Prognostic significance of psoas muscle index at diagnosis in cervical cancer progression

Abstract

Objective

To assess whether the psoas muscle index (PMI) measured using computed tomography (CT) at diagnosis can predict cancer progression and overall survival (OS) in patients with cervical cancer.

Methods

We retrospectively reviewed the medical records of patients diagnosed with cervical cancer at Korea University Anam Hospital from January 2019 to December 2023. Patients with incomplete data or those who were lost to follow-up were excluded. PMI was measured on axial CT images at the third lumbar vertebral (L3) level at diagnosis, defined as the sum of bilateral psoas diameters normalized to height squared (m²), and a cutoff value of 35 mm/m² was used to classify patients into low and high PMI groups. Survival outcomes were assessed using Kaplan–Meier analysis and the log-rank test. Cox proportional hazards models were used to identify prognostic factors.

Results

Of the 178 patients, 92 were in the low PMI group and 86 in the high PMI group. Higher PMI was associated with significantly prolonged disease-free survival (DFS) (p<0.001), but not with OS (p=0.395). In multivariate Cox regression analysis, higher PMI independently predicted a lower likelihood of disease progression (adjusted hazard ratio, 0.30; p<0.001). However, PMI was not associated with OS.

Conclusion

This study demonstrates that PMI measured at diagnosis may serve as a useful independent prognostic factor for cancer progression in patients with cervical cancer, particularly for predicting DFS.

Synopsis

Low psoas muscle index (PMI) at diagnosis was associated with increased progression risk. Patients with low PMI had shorter disease-free survival than those with high PMI. PMI remained an independent prognostic factor. PMI may help identify cervical cancer patients at increased risk of disease progression.

INTRODUCTION

Globally, cervical cancer ranks as the fourth most frequently diagnosed malignancy among women. The World Health Organization reported that about 660,000 new cases of cervical cancer were identified worldwide in 2022, with an estimated 350,000 deaths from cervical cancer [1]. In recent years, widespread implementation of human papillomavirus (HPV) vaccination and increased participation in cervical cancer screening programs have contributed to a significant decline in the overall incidence of cervical cancer. Specifically, incidence rates have dropped by more than half from the mid-1970s to the mid-2000s. However, among women of reproductive age (30–44 years), the incidence of cervical cancer increased by approximately 1.7% between 2012 and 2019 [2]. Furthermore, the American Cancer Society projects that approximately 13,360 new cases of cervical cancer will be diagnosed in the United States in 2025, with an estimated 4,320 deaths resulting from the disease [2]. These figures are not negligible. The reported recurrence rates range from 11% to 22% in patients with International Federation of Gynaecology and Obstetrics (FIGO) stages IB to IIA, and from 28% to 64% in those with stages IIB to IVA. In particular, patients with advanced-stage disease (stage III or higher) exhibit recurrence rates exceeding 70% [3]. Therefore, there is a clear need to identify high-risk patients using reliable prognostic factors to develop more tailored and effective treatment strategies.

In patients with cancer, body composition and function can affect survival outcomes; therefore, muscle quantity and quality are important prognostic factors [4]. The skeletal muscle plays a key role in insulin-mediated glucose metabolism and also functions as an endocrine organ, contributing to the regulation of balanced mitochondrial metabolism [5]. Numerous studies have investigated the association between skeletal muscle mass and survival outcomes in patients with cancer. Various methods have been used to assess skeletal muscle quantity, among which the measurement of muscle content at the third lumbar vertebra (L3) level is widely regarded as a reliable cross-sectional indicator of total body muscle mass [6,7]. However, calculating the skeletal muscle index (SMI), which involves summing the cross-sectional areas of all muscles at the L3 level, requires specialized software and expert interpretation. Consequently, its use in routine clinical decision-making remains limited because of these practical constraints [7]. To overcome these practical limitations, increasing attention has been directed toward the psoas muscle. In addition to its role as the primary hip flexor, the psoas muscle contributes to stabilization of the lumbar spine, sacroiliac joint, and hip joint. Although it accounts for approximately 10% of the total skeletal muscle area at the L3 vertebral level, it plays a key role in posture maintenance and core strength, making it a valuable marker for assessing the degree of sarcopenia [8]. The psoas muscle index (PMI), representing skeletal muscle mass, was calculated as the sum of the longest transverse and vertical diameters of both psoas muscles at the L3 vertebral level divided by height squared (m²) [9]. Several studies have shown that the PMI is comparable to the SMI in the diagnosis of sarcopenia, supporting the use of the psoas muscle as a reliable surrogate marker [7,10].

