden index. We have also ensured that the corresponding ROC curve is included as Supplementary Figure 1 and is accessible to reviewers.

Because this cutoff value was derived from the present cohort and was not prespecified from an external dataset, we now describe it as an exploratory cutoff. To reduce reliance on dichotomization, we additionally analyzed the CALLY index as a continuous variable in Cox proportional hazards models. The Methods and Discussion sections have been revised accordingly, and the limitations associated with cohort-derived cutoff selection have been explicitly acknowledged.

Q3.

Standard ROC analysis is not adequate for censored survival outcomes.

Our response to Q3:

Thank you for this important methodological comment. We agree that conventional ROC analysis is not appropriate as the primary method for evaluating censored time-to-event outcomes such as DFS and OS. In the revised manuscript, we therefore performed time-dependent ROC analyses using EZR at prespecified time points of 1, 3, and 5 years after hepatic resection. Time-dependent AUCs were calculated for the CALLY index and established liver function indices, including ALBI score, EZ-ALBI score, FIB-4 index, MELD score, and Child-Pugh classification. The revised Table 4A now presents the time-dependent AUCs. We also revised the terminology throughout the manuscript by replacing “diagnostic accuracy” with “prognostic discrimination” when referring to DFS and OS.

We agree that the discrimination of the CALLY index was modest. Therefore, we have moderated the interpretation of the results and no longer state that the CALLY index has superior diagnostic accuracy. Instead, we state that the CALLY index showed modest prognostic discrimination and may provide adjunctive prognostic information. Conventional ROC analysis was retained only for early recurrence within 1 year after surgery, which was evaluated as a binary outcome.

Q4.

The discrimination of the CALLY index is modest and should not be overstated.

Our response to Q4:

Thank you for this important comment. We agree that the prognostic discrimination of the CALLY index was modest and that its clinical utility should not be overstated. As described in our response to Major Comment 3, we performed time-dependent ROC analyses for DFS and OS and revised the terminology by replacing “diagnostic accuracy” with “prognostic discrimination.” We have also removed statements suggesting that the CALLY index has superior or high predictive performance.

In the revised manuscript, we now state that the CALLY index showed modest prognostic discrimination and may provide adjunctive prognostic information rather than serving as a stand-alone prognostic tool. We have also revised the Results, Discussion, and Conclusions to avoid overinterpretation of the AUC values.

We agree that formal assessment of incremental clinical value would require comparison of a baseline clinicopathological model with and without the CALLY index, as well as evaluation of calibration and decision-curve analysis. These analyses were not performed in the present study. Therefore, we have avoided claiming that the CALLY index improves clinical decision-making and have added this point as a limitation. We now state that further studies are required to determine whether the CALLY index provides incremental value beyond established tumor- and liver-related prognostic factors.

Q5.

The Cox regression strategy requires substantial clarification and revision.

Number of DFS events, recurrences, deaths, and censored observations.

Median follow-up calculated using an appropriate method, preferably reverse Kaplan-Meier.

Proportional-hazards assumption testing for each Cox model.

The complete prespecified covariate-selection strategy.

Missing-data handling and the number of patients included in each model.

Potential collinearity must also be addressed because the CALLY index contains albumin, CRP, and lymphocyte count, while other models include albumin-related measures such as ALBI or EZ-ALBI.

Our response to Q5:

Thank you for these important methodological comments. We agree that the Cox regression strategy required further clarification. In the revised manuscript, we have added the numbers of DFS events, recurrences, deaths, and censored observations. We also added the median follow-up period and clarified the number of patients included in the analyses.

We have revised the Statistical Analysis section to describe the covariate-selection strategy for the Cox proportional hazards models. Variables were selected based on clinical relevance and statistical significance in univariate analysis, while taking into account the number of observed events to reduce the risk of overfitting. We also assessed the proportional hazards assumption using graphical and residual diagnostics available in JMP and clarified this in the revised Methods section.

We agree that dichotomization of continuous variables may lead to loss of information and unstable estimates. Therefore, in response to this comment, we additionally analyzed the CALLY index as a continuous variable. The continuous-variable analysis showed that the CALLY index remained significantly associated with postoperative outcomes, supporting the robustness of the association without relying exclusively on the cutoff value. We have added these results to the revised Results section and tables.

