Abstract
Background/Objectives
Sarcopenia, characterized by the progressive loss of skeletal muscle mass and function, has been linked to poor oncological outcomes. This study aimed to assess the relationship between sarcopenia—defined through combined radiological and biochemical assessments—and survival outcomes in patients with rectal cancer.
Methods
Sarcopenia was evaluated using radiological measurements of skeletal muscle mass, visceral and subcutaneous fat tissue volumes, and biochemical parameters including albumin, protein, and Fib-4 index levels.
Results
Deceased patients were older than survivors (mean 70 vs. 63years). Elevated Fib-4 scores (3.0–4.9) were mainly observed in non-operated patients with poor tumor regression. Post-treatment albumin levels were significantly higher in patients with complete response (42.0±3.5mg/dL) than in those with regression score-3 (37.3±8.7mg/dL) and non-operated patients (34.9±8.3mg/dL; p < 0.001). Pre-treatment skeletal muscle mass, subcutaneous fat, and visceral fat volumes were greater in survivors (22.3±7.0cm3 vs 19.2±7.0cm3, 27.7±20.3cm3 vs 18.7±14.0cm3 and 49.7±37.8cm3 vs 29.9±20.5cm3 respectively) than in deceased patients (p < 0.05). Larger tissue volumes—muscle≥16.95cm3, visceral fat≥39.35cm3, and subcutaneous fat≥17.65 cm3 were associated with longer overall survival. In univariate analysis, older age, low albumin, high Fib-4 index, and reduced tissue volumes predicted poorer survival, while multivariate analysis identified low post-treatment albumin as the only independent prognostic factor (HR 0.28, 95% CI:0.12–0.65, p = 0.003).
Conclusions
Sarcopenia is associated with decreased overall survival in rectal cancer. In patients receiving neoadjuvant therapy, lower volumes of muscle mass, subcutaneous fat, and visceral fat, together with lower albumin and protein levels and higher Fib-4 scores, may serve as predictive markers of sarcopenia.
Keywords: Rectal cancer, sarcopenia, cross-section skeletal muscle mass volume measurement, subcutaneous fat, visceral fat, poor survival
Background
Sarcopenia, characterized by the loss of muscle mass and function, has been identified as an independent risk factor for poorer overall survival (OS) in patients with locally advanced rectal cancer [1]. The cross-sectional area of skeletal muscles at the level of the third lumbar vertebra (L3) on computed tomography (CT) images is a radiologic biomarker commonly used to assess the sarcopenia status of patients with sarcopenia or cancer cachexia [2]. Serum albumin levels are a marker of sarcopenia and an important biochemical marker in the prognostic evaluation of rectal cancer patients [3]. Low albumin levels may indicate poor nutritional status and higher tumor burden and have been associated with an overall poor prognosis. The fibrosis-4 (Fib-4) score is a biochemical test used to estimate liver fibrosis, usually resulting from chronic liver diseases, including non-alcoholic fatty liver disease [4]. The Fib-4 score also provides an idea of the prognosis for cancer patients [3,5]. Higher FIB-4 scores are associated with higher risks of sarcopenia [4].
For the detection of sarcopenia in cancer patients, skeletal muscle mass volume, serum albumin levels, and FIB-4 scores can be evaluated, providing a comprehensive view of the general health status of rectal cancer patients. Correct interpretation of sarcopenia data offers powerful clues to predict treatment responses and survival outcomes. However, diagnosing sarcopenia also requires awareness of sarcopenia prevention to improve treatment outcomes. Lifestyle changes are emphasized in managing both sarcopenia and liver fibrosis. This is recommended for all cancers in general and gastrointestinal cancers in particular. Regular physical activity, along with a balanced diet rich in essential nutrients, can help improve muscle mass, reduce liver fat accumulation, and enhance liver function, thereby aiding the development of sarcopenia-preventive strategies. Thus, integrating lifestyle changes into treatment plans can improve survival, especially in oncology patients, by maintaining both liver and muscle health and overall well-being.
Patients, materials & metods
This study reviewed 300 patients diagnosed with rectal cancer who were referred to the Department of Radiation Oncology at Recep Tayyip Erdogan University, Faculty of Medicine, Rize Education and Research Hospital between September 2013 and May 2023. To ensure dataset completeness and reliability, patients with missing data, unclear information, or unavailable imaging records were excluded. Among the two radiotherapy planning systems used in our clinic, only patients whose contours were delineated with the Varian treatment planning system were included. Those treated with the other device or with incomplete datasets were excluded to maintain methodological consistency and data homogeneity. The Varian contouring workstation automatically provides quantitative measurements, including Hounsfield units (HU) and tissue volume cubic centimeters (cm³), via the ‘Statistics with Structure’ module. Figure 1 demonstrates a representative example.