Sarcopenia, first described by Rosenberg in 1989, is a condition characterized by the loss of skeletal muscle mass and function [11]. To date, numerous studies have evaluated the association between muscle mass, often assessed by psoas muscle quantity, and survival outcomes such as disease-free survival (DFS) or overall survival (OS) in patients with cancer. While some studies have reported that a lower psoas muscle mass is associated with poor survival outcomes [11,12,13,14,15,16,17], others have found no significant relationship between psoas muscle mass and survival outcomes in patients with cancer [16,17]. Therefore, the association between the psoas muscle mass and survival outcomes in patients with cancer remains inconclusive. Most studies have focused on patients with hepatocellular carcinoma, renal cell carcinoma, breast cancer, or gastric cancer, with limited research on cervical cancer. Therefore, this study aimed to evaluate the prognostic value of the PMI measured at the time of diagnosis by analyzing its association with survival outcomes such as DFS and OS in patients with cervical cancer.

MATERIALS AND METHODS

1. Study population and data collection

Clinical data from 178 patients diagnosed with cervical cancer at the Korea University Anam Hospital between January 2019 and December 2023 were retrospectively analyzed. Cervical cancer, including squamous cell carcinoma and adenocarcinoma, was diagnosed based on cytological assessments, such as Pap smear, and histological evaluations, including colposcopy and punch biopsy. Of the 225 patients initially identified, those with incomplete clinical data, lost to postoperative follow-up, or with concurrent malignancies were excluded, resulting in a final study cohort of 178 patients. The participant selection process is illustrated in Fig. 1.

Fig. 1. Flowchart of study participant selection.

PMI, psoas muscle index.

Patients initially diagnosed with cervical cancer were hospitalized and underwent a comprehensive series of evaluations, including abdominal computed tomography (CT), chest CT, pelvic magnetic resonance imaging (MRI), gastrofiberscopy, total colonofiberscopy, mammography, and positron emission tomography-computed tomography. Routine blood tests included a complete blood count with differential, serum chemistry analysis, and evaluation of tumor markers, including squamous cell carcinoma antigen (SCC-Ag), carbohydrate antigen 19-9 (CA 19-9), and carcinoembryonic antigen (CEA).

2. Measurement of the PMI

To calculate the PMI, we used axial images at the mid-level of the L3, obtained from abdominal CT scans performed at the time of cervical cancer diagnosis. At the mid-L3 vertebral level, the maximal transverse and perpendicular diameters of both psoas muscles were measured, and the sum was divided by height squared (m²) to obtain the PMI, following previously described methods [9]. A representative image of the PMI measurement is shown in Fig. 2.

Fig. 2. Method for calculating the PMI. A and B indicate the maximal transverse and perpendicular diameters of the right psoas muscle, whereas C and D represent those of the left psoas muscle. The PMI is calculated as the sum of these 4 diameters divided by the square of the patient’s height (m²).

CT, computed tomography; PMI, psoas muscle index.