We also addressed potential collinearity. Because the CALLY index includes albumin, CRP, and lymphocyte count, these individual components were not included simultaneously with the CALLY index in the same multivariable model. In addition, albumin-based indices such as ALBI and EZ-ALBI were interpreted cautiously when considered together with the CALLY index. We have added this explanation to the Methods and Discussion sections.

Finally, we clarified the handling of missing data. Complete-case analysis was performed for variables included in each model, and the number of patients included in each analysis is now reported. We have also acknowledged residual confounding, possible overfitting, and information loss due to dichotomization as limitations.

Q6.

The propensity-score matching analysis is inadequately reported.

Our response to Q6:

Thank you for this important comment. We agree that the propensity-score matching analysis was inadequately described in the original manuscript and that balance should not be assessed using nonsignificant P values alone.

In the revised manuscript, we have substantially expanded the description of the propensity-score matching procedure. The propensity score was estimated using a logistic regression model with the CALLY index group as the dependent variable. One-to-one nearest-neighbor matching was performed without replacement using a caliper width of 0.2 of the standard deviation of the logit of the propensity score. Patients outside the region of common support were not matched, and complete-case analysis was performed for variables included in the propensity-score model.

We also clarified that covariate balance was assessed using standardized mean differences, and an absolute standardized mean difference <0.1 was considered to indicate adequate balance.

Regarding survival analysis in the matched cohort, we revised the text to clarify that survival outcomes after matching were evaluated using Kaplan–Meier curves and log-rank tests. We did not estimate matched hazard ratios because the propensity-score matched analysis was performed as a sensitivity analysis to examine whether the survival separation between the high and low CALLY index groups persisted after reducing measured baseline imbalance, rather than as a primary model for effect estimation.

Finally, we clarified that the CALLY index is a prognostic biomarker rather than a treatment exposure; therefore, the propensity-score matched analysis was interpreted as a sensitivity analysis of prognostic association, not as a causal effect analysis. We have avoided causal terms such as “effect” and “impact” throughout the revised manuscript.

Q7.

Important clinical confounders and temporal changes need consideration.

Our response to Q7:

Thank you for this important comment. We agree that temporal changes during the long study period may have influenced postoperative outcomes. The present cohort included patients who underwent hepatic resection between 2004 and 2022, during which surgical techniques, perioperative management, imaging modalities, surveillance strategies, antiviral treatment, and treatment options for recurrent HCC changed substantially.

In response to this comment, we performed an additional sensitivity analysis according to treatment era. Patients were divided into two cohorts based on the year of surgery: 2004–2013 and 2014–2022. Within each treatment era, OS and DFS were compared between the high and low CALLY index groups using Kaplan–Meier curves and log-rank tests. In both treatment eras, the low CALLY index group showed significantly worse OS and DFS than the high CALLY index group. These results have been added as Supplementary Figure 2.

We also reviewed the availability of clinically relevant variables, including performance status, resection-related factors, and post-recurrence treatment, and added available information to the revised manuscript where possible. However, some variables, such as detailed antiviral therapy, frailty, sarcopenia, portal hypertension, and detailed post-recurrence treatment, were not uniformly available throughout the entire study period. Therefore, we have acknowledged residual confounding related to temporal changes and unavailable clinical variables as a limitation in the revised Discussion.

Q8.

Laboratory measurement and calculation of the CALLY index require clarification.

The authors should report:

The permitted interval between blood collection and surgery.

Whether patients with active infection or acute inflammatory disorders were excluded.

The CRP assay and lower detection limit.

How CRP values of zero or below the detection limit were handled, because CRP appears in the denominator.

The units used for CRP and lymphocyte count.

The distribution of lymphocyte count and the CALLY index in the total cohort and both groups.

The definitions “high >2.2” and “low <2.2” leave patients with a value exactly equal to 2.2 unclassified. One group should use an inclusive boundary.

Our response to Q8:

Thank you for this important comment. We agree that the laboratory measurement conditions and calculation of the CALLY index required clarification, particularly because CRP is included in the denominator of the formula.

In the revised manuscript, we have added detailed information regarding laboratory measurements. Preoperative laboratory data were obtained within 7 days before hepatic resection. Serum albumin was expressed in g/dL, lymphocyte count in cells/μL, and CRP in mg/dL. The lower reporting limit for CRP at our institution was 0.01 mg/dL, and no patient had a CRP value of zero. For patients with CRP values reported as zero or below the lower detection limit, no such patients were present. This handling has now been explicitly described in the Methods section.