Figure 1.
A sample patient with contoured tissues in an axial CT section at the L3 vertebral level in the Radiotherapy Treatment Planning System (VarianR Medical Systems). After contouring the muscle, subcutaneous fat, and visceral fat the cm³ volumes automatically calculated by the system are shown. Muscles mass (psoas, erector spinae, quadratus lumborum, transversus abdominis, external and internal obliques, and rectus abdominis) are shown in green (a). Subcutaneous fat is shown in light yellow (b), and visceral fat is shown in pink (c). Automatically calculated cm³ volumes of tissues in the Radiotherapy Treatment Planning System (d-e-f).
A total of 96 patients who met the predefined eligibility criteria were included in the study. Eligible patients were required to have received neoadjuvant radiotherapy, be 18 years of age or older, have no evidence of distant metastasis (with the exception of resectable oligometastatic disease), and present with locally advanced tumors (T3–T4 and/or regional lymph node–positive disease). Patients with widespread distant metastatic spread or a diagnosis of secondary primary malignancies were excluded from the analysis. At our institution, patients are considered eligible for curative-intent rectal cancer surgery if they have no evidence of unresectable distant metastases, maintain adequate performance status (ECOG 0–2), and demonstrate sufficient organ function to tolerate major abdominal surgery. Surgical resection is routinely offered to patients with localized or locally advanced disease following neoadjuvant therapy. In cases of metastatic involvement, surgery is performed only when metastases are classified as operable oligometastatic lesions, as determined by a multidisciplinary tumor board. Patients with widespread or unresectable metastatic disease are managed non-operatively and were therefore excluded from this study.
All patients underwent surgery according to standardized institutional protocols based on tumor location, staging, and operability. As this was a retrospective study, no perioperative interventions specifically targeting muscle or nutritional status were applied beyond routine clinical practice. Thus, any surgery-related postoperative changes in body composition could not be evaluated separately. Given the retrospective nature of this study, the requirement for individual informed consent was waived. We followed the STROBE guidelines to optimize reporting and methodological design, ensuring the transparency, completeness, and reproducibility of the study. The study has received local ethics committee approval with the number E-40465587-050.01.04-787, dated 01.09.2023, and decision number 2023/188. Permission for research and data use was obtained from the Rize Governorship Provincial Health Directorate with the number E-64960800-799-217546661 and the date 09.06.2023, on the condition that the study is carried out by taking security measures regarding patient privacy and information security. The study used de-identified retrospective data, so it was exempt from the requirement for informed consent and was conducted according to the ethical principles outlined in the Declaration of Helsinki.
Patients were staged retrospectively according to the TNM staging system [6], and those receiving neo-adjuvant radiotherapy [7] were included in the study. ypT and ypN, reflecting the therapeutic response of the tumor, were used for post-treatment pathological staging [8]. Treatment response was graded using a modification of the Ryan tumor regression grade (TRGr) system [9]. TRGr-0: No response, TRGr-1: Minimal microscopic disease, TRGr-2: Moderate regression, TRGr-3: Macroscopic residue. In our study, the relationship between post-treatment TRGr score status and survival, along with radiological and biochemical sarcopenia parameters that could affect them, were investigated.
Biochemical protein and albumin values, as well as pre-treatment and post-treatment values of CEA and CEA19-9, which may be associated with tumor burden, were recorded along with the Fib-4 score as indicators of patients’ nutritional habits [5,10].
Patients were selected by scanning radiotherapy planning computed tomography (CT) images taken with the Aquilion LB brand CT radiotherapy simulator scanner (Toshiba Medical Systems, Tokyo, Japan) [11]. CT images of the pelvic area with a minimum upper limit, including two lumbar vertebrae and a slice thickness of 3 mm, were determined based on the transverse non-contrast CT images at the L3 level [2,12–15]. The radiotherapy planning system calculated muscle mass, subcutaneous fat, and visceral fat tissue volumes in cm³ using Varian Medical Systems, Eclipse Version 13.6 (Varian Trilogy IX, Varian Medical Systems, Eclipse Version 13.6, Varian Palo Alto, CA, USA) in a single section. The muscles mass were contoured (Figure 1a) as psoas, erector spinae, quadratus lumborum, transversus abdominis, external and internal oblique, and rectus abdominis [15]. For the fat tissue, the subcutaneous fat tissue volume (Figure 1b) and for the visceral fat, the inner mesenteric fat tissue area (Figure 1c) were contoured. In Figure 1, the contours and calculated volumes of muscle mass, subcutaneous fat, and visceral fat are shown in a sample patient. All volumetric measurements were performed manually on axial CT images using the radiotherapy contouring workstation (Varian Eclipse) routinely employed in our department. The contouring was jointly conducted by a radiation oncologist and a radiologist, each with more than five years of experience in oncologic imaging. In cases of uncertainty, the final contours were determined by consensus. Inter-observer agreement was assessed using the intraclass correlation coefficient (ICC), demonstrating excellent consistency (ICC = 0.92, 95% CI: 0.87–0.96). CT scans were obtained with standardized parameters (120 kVp, 200–250 mAs, slice thickness 3 mm).