Although a standardized cutoff value for PMI in the diagnosis of sarcopenia has not been firmly established, most previous studies have defined sarcopenia using a threshold of 35 mm/m² based on the PMI [7,18]. To evaluate whether this threshold was appropriate for our cohort, we performed a receiver operating characteristic (ROC) curve analysis. The ROC analysis showed that using either 35 mm/m² or 37 mm/m² as the cutoff resulted in the same area under the curve of 0.669. For the 35 mm/m² threshold, the sensitivity and specificity were 0.70 and 0.57, respectively, demonstrating a more balanced performance. In contrast, the 37 mm/m² threshold increased sensitivity to 0.88 but reduced specificity to 0.46, indicating that the overall discriminative ability did not improve. Although the Youden index suggested 37 mm/m² as the optimal cutoff, this trade-off between sensitivity and specificity made it less clinically practical. Furthermore, several previous studies using PMI as a prognostic factor have also adopted 35 mm/m² as their cutoff. Given the single-center design and cohort size, we did not recalibrate the PMI cutoff based solely on our data to avoid overfitting and to maintain external consistency with prior studies. Retaining the established threshold improves clinical interpretability and enables direct comparison with prior studies using the same cutoff. Thus, we determined that 35 mm/m² was the most appropriate cutoff for our cohort and categorized patients with PMI <35 mm/m² as the low PMI group and those with PMI ≥35 mm/m² as the high PMI group.

3. FIGO staging and treatment strategy

Treatment strategies for cervical cancer vary depending on the disease stage and desire to preserve fertility. The 2018 FIGO staging system was used to classify disease stage for all participants [19]. After determining the FIGO stage of cervical cancer in each participant, those with stage I disease (including IA and IB) were classified into the early-stage group, whereas those with stage II or higher (including IIA and IIB) were categorized into the advanced-stage group. The treatment options for each FIGO stage are summarized below. Radical trachelectomy may be considered in patients with stage IA2 or IB1 disease who wish to preserve their fertility. For patients with stage IA2 disease who do not wish to preserve fertility, a modified radical hysterectomy is recommended. Radical hysterectomy remains the standard treatment for stage IB1 and more advanced cases. For patients with stage IB3 or IIA2 disease and those with parametrial invasion consistent with stage IIB or higher, concurrent chemoradiotherapy (CCRT) is the preferred approach. In cases of stage IIIC2 or more advanced disease involving para-aortic lymph node (LN) metastasis, systemic chemotherapy may also be considered [19].

4. Treatment and follow-up assessment

Patients who did not require adjuvant chemotherapy or radiotherapy after primary surgery were monitored every 3 months during the first postoperative year. Follow-up evaluations included abdominal and chest CT scans, as well as tumor marker assessments for SCC-Ag, CA 19-9, and CEA. During the subsequent 2 years, follow-ups were conducted every 6 months, and annually thereafter using the same modalities.

Patients eligible for CCRT received external beam radiotherapy approximately 5 times per week in combination with weekly platinum-based chemotherapy (cisplatin 40 mg/m²). Treatment response was evaluated using pelvic MRI after the 23rd–28th radiotherapy sessions. Chemotherapy regimen typically included a combination of paclitaxel and cisplatin. More recently, a combination of paclitaxel with either carboplatin or cisplatin, and bevacizumab has been used in patients with advanced or recurrent cervical cancer who are deemed unsuitable for surgery or radiation therapy. Additionally, immune checkpoint inhibitors such as pembrolizumab and nivolumab have emerged as viable treatment options. Chemotherapy was administered every 3 weeks. After 3 cycles, the treatment response was assessed using abdominal CT, chest CT, and tumor marker evaluation. If disease progression was detected, alternative chemotherapeutic regimens were considered based on the patient’s condition.

For the survival analysis, DFS was defined as the time from the initial diagnosis of cervical cancer to radiologically confirmed recurrence. OS was defined as the time from diagnosis to the last follow-up or death.