We also reviewed the medical records for clinically apparent active infection or acute inflammatory disease at the time of preoperative blood sampling. Patients with such conditions were excluded. This point has been added to the Methods sections.

Finally, we corrected the group definition to avoid ambiguity at the cutoff value. Patients were classified into the high CALLY index group (≥2.2) and the low CALLY index group (<2.2).

Q9.

Patient selection and missing data should be reported transparently.

Our response to Q9:

Thank you for this important comment. We agree that the patient selection process and handling of missing data were insufficiently described in the original manuscript.

In the revised manuscript, we have revised Figure 1 to provide a detailed flow diagram of patient selection. Of the 1,089 patients who underwent hepatic resection for liver tumors during the study period, 478 were excluded: 68 had non-HCC liver tumors, 381 had recurrent HCC, and 29 had missing laboratory data required for calculation of the CALLY index. Finally, 611 patients who underwent initial hepatic resection for primary HCC were included in the analysis.

We also reviewed missing data for the variables used in the analyses and added Supplementary Table 1, which summarizes the number of available and missing values for each variable. Diabetes mellitus status was missing in one patient, which explains why the total number for this variable was 610 rather than 611 in Table 1. In contrast, data for albumin, CRP, lymphocyte count, AFP, DCP, tumor size, vascular invasion, and survival outcomes were available for all 611 patients.

We have clarified in the Statistical Analysis section that complete-case analysis was performed for variables with missing data. Because missing data were minimal, multiple imputation was not performed. We have also added this point to the revised manuscript and acknowledged the possibility of selection bias related to patient exclusion as a limitation.

Q10.

Descriptive statistics and table reporting need revision.

Our response to Q10:

Thank you for this important comment. We agree that mean ± standard error is not appropriate for describing patient-level baseline distributions and may imply excessive precision. In response to the reviewer’s comment, we revised Tables 1 and 5 and now present all continuous variables as median [interquartile range]. Categorical variables are presented as numbers and percentages.

Accordingly, between-group comparisons for continuous variables were performed using the Mann–Whitney U test. The Statistical Analysis section and the footnotes of Tables 1 and 5 have been revised to clarify the presentation of variables and the statistical tests used.

We also carefully rechecked the values in the tables. The value of total bilirubin in Table 5 was confirmed to be a typographical/reporting error and has been corrected. The unit of CRP has been added to Table 1 as mg/dL. In addition, because DCP showed a skewed distribution, we rechecked the DCP distributions and the corresponding P value using the Mann–Whitney U test.

Q11.

The data-availability statement is incomplete.

Our response to Q11:

Thank you for this important comment. We agree that the original data-availability statement was incomplete because it did not specify where the data could be accessed.

In the revised submission, we have added a de-identified minimal dataset as a Supporting Information file. This dataset includes the variables necessary to reproduce the baseline tables, survival analyses, time-dependent ROC analyses, and propensity-score matching analysis. We have also revised the Data Availability Statement to clearly indicate that the relevant data underlying the findings are available in the Supporting Information files. Statistical analyses were performed using JMP Pro and EZR as described in the Methods section.

The revised Data Availability Statement now reads as follows:

“All relevant data underlying the findings of this study are provided in the Supporting Information files. The de-identified minimal dataset used to reproduce the baseline tables, survival analyses, time-dependent ROC analyses, and propensity-score matching analysis is provided as S1 Dataset.”

Q12.

Ethics and consent reporting should be clarified.

Our response to Q12:

Thank you for this important comment. We agree that the ethics and consent procedures required clarification.

In the revised manuscript, we have clarified that this retrospective observational study was approved by the Ethics and Indications Committee of Hiroshima Red Cross Hospital and Atomic-bomb Survivors Hospital (approval No. 2021-029). Because of the retrospective nature of the study, the requirement for written informed consent was waived by the ethics committee, and an opt-out approach was used. Information regarding the study was disclosed, and patients or their representatives were given the opportunity to decline participation. This opt-out procedure also applied to patients who had died or could not be contacted.