Patient follow-up
Patient follow-up was performed using the national electronic hospital information system (Aksaglık module) operated by the Ministry of Health of Türkiye. The most recent outpatient visit dates were verified through both the Aksaglık system and the e-Nabız national health database, which integrates patient records from multiple hospitals and cities. Mortality data were confirmed through the official Death Notification System after obtaining authorization from the local health authority. These combined data sources ensured the accuracy and completeness of survival information, and no patients were lost to follow-up during the study period.
Statistical evaluation
After the data obtained from the research was coded, it was transferred to and evaluated in the SPSS (Statistical Package for Social Sciences) (Version 22 for Windows, SPSS Inc, Chicago, IL, USA) software for analysis. Continuous variables were expressed as mean ± standard deviation and median (minimum value - maximum value), while categorical variables were defined using number (%). The normality of continuous variables was assessed using the Shapiro-Wilk Test. Pearson chi-square was used for intergroup comparisons of categorical variables. Since continuous variables did not conform to normal distribution, the Mann-Whitney U Test was used for comparisons between two groups. For more than two groups, the data were compared using the Kruskal-Wallis test. Survival assessment was performed using Kaplan-Meier analysis.
OS was defined as the time from diagnosis of rectal cancer to death from any cause or the date of last follow-up. Receiver operating characteristic (ROC) curve analyses were conducted for continuous variables including muscle mass, subcutaneous fat, and visceral fat to evaluate their prognostic significance for OS. Optimal cut-off values were determined using the Youden Index. Kaplan–Meier survival curves were generated for OS, and differences between groups were compared using the log-rank test. Univariate Cox regression analysis was performed to estimate hazard ratios (HRs) for OS. Variables found to be statistically significant in univariate analysis were included in multivariate Cox regression models. All statistical tests were two-sided, and a p-value <0.05 was considered indicative of statistical significance.
Results
Patient characteristics
The mean age of the patients was 65.9 years (range, 43–89 years). The cohort included 39 females and 57 males, corresponding to a female-to-male ratio of 0.68, indicating a male predominance. The age of those with regression response TRGr-1 was 62.1 ± 8.6, while the oldest group of non-operated patients was 74.0 ± 9.8. There was no significant difference in regression scores by gender (p = 0.717). Stage T3 (n = 80, 83.3%) was predominant, and the nodal stage N1 rate was 60.4% (n = 58). The number of patients with distant metastasis at diagnosis was 12. In 43 patients (44.8%), the tumor was located distally (≤5 cm), while in 20 (20.8%) patients, it was located proximally (11–15 cm). Non-operative management was applied in 32.3% (n = 31) of patients due to comorbidities or refusal of surgery. The majority of the distal location group, n = 18 (58.1%), was in the non-operated group.
Tumor regression, biochemical markers, and tissue volumes
Pathological complete response (TRGr-0) was achieved in 11.4% of patients, with TRGr-1, TRGr-2, and TRGr-3 observed in 9.4%, 21.8%, and 9.4%, respectively. Key parameters related to TRGr scores are detailed in Table 1.
Table 1.
Treatment response status of patients diagnosed with rectal cancer according to the ‘modification of the ryan tumor regression grade’ (TRGr) score and associated parameters.