5. Statistical analysis

Group comparisons were performed using the Student’s t test for continuous variables and the chi-square or Fisher’s exact test for categorical variables, as appropriate. DFS and OS were estimated using the Kaplan–Meier method, and differences between groups were assessed using the log-rank test. Univariate and multivariate analyses were conducted using the Cox proportional hazards regression model to identify risk factors associated with cervical cancer recurrence and OS. All statistical analyses were performed using SPSS Statistics for Windows (version 25.0; SPSS Inc., Chicago, IL, USA) with a 2-sided p-value of <0.05 considered statistically significant.

6. Ethics

This study was approved by the Institutional Review Board (IRB) of Korea University Anam Hospital (IRB number: 2025AN0217). All procedures were performed in accordance with relevant institutional guidelines and regulations. The requirement for informed consent was waived by IRB due to the retrospective nature of the study.

RESULTS

The mean PMI of all 178 participants was 35.74±6.87 mm/m², and based on the PMI cut-off of 35 mm/m², 92 participants were categorized into the low PMI group and 86 into the high PMI group. The main baseline characteristics of the participants are summarized in Table 1, while additional demographic and clinical variables are provided in Table S1. The ages of the participants ranged from 22 to 93 years. The mean age was significantly higher in the low PMI group (58.10±14.45 years) compared with the high PMI group (52.81±14.94 years). The mean weight and body mass index (BMI) were significantly lower in the low PMI group than in the high PMI group. The proportion of patients with tumor size >4 cm, the proportion of patients receiving CCRT, the proportion of patients receiving adjuvant chemotherapy, and the proportion of abnormally elevated CA 19-9 and CEA among the tumor markers was higher in the low PMI group than in the high PMI group. Other factors such as weight, parity, menopausal status, comorbidities, histology, FIGO stage, LN metastasis status, HPV infection status, number of chemotherapies, radical hysterectomy status, trachelectomy status, and SCC-Ag level were not significantly different between the 2 groups.

Table 1. Characteristics of the study participants according to PMI status of cervical cancer.

Variables		Low-PMI group (n=92)	High-PMI group (n=86)	p-value
Age (yr)		58.10±14.45	52.81±14.94	0.018
BMI (kg/m2)		22.96±3.60	25.17±4.85	0.001
FIGO stage				0.199
	Early-stage group	32 (34.8)	38 (44.2)
	Advanced-stage group	60 (65.2)	48 (55.8)
Tumor size				0.046
	≤4 cm	43 (46.7)	53 (61.6)
	>4 cm	49 (53.3)	33 (38.4)
LN metastasis status				0.075
	Negative	49 (53.3)	60 (69.8)
	PLND metastasis	25 (27.2)	16 (18.6)
	PLND and PALND metastases	18 (19.5)	10 (11.6)
HPV infection status				0.265
	No	40 (44.0)	45 (52.3)
	Yes	51 (56.0)	41 (47.7)
CCRT status				0.040
	No	28 (30.4)	39 (45.3)
	Yes	64 (69.6)	47 (54.7)
Adjuvant chemotherapy status				0.010
	No	59 (64.1)	70 (81.4)
	Yes	33 (35.9)	16 (18.6)
Number of chemotherapies		2.98±4.62	2.12±6.73	0.318

Values are presented as mean ± standard deviation or number (%).

Bold values indicate statistical significance (p<0.05).

BMI, body mass index; CCRT, concurrent chemoradiotherapy; FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; LN, lymph node; PALND, para-aortic lymph node; PLND, pelvic lymph node; PMI, psoas muscle index.

Kaplan–Meier curve analysis was performed to compare DFS and OS according to the PMI status, as shown in Fig. 3. The median follow-up duration was 30.24±16.71 months. During the follow-up period, the recurrence and mortality rates were 31.5% and 9.0%, respectively. The 2-year DFS rates were 56.9% in the low PMI group and 83.0% in the high PMI group, and DFS was significantly longer in the high PMI group than in the low PMI group (p<0.001). In contrast, the 2-year OS rate was 89.5% in the low-PMI group and 92.0% in the high-PMI group; the difference in OS between the 2 groups was not statistically significant (p=0.395).