We have also clarified the handling of patient data. Authorized investigators accessed clinical records only for data collection. After data extraction, direct personal identifiers were removed, and statistical analyses were performed using a de-identified dataset. This information has been added to the revised Ethics Statement.

Minor comments

Q1.

The abstract should report the principal adjusted hazard ratios and 95% confidence intervals rather than only stating statistical significance.

Our response to Q1:

Thank you for this suggestion. We have revised the Abstract to include the principal adjusted hazard ratios and 95% confidence intervals for DFS and OS. Specifically, we now report that a low CALLY index was independently associated with worse DFS and OS, with the corresponding HRs, 95% CIs, and P values.

Q2.

Replace causal wording such as “impact” and “effect” with “association” unless a causal analysis is justified.

Our response to Q2:

Thank you for this comment. We agree that causal wording is not appropriate for this retrospective observational study. We have revised the manuscript throughout by replacing causal terms such as “impact” and “effect” with non-causal terms such as “association,” “prognostic association,” and “adjunctive prognostic information.”

Q3.

Figure 1 should present all exclusion categories and numbers.

Our response to Q3:

Thank you for this comment. We have revised Figure 1 to show all exclusion categories and the corresponding numbers. Of the 1,089 initially screened patients, 68 were excluded because of non-HCC liver tumors, 381 because of recurrent HCC, and 29 because of missing laboratory data required for calculation of the CALLY index. The final study cohort consisted of 611 patients with primary HCC.

Q4.

Figure legends should state the statistical test used and explain the censoring marks.

Our response to Q4:

Thank you for this helpful suggestion. We have revised the figure legends to state that P values for Kaplan–Meier survival curves were calculated using the log-rank test. We have also added an explanation that tick marks indicate censored observations.

Q5.

The manuscript is generally intelligible, but minor grammatical and terminology corrections are required throughout.

Our response to Q5:

Thank you for this comment. We have carefully reviewed the manuscript and corrected grammatical, stylistic, and terminology issues throughout. We also standardized key terms, including “preoperative CALLY index,” “propensity-score matching,” “caliper,” and “prognostic discrimination.”

Answers to the Reviewer #3:

We are grateful for the reviewer’s thoughtful comments and suggestions. Our responses to them are as follows. We changed the manuscript accordingly.

Q1.

High and Low CALLY group is classified as value ><2.2. What is the rational for considering 2.2 as demarcating value?

Our response to Q1:

Thank you for this important comment. We agree that the rationale for using 2.2 as the cutoff value for the CALLY index was insufficiently described in the original manuscript. To our knowledge, no universally accepted cutoff value for the CALLY index has been established across cancer types or even among patients with HCC. Previous studies have used different cutoff values depending on the cancer type, treatment setting, endpoint, and statistical method. Therefore, the cutoff value of 2.2 in the present study should not be interpreted as an externally validated universal threshold.

In the original analysis, the cutoff value of 2.2 was derived from the present cohort using ROC analysis for survival-group stratification. We have now clarified this point in the Methods section. In response to the reviewer’s comment, we also performed additional analyses treating the CALLY index as a continuous variable to avoid relying exclusively on dichotomization. Furthermore, we have revised the wording in the Results and Discussion to state that the cutoff-based analysis was exploratory and that the optimal cutoff value requires further validation in independent cohorts.

Q2.

Line 170. check the spelling-"caliper"

Our response to Q2:

Thank you for pointing that out. We have corrected the spelling error.

Q3.

Conclusion in abstract: The perioperative CALLY Index is an independent prognostic factor after hepatic resection for primary HCC. Conclusion in Full Text: The perioperative CALLY Index is independently associated with DFS and OS after primary HCC resection. Could this study alone establish CALLY Index as independent prognostic factor??

Our response to Q3:

Thank you for this important comment. We agree that the original wording of the conclusion may have overstated the findings of this retrospective single-center study. The CALLY index was calculated using preoperative laboratory data; therefore, we have revised “perioperative CALLY index” to “preoperative CALLY index” throughout the manuscript for consistency.

We also agree that this study alone cannot definitively establish the CALLY index as a validated prognostic factor. Therefore, we have revised the Abstract Conclusion and the full-text Conclusion to state that the preoperative CALLY index was “independently associated with DFS and OS” rather than definitively describing it as an “independent prognostic factor.”