| Variables | Unknown (n:15) |
TRGr −0 (n:11) |
TRGr −1 (n:9) |
TRGr-2 (n:21) |
TRGr-3 (n:9) |
Non-operable (n:31) |
p |
|---|---|---|---|---|---|---|---|
| Age (year) | |||||||
| Mean ± Sd | 65.1 ± 12.5 | 62.1 ± 8.6 | 59.7 ± 8.4 | 60.1 ± 9.7 | 61.4 ± 14.5 | 74.0 ± 9.8 | <0.001 |
| Median(min-max) | 68(48–86) | 64 (51–76) | 59 (50–78) | 58 (46–78) | 63 (43–81) | 75 (51–94) | |
| Gender | |||||||
| M (n = 57) | 11 (73.3) | 7 (63.6) | 6 (66.7) | 11 (52.4) | 6 (66.7) | 16 (51.6) | 0.717 |
| F (n = 39) | 4 (26.7) | 4 (36.4) | 3 (33.3) | 10 (47.6) | 3 (33.3) | 15 (48.4) | |
| Pathological type | |||||||
| Adeno (n = 93) | 14 (93.3) | 11(100.0) | 9(100.0) | 19(90.5) | 9 (100.0) | 31 (100.0) | 0.369 |
| Mucinous (n = 3) | 1 (6.7) | 0 (0.0) | 0 (0.0) | 2 (9.5) | 0 (0.0) | 0 (0.0) | |
| Tumor stage | |||||||
| 2 (n = 3) | 1 (6.7) | 0 (0.0) | 0 (0.0) | 2 (9.5) | 0 (0.0) | 0 (0.0) | 0.222 |
| 3 (n = 80) | 13 (86.7) | 10 (90.9) | 9 (100.0) | 18 (85.7) | 7 (77.8) | 23 (74.2) | |
| 4 (n = 13) | 1 (6.7) | 1(9.1) | 0 (0.0) | 1 (4.8) | 2 (22.2) | 8 (25.8) | |
| Nodal stage | |||||||
| 0 (n = 18) | 5 (33.3) | 0 (0.0) | 1 (11.1) | 2 (9.5) | 1 (11.1) | 9 (29.0) | 0.358 |
| 1 (n = 58) | 9 (60.0) | 7 (63.6) | 6 (66.7) | 13 (61.9) | 6 (66.7) | 17(54.8) | |
| 2 (n = 20) | 1 (6.7) | 4 (36.4) | 2 (22.2) | 6 (28.6) | 2 (22.2) | 5 (16.1) | |
| Metastasis | |||||||
| 0 (n = 84) | 13 (86.7) | 11(100.0) | 9(100.0) | 21(100.0) | 7 (77.8) | 23(74.2) | 0.040 |
| 1 (n = 12) | 2 (13.3) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 2 (22.2) | 8 (25.8) | |
| Tumor localization | |||||||
| ≤5cm n = 43) | 9 (60.0) | 3 (27.3) | 1 (11.1) | 9 (42.9) | 3 (33.3) | 18 (58.1) | 0.030 |
| 6-10 (n = 33) | 3 (20.0) | 3 (27.3) | 3 (33.3) | 8 (38.1) | 4 (44.4) | 12 (328.7) | |
| 11–15(n = 20) | 3 (20.0) | 5 (45.5) | 5 (55.6) | 4 (19.0) | 2 (22.2) | 1 (3.2) | |
| PNI | |||||||
| Unknown | 9 (60.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 31 (100.0) | <0.001 |
| Yes | 0 (0.0) | 0 (0.0) | 1 (11.1) | 3 (14.3) | 4 (44.4) | 0 (0.0) | |
| No | 6 (40.0) | 11(100.0) | 8 (88.9) | 18 (85.7) | 5 (55.6) | 0 (0.0) | |
| LVI | |||||||
| Unknown | 9 (60.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 31 (100.0) | <0.001 |
| Yes | 0 (0.0) | 0 (0.0) | 1 (11.1) | 1 (4.8) | 4 (44.4) | 0 (0.0) | |
| No | 6 (40.0) | 11(100.0) | 8 (88.9) | 20 (95.2) | 5 (55.6) | 0 (0.0) | |
| Local recurrence | |||||||
| Unknown | 1 (6.7) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 3 (9.7) | 0.037 |
| Dysplasia | 1 (6.7) | 1 (9.1) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | |
| Yes (n = 12) | 2 (13.3) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1 (11.1) | 9 (29.0) | |
| No (n = 78) | 11 (73.3) | 10 (90.9) | 9(100.0) | 21(100.0) | 8 (88.9) | 19 (61.3) | |
| Last follow up | |||||||
| Live | 5 (33.3) | 9 (81.8) | 7 (77.8) | 20 (95.2) | 5 (55.6) | 10 (32.3) | <0.001 |
| Deceased | 10 (66.7) | 2(18.2) | 2(22.2) | 1 (4.8) | 4 (44.4) | 21 (67.7) | |
| Post-treatment M | |||||||
| 0 (n = 71) | 8 (53.3) | 10 (90.9) | 6 (66.7) | 19 (90.5) | 6 (66.7) | 22 (70.9) | 0.126 |
| 1 (n = 25) | 7 (46.7) | 1 (9.1) | 3 (33.3) | 2 (9.5) | 3 (33.3) | 9 (29.1) | |
| FIB-4 score | |||||||