Fig. 3. Kaplan–Meier curves for (A) DFS and (B) OS according to PMI group: PMI <35 mm/m² (n=92) vs. PMI ≥35 mm/m² (n=86). Log-rank p-values were p<0.001 for DFS and p=0.395 for OS.

DFS, disease-free survival; OS, overall survival; PMI, psoas muscle index.

To identify risk factors associated with cervical cancer recurrence, univariate and multivariate Cox proportional hazards analyses were performed. The key variables with statistical significance or included in the multivariate adjustment are summarized in Table 2, while the remaining Cox proportional analyses results are provided in Table S2. In the univariate analysis of DFS, comorbidities, FIGO stage, tumor size, LN metastasis status, HPV infection status, adjuvant chemotherapy, number of chemotherapies, radical hysterectomy status, CA 19-9 level, and PMI status were all positively associated with cervical cancer recurrence. Multivariate Cox proportional hazards analysis was conducted by adjusting for variables that showed statistical significance in univariate analysis, including age, comorbidities, FIGO stage, tumor size, LN metastasis status, HPV infection status, CCRT, and number of chemotherapies. The results showed that a higher PMI was independently associated with a significantly reduced risk of cervical cancer recurrence (adjusted hazard ratio [HR]=0.30; 95% confidence interval [CI]=0.155–0.559; p<0.001). In contrast, advanced-stage group (adjusted HR=4.19; 95% CI=1.753–10.007; p=0.001), adjuvant chemotherapy status (adjusted HR=15.22; 95% CI=7.314–31.660; p<0.001), and an increased number of chemotherapies (adjusted HR=1.10; 95% CI=1.070–1.137; p<0.001) were significantly and positively associated with recurrence. To further assess whether the prognostic impact of PMI persisted beyond stage-related effects, we additionally performed exploratory stage-stratified analyses (early-stage group vs. advanced-stage group), as shown in Table S3. In the advanced-stage subgroup, high PMI remained significantly associated with a lower risk of recurrence, even after adjustment for treatment-related factors. In the early-stage subgroup, no DFS events occurred in the high PMI group; therefore, the HR was not estimable, but DFS differed significantly between groups by the log-rank test (p<0.001). Additionally, univariate and multivariate analyses using the Cox proportional hazards model were performed to identify risk factors affecting OS in patients with cervical cancer. However, no variables were statistically significant predictors of OS. The statistics for this are presented in the Table S4.

Table 2. Cox proportional hazards model of DFS in the univariate and multivariate analyses.

Variables		Risk factors for DFS
		HR (95% CI)	p-value	HR* (95% CI)	p-value
Age (yr)		1.02 (0.99–1.04)	0.058	0.99 (0.97–1.02)	0.920
Comorbidities
	No	1.00 (Reference)		1.00 (Reference)
	Yes	1.91 (1.13–3.24)	0.016	1.02 (0.54–1.90)	0.962
FIGO stage
	Early-stage group	1.00 (Reference)		1.00 (Reference)
	Advanced-stage group	7.00 (3.00–16.34)	<0.001	4.19 (1.75–10.01)	0.001
Tumor size
	≤4 cm	1.00 (Reference)		1.00 (Reference)
	>4 cm	2.08 (1.21–3.55)	0.008	0.59 (0.30–1.15)	0.121
LN metastasis status
	Negative	1.00 (Reference)		1.00 (Reference)
	PLND metastasis	3.74 (2.01–6.98)	<0.001	1.39 (0.62–3.08)	0.424
	PLND and PALND metastases	5.54 (2.83–10.85)	<0.001	1.98 (0.82–4.77)	0.127
HPV infection status
	No	1.00 (Reference)		1.00 (Reference)
	Yes	2.02 (1.16–3.51)	0.013	1.14 (0.61–2.13)	0.686
CCRT status
	No	1.00 (Reference)		1.00 (Reference)
	Yes	1.69 (0.92–3.10)	0.090	0.95 (0.46–1.94)	0.880
Adjuvant chemotherapy status
	No	1.00 (Reference)		1.00 (Reference)
	Yes	20.55 (10.47–40.32)	<0.001	15.22 (7.31–31.66)	<0.001
Number of chemotherapies		1.10 (1.08–1.13)	<0.001	1.10 (1.07–1.14)	<0.001
PMI status
	Low PMI	1.00 (Reference)		1.00 (Reference)
	High PMI	0.30 (0.16–0.55)	<0.001	0.30 (0.16–0.56)	<0.001