Attachment

Submitted filename: Response to Reviewers.docx

pone.0358776.s006.docx^{(38.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0358776.r003

Decision Letter 1

Takehiko Hanaki

7 Sep 2026

Association between the preoperative C-reactive protein-albumin-lymphocyte index and prognosis after hepatic resection for hepatocellular carcinoma

PONE-D-26-31992R1

Dear Dr. Shimagaki,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Academic Editor

PLOS One

Additional Editor Comments (optional):

I appreciate the authors’ careful revisions and the substantial effort made to improve the manuscript.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #2: Yes

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Reviewer #1: (No Response)

Reviewer #2: The manuscript has been significantly improved and the remaining limitations are inherent to the study design rather than correctable methodological issues. I believe the revised manuscript is suitable for publication in PLOS ONE.

**********

what does this mean?). If published, this will include your full peer review and any attached files.

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Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy

Reviewer #1: Yes: Ahmed M. Abdelsalam

Reviewer #2: Yes: Marwa Abass

**********

PLoS One. doi: 10.1371/journal.pone.0358776.r004

Acceptance letter

Takehiko Hanaki

PONE-D-26-31992R1

PLOS One

Dear Dr. Shimagaki,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Receiver operating characteristic curve of the CALLY index for survival-group stratification after HCC resection.

HCC: hepatocellular carcinoma.

(PDF)

pone.0358776.s001.pdf^{(86.8KB, pdf)}

S2 Fig. Kaplan–Meier survival curves according to the CALLY index stratified by treatment era.

(PDF)

pone.0358776.s002.pdf^{(178.2KB, pdf)}

S1 Table. Missing data by variable.

(DOCX)

pone.0358776.s003.docx^{(38.4KB, docx)}

S2 Table. The distribution of initial post-recurrence treatment.

(DOCX)

pone.0358776.s004.docx^{(38.1KB, docx)}

S1 File. Dataset.

(XLSX)

pone.0358776.s005.xlsx^{(620KB, xlsx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0358776.s006.docx^{(38.7KB, docx)}

Data Availability Statement

[pone.0358776.ref001] 1.Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63. [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref002] 2.Erridge S, Pucher PH, Markar SR, Malietzis G, Athanasiou T, Darzi A, et al. Meta-analysis of determinants of survival following treatment of recurrent hepatocellular carcinoma. Br J Surg. 2017;104(11):1433–42. doi: 10.1002/bjs.10597 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref003] 3.Qin S, Chen M, Cheng A-L, Kaseb AO, Kudo M, Lee HC, et al. Atezolizumab plus bevacizumab versus active surveillance in patients with resected or ablated high-risk hepatocellular carcinoma (IMbrave050): A randomised, open-label, multicentre, phase 3 trial. Lancet. 2023;402(10415):1835–47. doi: 10.1016/S0140-6736(23)01796-8 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref004] 4.Reig M, Sanduzzi-Zamparelli M, Forner A, Rimola J, Ferrer-Fàbrega J, Burrel M, et al. BCLC strategy for prognosis prediction and treatment recommendations: The 2026 update. J Hepatol. 2026;84(3):631–54. doi: 10.1016/j.jhep.2025.10.020 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref005] 5.Iseda N, Itoh S, Toshima T, Yoshiya S, Bekki Y, Tsutsui Y, et al. Outcome of hepatectomy after systemic therapy for hepatocellular carcinoma: A Japanese multicenter study. Surg Today. 2025;55(4):510–7. doi: 10.1007/s00595-024-02930-x [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref006] 6.Singal AG, Llovet JM, Yarchoan M, Mehta N, Heimbach JK, Dawson LA, et al. AASLD Practice Guidance on prevention, diagnosis, and treatment of hepatocellular carcinoma. Hepatology. 2023;78(6):1922–65. doi: 10.1097/HEP.0000000000000466 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref007] 7.Rimassa L, Chan SL, Sangro B, Lau G, Kudo M, Reig M, et al. Five-year overall survival update from the HIMALAYA study of tremelimumab plus durvalumab in unresectable HCC. J Hepatol. 2025;83(4):899–908. doi: 10.1016/j.jhep.2025.03.033 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref008] 8.Johnson PJ, Berhane S, Kagebayashi C, Satomura S, Teng M, Reeves HL, et al. Assessment of liver function in patients with hepatocellular carcinoma: A new evidence-based approach-the ALBI grade. J Clin Oncol. 2015;33(6):550–8. doi: 10.1200/JCO.2014.57.9151 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref009] 9.Panettieri E, Campisi A, De Rose AM, Mele C, Giuliante F, Vauthey J-N, et al. Emerging prognostic markers in patients undergoing liver resection for hepatocellular carcinoma: A narrative review. Cancers (Basel). 2024;16(12):2183. doi: 10.3390/cancers16122183 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref010] 10.Iida H, Tani M, Komeda K, Nomi T, Matsushima H, Tanaka S, et al. Superiority of CRP-albumin-lymphocyte index (CALLY index) as a non-invasive prognostic biomarker after hepatectomy for hepatocellular carcinoma. HPB (Oxford). 2022;24(1):101–15. doi: 10.1016/j.hpb.2021.06.414 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref011] 11.Tsunematsu M, Haruki K, Taniai T, Tanji Y, Shirai Y, Furukawa K, et al. The impact of C-reactive protein-albumin-lymphocyte (CALLY) index on the prognosis of patients with distal cholangiocarcinoma following pancreaticoduodenectomy. Ann Gastroenterol Surg. 2022;7(3):503–11. doi: 10.1002/ags3.12637 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref012] 12.She S, Shi J, Zhu J, Yang F, Yu J, Dai K. Impact of inflammation and the immune system on hepatocellular carcinoma recurrence after hepatectomy. Cancer Med. 2024;13(4):e7018. doi: 10.1002/cam4.7018 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref013] 13.Wu B, Liu J, Shao C, Yu D, Liao J. Integrating inflammation, nutrition, and immunity: the CALLY index as a prognostic tool in digestive system cancers - a systematic review and meta-analysis. BMC Cancer. 2025;25(1):672. doi: 10.1186/s12885-025-14074-3 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref014] 14.Matsui S, Kato Y, Ohgi K, Ashida R, Yamada M, Otsuka S, et al. Prognostic impact of the CALLY index in patients with resectable pancreatic cancer. Surgical Oncology Insight. 2025;2(1):100119. doi: 10.1016/j.soi.2024.100119 [DOI] [Google Scholar]