| Unknown | 0 (0.0) | 1 (9.1) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 3 (9.7) | 0.211 |
| 0.0–2.9 | 15(100.0) | 10 (90.9) | 9(100.0) | 21(100.0) | 8 (88.9) | 24 (77.4) | |
| 3.0–4.9 | 0 (0.0) | 0 (0.0) | 0 (0.0) | 0 (0.0) | 1(11.1) | 4 (12.9) | |
| Pre-treatment CEA (U/ml) | |||||||
| Mean ± SD | 11.7 ± 15.1 | 13.2 ± 23.1 | 6.5 ± 8.5 | 4.6 ± 6.9 | 20.6 ± 28.0 | 16.4 ± 27.4 | 0.089 |
| Median (min-max) | 5 (2–51) | 2 (1–68) | 4 (1–27) | 3(1–33) | 3 (2–77) | 7 (1–117) | |
| Post- treatment CEA (U/ml) | |||||||
| Mean ± SD | 667.2 ± 2187.4 | 45.7 ± 140 | 28.8 ± 67.7 | 1.4 ± 0.8 | 754.1 ± 2063 | 64.2 ± 173.0 | <0.001 |
| Median (min-max) | 4 (1–7934) | 1(1–445) | 1(1–195) | 1(1–4) | 3 (1–5858) | 6 (1–685) | |
| Pre-treatment CA19 9 (U/ml) | |||||||
| Mean ± SD | 8.2 ± 7.1 | 77.1 ± 172 | 10.2 ± 7.5 | 10.0 ± 12.6 | 9.8 ± 16.0 | 78.3 ± 256.0 | 0.016 |
| Median (min-max) | 5.0 (2–23) | 24.5 (2–535) | 8(2–24) | 4(2–42) | 2 (1–50) | 15.0(1–1343) | |
| Post- treatment CA19 9 (U/ml) | |||||||
| Mean ± SD | 273.3 ± 816.0 | 6.3 ± 4.5 | 136.3 ± 378 | 8.4 ± 9.8 | 19.3 ± 34.5 | 380.3 ± 1364 | 0.007 |
| Median (min-max) | 8.9 (2–2975) | 7(1–16) | 9(3–1146) | 4.5(1–29) | 7 (1–103) | 20.0(2–6571) | |
| Pre-treatment albumin (mg/dL) | |||||||
| Mean ± SD | 41.2 ± 3.8 | 41.9 ± 3.7 | 43.0 ± 2.8 | 39.2 ± 7.7 | 38.1 ± 12.9 | 39.2 ± 3.4 | 0.072 |
| Median (min-max) | 41.0 (36–48) | 41.0(38–48) | 43.0(37–47) | 40.0(8–45) | 43 (4–44) | 40.0 (30–45) | |
| Post-treatment albumin (mg/dL) | |||||||
| Mean ± SD | 37.1 ± 8.2 | 42.0 ± 3.5 | 42.8 ± 6.8 | 44.7 ± 6.2 | 37.3 ± 8.7 | 34.9 ± 8.3 | <0.001 |
| Median (min-max) | 39.0 (21–45) | 42.0 (36–46) | 44.0(28–52) | 45.0(35–68) | 42 (22–45) | 37.5(14–46) | |
| Pre-treatment protein (mg/dL) | |||||||
| Mean ± SD | 72.1 ± 4.2 | 73.4 ± 5.5 | 72.1 ± 4.4 | 67.5 ± 16.3 | 66.3 ± 22.8 | 73.3 ± 6.4 | 0.924 |
| Median (min-max) | 71.0 (67–81) | 73.5(63–82) | 71.0(64–78) | 71.0(4–81) | 74 (7–81) | 73.0(63–94) | |
| Post-treatment protein (mg/dL) | |||||||
| Mean ± Sd | 69.8 ± 8.1 | 71.7 ± 5.2 | 71.2 ± 7.6 | 73.7 ± 8.1 | 66.6 ± 12.4 | 65.6 ± 9.7 | 0.011 |
| Median (min-max) | 70.0 (54–80) | 71.5(61–78) | 73.0(61–85) | 74.0(43–83) | 71 (48–78) | 67.0(37–78) | |
| Muscle mass volume (cm3) | |||||||
| Mean ± SD | 20.5 ± 5.6 | 21.3 ± 7.0 | 25.1 ± 5.7 | 22.3 ± 7.2 | 23.4 ± 10.6 | 18.6 ± 6.6 | 0.128 |
| Median (min-max) | 19.2(12.7–31.6) | 20.1(10-33.4) | 24(17.5–34.2) | 21.7(11.2–39.2) | 18 (12.3–41.5) | 16.8(5-32.3) | |
| Adipose volume (cm3) | |||||||
| Mean ± SD | 19.3 ± 14.0 | 19.8 ± 12 | 32.8 ± 22.8 | 22.4 ± 14.2 | 40.6 ± 31.1 | 21.6 ± 16.6 | 0.275 |
| Median (min-max) | 14.2(0.2–50.5) | 18.2(5.9–48.1) | 28.7(4.8–68.9) | 20.4(2.2–51.8) | 23 (13-97.9) | 16.0(0.8–48.5) | |
| Visceral fat volume (cm3) | |||||||
| Mean ± SD | 34.6 ± 26.4 | 55.7 ± 48 | 45.4 ± 36.2 | 38.6 ± 22.3 | 59.1 ± 51.7 | 35.4 ± 27.4 | 0.469 |
| Median (min-max) | 33.9(2.0–102.8) | 36.8(3.6–142.3) | 36.4(11.8–127.1) | 36.5(8.9–86.7) | 47(10.9-190) | 25.9(3.1–103.3) | |
TRGr: Modification of the Ryan tumor regression grade, Adeno: adenocarcinoma, Mucinous: mucinous carcinoma, PNI: Perineural invasion, LVI: Lymphovascular invasion, T: Tumor, N: Nodal metastasis, M: Distal metastasis. CEA: Carcinoembryonic antigen, CA19-9: Carbohydrate antigen.