Bold values indicate statistical significance (p<0.05).

CCRT, concurrent chemoradiotherapy; CI, confidence interval; DFS, disease-free survival; FIGO, International Federation of Gynecology and Obstetrics; HPV, human papillomavirus; HR, hazard ratio; LN, lymph node; PALND, para-aortic lymph node; PLND, pelvic lymph node.

^*HR adjusted for age, comorbidities, FIGO stage, tumor size, LN metastasis status, HPV infection status, CCRT, and number of chemotherapies.

DISCUSSION

Our analysis revealed that a higher PMI measured at diagnosis was significantly associated with a reduced risk of recurrence in cervical cancer. However, no significant association was observed between PMI and OS. To the best of our knowledge, this is the first study to evaluate the association between PMI and DFS in cervical cancer using abdominal CT at diagnosis.

Cancer cachexia affects approximately 50%–80% of patients with cancer and, in severe cases, can directly contribute to mortality in up to 20% of cases [20]. Cachexia is a multifactorial syndrome characterized by involuntary weight loss, anorexia, and skeletal muscle wasting. Patients with cancer-associated cachexia exhibit poor tolerance to anticancer treatment, diminished therapeutic response, reduced quality of life, and worse survival outcomes [20,21]. For these reasons, preventing muscle loss through regular physical activity and adequate intake of high-quality protein may help reduce the risk of cachexia and promote better treatment outcomes. Accordingly, the role of skeletal muscle preservation in cancer care has been increasingly emphasized [21]. Skeletal muscle mass is commonly assessed using the SMI [22]. SMI is calculated by measuring the cross-sectional muscle area at the L3 level and dividing it by height squared (m²) [22]. Several studies have used SMI to assess muscle mass in patients with cancer and have reported that a lower SMI is associated with worse survival outcomes [23,24,25]. However, other studies have reported no significant association between SMI and survival outcomes such as DFS or OS [22,26,27]. Therefore, the relationship between SMI and cancer prognosis remains unclear. Among the previous studies, Ueki et al. [22] conducted a retrospective study in patients with metastatic renal cell carcinoma. They evaluated the association between skeletal muscle mass and survival outcomes using both PMI and SMI. The results demonstrated no significant association between the SMI and survival outcomes; however, a lower PMI was significantly associated with an increased risk of poorer survival [22]. In a study by Zhang et al. [7] involving patients with hepatocellular carcinoma, the PMI, in addition to the SMI, was identified as a significant predictor of OS. Moreover, the study emphasized that PMI is not only predictive, but also a practical, simple, and reliable tool for assessing skeletal muscle mass in cancer patients [7]. SMI typically requires specialized software to measure the cross-sectional area of multiple muscles at the L3 level. In contrast, PMI can be assessed more easily using standard CT images. Owing to its simplicity and practicality, the psoas muscle has garnered increasing interest as a surrogate marker for assessing skeletal muscle mass in clinical settings.