[pone.0358776.ref015] 15.Shimagaki T, Sugimachi K, Tomino T, Onishi E, Toshima T, Itoh S, et al. The EZ-ALBI score as a prognostic tool for outcomes after resection of small hepatocellular carcinoma (</=3 cm ). Hepatol Res. 2025;55(8):1159–71. [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref016] 16.Zhao H, Yin B, Li X-R, Liu X-Y, Bu Z-T, Shi H-P. The CRP-albumin-lymphocyte index provides enhanced prognostic value in liver cancer compared to the TNM staging system. Sci Rep. 2025;15(1):20090. doi: 10.1038/s41598-025-03985-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref017] 17.Yamamoto M, Kobayashi T, Kuroda S, Hamaoka M, Okimoto S, Honmyo N, et al. Verification of inflammation-based prognostic marker as a prognostic indicator in hepatocellular carcinoma. Ann Gastroenterol Surg. 2019;3(6):667–75. doi: 10.1002/ags3.12286 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref018] 18.Shimagaki T, Sugimachi K, Mano Y, Onishi E, Iguchi T, Nakashima Y, et al. Cachexia index as a prognostic predictor after resection of pancreatic ductal adenocarcinoma. Ann Gastroenterol Surg. 2023;7(6):977–86. doi: 10.1002/ags3.12686 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref019] 19.Hiraoka A, Kumada T, Michitaka K, Toyoda H, Tada T, Ueki H, et al. Usefulness of albumin-bilirubin grade for evaluation of prognosis of 2584 Japanese patients with hepatocellular carcinoma. J Gastroenterol Hepatol. 2016;31(5):1031–6. doi: 10.1111/jgh.13250 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref020] 20.Akaoka M, Haruki K, Yamahata Y, Okazaki K, Furukawa K, Tsunematsu M, et al. The prognostic impact of perioperative dynamic changes in cachexia index in patients with hepatocellular carcinoma. Ann Gastroenterol Surg. 2024;8(5):917–26. doi: 10.1002/ags3.12804 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref021] 21.Matsumoto T, Kitano Y, Imai K, Kinoshita S, Sato H, Shiraishi Y, et al. Clinical significance of preoperative inflammation-based score for the prognosis of patients with hepatocellular carcinoma who underwent hepatectomy. Surg Today. 2022;52(7):1008–15. doi: 10.1007/s00595-021-02427-x [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref022] 22.Seyhan D, Allaire M, Fu Y, Conti F, Wang XW, Gao B, et al. Immune microenvironment in hepatocellular carcinoma: from pathogenesis to immunotherapy. Cell Mol Immunol. 2025;22(10):1132–58. doi: 10.1038/s41423-025-01308-4 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0358776.ref023] 23.Tampaki M, Papatheodoridis GV, Cholongitas E. Intrahepatic recurrence of hepatocellular carcinoma after resection: an update. Clin J Gastroenterol. 2021;14(3):699–713. doi: 10.1007/s12328-021-01394-7 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref024] 24.Yang S-L, Luo Y-Y, Chen M, Zhou Y-P, Lu F-R, Deng D-F, et al. A systematic review and meta-analysis comparing the prognosis of multicentric occurrence and vs. intrahepatic metastasis in patients with recurrent hepatocellular carcinoma after hepatectomy. HPB (Oxford). 2017;19(10):835–42. doi: 10.1016/j.hpb.2017.06.002 [DOI] [PubMed] [Google Scholar]