All patients with a Fib-4 score of 3.0–4.9 (n = 5) were in the group that could not undergo surgery and had a TRGr-3. However, there was no significant relationship between the Fib-4 score and the regression score (p = 0.211). Pre-treatment CEA (U/mL) was found to be higher in patients with a complete response (13.2 ± 23.1) compared to TRGr-3 patients (20.6 ± 28.0) (p = 0.089). Post-treatment CEA in the complete response patients was 45.7 ± 140, while in TRGr-3 patients it was 754.1 ± 2063; the difference was statistically significant (p < 0.001).
Pre-treatment values of CA19.9 (U/mL) decreased in comparison to post-treatment values in the complete response group (77.1 ± 172 vs 6.3 ± 4.5), while they were statistically significantly higher in the group that could not undergo surgery (78.3 ± 256.0 vs 380.3 ± 1364). There was no statistical significance between pre- and post-treatment albumin levels. However, in the complete response group, albumin was 41.9 ± 3.7 mg/dL, while it was lower in TRGr-3 and the group that could not undergo surgery (38.1 ± 12.9 and 39.2 ± 3.4, respectively). Post-treatment albumin was 42,0 ± 3,5 in patients with complete response, while it was found to be lower in TRGr-3 and the group that could not undergo surgery (37.3 ± 8.7 and 34.9 ± 8.3, respectively, p < 0.001).
Tissue volumes in patients with complete response compared to those those who could not undergo surgery (muscle mass volume: 21.3 ± 7.0 vs 18.6 ± 6.6 and visceral fat: 55.7 ± 48 vs 35.4 ± 27.4) were lower, while adipose tissue volume was found to be higher (19.8 ± 12 vs 21.6 ± 16.6 (40.6 ± 31.1 for TRGr-3) (p > 0.005) (Table 1).
Survival-based differences
Prognostic features of living and dead patients are compared in Table 2. The proportion of surviving patients was 58.3% (n = 56), while 41.7% (n = 40) had died. The deceased group was significantly older than the surviving group (68.9 ± 11.6 vs. 63.4 ± 11.6; p = 0.033).
Table 2.
Prognostic features in surviving and deceased patients with rectal cancer.
| Variables | Live (n:56) %58.3 | Deceased (n:40) 41.7% | p |
|---|---|---|---|
| Age (year) | |||
| Mean ± Sd | 63.4 ± 11.6 | 68.9 ± 11.6 | 0.033 |
| Median (min-max) | 63 (43–89) | 70 (48–94) | |
| Gender | |||
| Male | 32 (57.1) | 25 (62.5) | 0.598 |
| Female | 24 (42.9) | 15 (37.5) | |
| Initial T stage | |||
| 2 | 3 (5.4) | 0 (0.0) | 0.321 |
| 3 | 46 (82.1) | 34 (85.0) | |
| 4 | 7 (12.5) | 6 (15.0) | |
| Initial N stage | |||
| 0 | 8 (14.3) | 10 (25.0) | 0.328 |
| 1 | 37 (66.1) | 21 (52.5) | |
| 2 | 11 (19.6) | 9 (22.5) | |
| Initial M | |||
| 0 (n = 84) | 54 (96.4) | 30 (75.0) | 0.002 |
| 1 (n = 12) | 2 (3.6) | 10 (25.0) | |
| Tumor location | |||
| ≤ 5 cm | 18 (32.1) | 25 (62.5) | 0.012 |