Our findings demonstrate that a higher PMI is associated with a reduced risk of recurrence in patients with cervical cancer. Consistent with our results, several studies using psoas-based indices reported that greater muscle loss is associated with poorer oncologic outcomes, including shorter DFS and OS [7,9,12,13,14,15,22,28,29]. However, the literature shows considerable variability in how the psoas muscle is measured, and no standardized PMI method has been established. In several studies, the PMI was calculated by summed cross-sectional area of both psoas muscles on axial CT at the L3 level divided by height squared (m²) [12,14,15,22]. Yoshikawa et al. [28] measured bilateral psoas areas at the L5 level and normalized the value by height squared. Other studies used linear measurements at L3 (longest and perpendicular diameters of both psoas muscles divided by height squared), which yielded results similar to ours [7,9,29]. Additional approaches have included measurements at the umbilical level normalized by height (m) rather than height squared [13]. These inconsistencies underscore the need for standardized PMI measurement protocols based on expert consensus and methodological validation. Although the mechanisms underlying the association between skeletal muscle loss and cancer progression remain incompletely understood, several plausible pathways have been proposed, as shown in Fig. 4. Skeletal muscle acts as an endocrine organ by releasing myokines involved in immune regulation and tumor suppression [4]. Reduced muscle mass may impair anti-tumor immunity by altering natural killer cell cell and T-cell–related responses [30]. Muscle loss is also linked to insulin resistance and dysregulated growth factor signaling, including the insulin-like growth factor-1/insulin-like growth factor binding protein-3 axis, which may promote tumor growth and adverse outcomes [31,32,33,34,35,36]. In addition, mitochondrial dysfunction and oxidative stress associated with muscle loss may contribute to systemic immune dysregulation and a pro-inflammatory, immunosuppressive milieu that facilitates tumor progression [5,37]. Collectively, these mechanisms provide biological support for our finding that lower PMI is associated with increased cancer progression. Not all studies have found consistent associations between psoas measures and cancer outcomes [16,17,38]. For example, Yao et al. [16] reported that lower psoas muscle density was associated with shorter OS, whereas psoas area was not. Similarly, Rutten et al. [17] (ovarian cancer) and Kim et al. [38] (bile duct cancer) found no significant relationship between psoas area and DFS or OS. One potential explanation is that psoas measurements at L3 may be influenced by degenerative spine conditions, such as disc degeneration or facet disease, leading to psoas loss that is not directly attributable to cancer-related sarcopenia [17,39]. Therefore, psoas-based indices may not always reflect systemic muscle wasting in all patient populations. Most existing studies have assessed the PMI in Western populations, raising concerns regarding generalizability. Given the differences in skeletal structure and body habitus across ethnic groups, PMI values are likely to vary. Therefore, further research is needed to develop standardized methods for PMI measurement and establish ethnicity-specific reference values for the diagnosis of sarcopenia.

Fig. 4. Proposed biological mechanisms linking skeletal muscle loss to tumor progression.

DAMP, damage-associated molecular pattern; GLUT4, glucose transporter type 4; IGF-1, insulin-like growth factor-1; IGFBP-3, insulin-like growth factor binding protein-3; IL, interleukin; mtDNA, mitochondrial DNA; NK, natural killer.