[pone.0358776.ref025] 25.Shiraishi T, Nonaka T, Tominaga T, Takamura Y, Oishi K, Hashimoto S, et al. The C-reactive protein-albumin-lymphocyte (CALLY) index is a useful predictor of postoperative complications in patients with a colonic stent for obstructive colorectal cancer: A Japanese multicenter study. Surg Today. 2025;55(4):502–9. doi: 10.1007/s00595-024-02924-9 [DOI] [PubMed] [Google Scholar]

PERMALINK

Association between the preoperative C-reactive protein-albumin-lymphocyte index and prognosis after hepatic resection for hepatocellular carcinoma

Tomonari Shimagaki

Atsushi Maeda

Tomoya Harima

Akihiro Sakai

Rumi Matono

Naotaka Hashimoto

Shohei Yamaguchi

Kozo Konishi

Kenkichi Hashimoto

Takashi Maeda

Roles

Abstract

Background

Methods

Results

Conclusions

Introduction

Materials and methods

Study design

Fig 1. Study flow diagram.

Preoperative evaluation and treatment

Definition of the CALLY index

Laboratory measurements and calculation of the CALLY index

Outcomes and follow-up

Sensitivity analysis according to treatment era

Treatment after recurrence

Clinicopathological variables

Propensity score matching analysis

Statistical analysis

Results

Patient characteristics

Table 1. Comparative analysis of clinicopathological parameters between the high and low CALLY index groups.

Treatment after recurrence

Table 2. Univariate and multivariate Cox regression analyses of the clinicopathological variables related to disease-free survival after hepatic resection for hepatocellular carcinoma.

Comparison of clinicopathological characteristics according to the CALLY index

Poor DFS and OS in the low CALLY index group

Fig 2. Kaplan–Meier survival curves according to the preoperative CALLY index.

Sensitivity analysis by treatment era

Prognostic factors for DFS

Prognostic factors for OS

Table 3. Univariate and multivariate Cox regression analyses of the clinicopathological variables related to overall survival after hepatic resection for hepatocellular carcinoma.

Prognostic discrimination of the CALLY index

Table 4. Time-dependent area under the curve (AUC) for disease-free survival (DFS) and overall survival (OS).

Table 5. Diagnostic accuracy of liver function assessment for predicting early recurrence (within 1 year after surgery) of resected hepatocellular carcinoma (n = 611).

Propensity-score matched analysis

Table 6. Patient characteristics in the high and low CALLY index groups after propensity-score matching.

Fig 3. Kaplan–Meier survival curves according to the preoperative CALLY index after propensity-score matching.

Discussion

Supporting information

Acknowledgments

Abbreviations

Data Availability

Funding Statement

References

Decision Letter 0

Takehiko Hanaki

Roles

Author response to Decision Letter 1

Decision Letter 1

Takehiko Hanaki

Roles

Acceptance letter

Takehiko Hanaki

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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