| 6-10cm | 23 (41.1) | 10 (25.0) | |
| 11–15cm | 15 (26.8) | 5 (12.5) | |
| PNI | |||
| Unknown | 13 (23.2) | 27 (67.5) | <0.001 |
| Yes | 4 (7.1) | 4 (10.0) | |
| No | 39 (69.6) | 9 (22.5) | |
| LVI | |||
| Unknown | 13 (23.2) | 27 (67.5) | <0.001 |
| Yes | 2 (3.6) | 4 (10.0) | |
| No | 41 (73.2) | 9 (22.5) | |
| Treatment response score | |||
| Unknown | 6 (10.7) | 9 (22.5) | 0.003 |
| 0 | 9 (16.1) | 2 (5.0) | |
| 1 | 7 (12.5) | 2 (5.0) | |
| 2 | 19 (33.9) | 2 (5.0) | |
| 3 | 5 (8.9) | 4 (10.0) | |
| Non-operable | 10 (17.9) | 21 (52.5) | |
| Post-treatment relapse | |||
| Unknown | 0 (0.0) | 4 (10.0) | 0.001 |
| Dysplasia | 1 (1.8) | 1 (2.5) | |
| Yes | 2 (3.6) | 10 (25) | |
| No | 53 (94.6) | 25 (62.5) | |
| Post- treatment N | |||
| Unknown | 16 (28.6) | 27 (67.5) | <0.001 |
| 0 | 36 (64.3) | 7 (17.5) | |
| 1 | 4 (7.1) | 6 (15.0) | |
| Post-treatment M | |||
| 0 | 49 (87.5) | 21 (53.8) | <0.001 |
| 1 | 7 (12.5) | 18 (46.2) | |
| Fib-4 score | |||
| Unknown | 2 (3.6) | 2 (5.0) | 0.644 |
| 0,0–2,9 | 52 (92.9) | 35 (87.5) | |
| 3,0–4,9 | 0 (0.0) | 3 (7.5) | |
| Pre-treatment CEA (U/ml) |
|||
| Mean ± Sd | 7.1 ± 12.6 | 18.9 ± 27.0 | 0.008 |
| Median (min-max) | 3 (1–77) | 7 (1–117) | |
| Post-treatment CEA (U/ml) |
|||
| Mean ± Sd | 6.7 ± 27.9 | 515.7 ± 1703.4 | <0.001 |
| Median (min-max) | 1 (1–195) | 6 (1–7934) | |
| Pre-treatment CA19-9 (U/ml) |
|||
| Mean ± Sd | 23.6 ± 76.1 | 57.8 ± 221.4 | 0.406 |
| Median (min-max) | 6.5 (1–535) | 6.8 (1–1343) | |
| Post-treatment CA19-9 (U/ml) |
|||
| Mean ± Sd | 9.3 ± 8.3 | 407.3 ± 1338.8 | 0.002 |
| Median (min-max) | 7.5 (1–29) | 15 (2–6571) | |
| Pre-treatment albumin (mg/dL) |
|||
| Mean ± Sd | 40.1 ± 7.3 | 40.1 ± 4.0 | 0.178 |
| Median (min-max) | 41.0(4–47) | 40 (30–48) | |
| Post-treatment albumin (mg/dL) |
|||
| Mean ± Sd | 43.4 ± 4.8 | 33.4 ± 8.3 | <0.001 |
| Median (min-max) | 43.0(30–68) | 34.5 (14–46) | |
| Pre-treatment protein (mg/dL) |
|||
| Mean ± Sd | 69.6 ± 13.9 | 72.9 ± 6.0 | 0.964 |
| Median (min-max) | 73 (4–82) | 71 (63–94) | |
| Post-treatment protein (mg/dL) |
|||
| Mean ± Sd | 72.7 ± 6.4 | 64.6 ± 10.4 | <0.001 |
| Median (min-max) | 73 (43–85) | 66 (37–80) | |
| Muscle mass volume (cm3) |
|||
| Mean ± Sd | 22.3 ± 7.0 | 19.2 ± 7.0 | 0.035 |
| Median (min-max) | 20.6 (11.2–41.5) | 17.2 (5-34.5) | |
| Subcutaneous fat volume (cm3) |
|||
| Mean ± Sd | 27.7 ± 20.3 | 18.7 ± 14.0 | 0.020 |
| Median (min-max) | 22.5 (0.2–97.9) | 14.1 (0.8–51.6) | |
| Visceral fat volume (cm3) |
|||
| Mean ± Sd | 49.7 ± 37.8 | 29.9 ± 20.5 | 0.005 |
| Median (min-max) | 40.2 (2-190.0) | 26.5 (3.1–83.5) |
T: Tumor, N: Nodal metastasis, M: Distal metastasis, PNI: Perineural invasion, LVI: Lymphovascular invasion.