This study has some limitations. First, its retrospective design did not allow longitudinal follow-up of patients, which limits the ability to infer causal relationships. Second, a selection bias may have been introduced during recruitment. Third, our study was conducted at a single institution, and the relatively small sample size may limit the generalizability of the findings compared with multicenter studies. In addition, this study included Korean patients from a single institution, and body composition measures such as PMI can vary according to ethnicity, nutritional status, and body habits. For this reason, our findings may have limited generalizability to multiethnic or different population groups. To enable broader clinical use of PMI as a prognostic indicator for cervical cancer recurrence, external validation studies using multicenter or population-based cohorts with diverse ethnic backgrounds are needed. In such studies, the potential need for population or ethnicity-specific cutoff adjustments should be explored, for example by re-establishing candidate thresholds in each population and confirming their performance in independent external cohorts. Alternatively, PMI may be considered as a continuous or standardized measure, such as z-scores or percentiles, to reduce misclassification when applying a single universal cutoff across heterogeneous populations. Fourth, although PMI offers a convenient CT-based measure of muscle mass and allows an indirect assessment of sarcopenia, it does not reflect muscle quality or functional status. Because sarcopenia involves changes in both muscle mass and functional capacity, relying on PMI alone cannot capture its full complexity. CT images can also provide information on muscle quality, such as muscle radiodensity measured in Hounsfield units, which reflects fat infiltration within the muscle and may add prognostic value beyond muscle size alone [40]. In addition, clinical performance scores, such as the Eastern Cooperative Oncology Group performance status, and other simple measures for physical function may better represent functional impairment. Therefore, future studies should integrate measures of muscle quantity, muscle quality, and functional performance to provide a more comprehensive assessment of sarcopenia and to better understand how these combined factors relate to cervical cancer recurrence. Fifth, as this was a retrospective study, treatment decisions were made according to the institutional standard protocols, which were primarily based on clinical stage, LN metastasis status, and tumor size. PMI values were not incorporated into treatment decision-making, as this study focused on evaluating the prognostic value of PMI rather than its role in guiding therapy. Although we adjusted for major clinical confounders–including age, comorbidities, FIGO stage, tumor size, LN metastasis status, HPV infection status, CCRT, and the number of chemotherapy cycles–in the multivariate Cox proportional hazards model, PMI remained independently associated with a significantly lower risk of recurrence (adjusted HR=0.30; p<0.001). Nevertheless, as with any retrospective analysis, the possibility of residual confounding cannot be fully excluded. To address this concern, we performed exploratory stage-stratified analyses (early vs. advanced), which are presented in Table S3. Future studies with larger multicenter cohorts and adequate event numbers will be essential to clarify whether PMI consistently retains its prognostic significance across different disease stages and treatment subgroups, and to more definitively establish its potential role in individualized treatment planning. Sixth, the median follow-up duration of 30.2 months may have been relatively short, which could have led to an underestimation of DFS. In particular, the relatively short follow-up period and the limited number of death events may have reduced the ability to detect a statistically meaningful association between PMI and OS. Therefore, the lack of significance observed in the OS analysis should be interpreted with caution, as it may reflect insufficient follow-up rather than a true absence of prognostic relevance. In our cohort, the median OS was not reached during the follow-up period, reflecting the limited number of death events (n=16). With longer-term follow-up, the association between PMI and OS may become more apparent or could change. However, as most cervical cancer recurrences are known to occur within the first 2–3 years after primary treatment, the follow-up period was considered sufficient to capture the majority of recurrence events. Given these limitations, the statistical power of our study may be insufficient to establish the PMI as a definitive prognostic factor for survival outcomes in cervical cancer. Although the calculation method of PMI has not yet been standardized, further studies are warranted to clarify and unify measurement techniques. In particular, age-stratified analyses and investigations involving multiethnic populations would help establish reference values that account for physiological differences in muscle mass among individuals. Furthermore, since the study primarily focused on DFS, additional research should explore whether PMI can also predict OS or serve as a prognostic marker within specific subgroups of cervical cancer, such as by histologic type, FIGO stage, or treatment modality. To validate the prognostic value of PMI and clarify its association with recurrence or OS in patients with cervical cancer, future prospective studies with larger multicenter cohorts and longitudinal follow-up are warranted.

In conclusion, a low PMI was significantly associated with poorer DFS in patients with cervical cancer. These results suggest that PMI may serve as a valuable prognostic indicator for identifying patients at higher risk of recurrence.

SUPPLEMENTARY MATERIALS

Table S1

Extended baseline characteristics of the study participants according to PMI status

Table S2

Extended Cox proportional hazards model of DFS in the univariate and multivariate analyses

Table S3

Stage-stratified analyses of DFS according to PMI group

Table S4

Cox proportional hazard models of OS in univariate and multivariate analyses