Three patients with a FIB-4 score ≥3.0 were among the deceased. Post-treatment albumin levels were higher in surviving patients (43.4 ± 4.8) than in deceased patients (33.4 ± 8.3; p < 0.001), while pre-treatment levels did not differ (40.1 ± 7.3 vs. 40.1 ± 4.0; p = 0.178). Pre- and post-treatment CEA levels were significantly lower in survivors than in deceased patients (pre: 7.1 ± 12.6 vs. 18.9 ± 27.0, p = 0.008; post: 6.7 ± 27.9 vs. 515.7 ± 1703.4, p < 0.001). Post-treatment CA19-9 and protein levels were also significantly lower in survivors.
Skeletal muscle mass (22.3 ± 7.0 vs. 19.2 ± 7.0 cm³; p = 0.035), subcutaneous fat (27.7 ± 20.3 vs. 18.7 ± 14.0 cm³; p = 0.020), and visceral fat volumes (49.7 ± 37.8 vs. 29.9 ± 20.5 cm³; p = 0.005) were significantly higher in surviving patients.
Survival analyses
The optimal cut-off value for muscle mass was 16.95, yielding a sensitivity of 50%, a specificity of 78.6%, and an AUC of 0.62 (95% CI: 0.51–0.74; p = 0.03). The cut-off value for subcutaneous fat was 17.65, with a sensitivity of 62.5%, a specificity of 67.9%, and an AUC of 0.64 (95% CI: 0.52–0.75; p = 0.02). The cut-off value for visceral fat was 39.35, with a sensitivity of 77.5%, a specificity of 53.6%, and an AUC of 0.66 (95% CI: 0.56–0.77; p = 0.005). The ROC curves depicting the prognostic value of muscle mass, subcutaneous fat, and visceral fat for OS are shown in Figure 2.
Figure 2.
ROC curve analysis illustrating the prognostic performance of muscle, subcutaneous fat, and visceral fat in relation to overall survival: (a) muscle mass, (b) subcutaneous fat, and (c) visceral fat.
The median follow-up was 28 months (range, 2–95 months). The 1- and 2-year OS rates were 87.2% and 78.1%, respectively. Median OS was 66 months (95% CI: 44.4–87.5). Kaplan-Meier analysis showed that patients with muscle mass ≥16.95 (p = 0.008), visceral fat ≥39.35 (p = 0.003), and subcutaneous fat ≥17.65 (p = 0.009) had significantly longer OS (Figure 3).
Figure 3.
Overall survival of patients with rectal cancer stratified by muscle mass (a), subcutaneous fat (b) and visceral fat (c) status.
Univariate Cox regression identified older age (<65 vs. ≥65, HR (%95 Cl) 2.58 (1.32–5.05), p = 0.005), lower post-treatment albumin (<39.5 vs. ≥39.5, HR (%95 Cl) 0.18 (0.09–0.37), p < 0.001), higher FIB-4 index (<1.09 vs. ≥1.09, HR (%95 Cl) 2.26 (1.13–4.52), p = 0.02), and lower muscle mass (<16.95 vs. ≥16.95, HR (%95 Cl), 0.43 (0.23–0.82), p = 0.01), subcutaneous fat (<17.65 vs. ≥17.65, HR (%95 Cl), 0.43 (0.22–0.82), p = 0.01), and visceral fat (<39.35 vs. ≥39.35, HR (%95 Cl), 0.34 (0.16–0.72), p = 0.005) as predictors of poor OS. In multivariate analysis, post-treatment albumin remained the only independent predictor of OS (p = 0.003). HR (%95 Cl) 0.28 (0.12–0.65) p = 0.003 (Table 3).
Table 3.
Univariate and multivariate cox regression analysis for the prediction of overall survival.
| |
Univariate Analysis |
Multivariate Analysis |
|||
|---|---|---|---|---|---|
| Variables | Cut-off | HR (%95 Cl) | p | HR (%95 Cl) | p |
| Age (years) | <65 vs. ≥65 | 2.58 (1.32–5.05) | 0.005 | 1.48 (0.52–4.17) | 0.45 |
| Gender | Female vs. Male | 1.01 (0.53–1.93) | 0.96 | ||
| T Stage | T2-T3 vs. T4 | 1.58 (0.65–3.80) | 0.30 | ||
| N Stage | N0 vs. N1-2 | 0.73 (0.35–1.51) | 0.39 | ||
| Post-treatment albumin (mg/dL) | <39.5 vs. ≥39.5 | 0.18 (0.09–0.37) | <0.001 | 0.28 (0.12–0.65) | 0.003 |
| FIB-4 score | <1.09 vs. ≥1.09 | 2.26 (1.13–4.52) | 0.02 | 1.15 (0.39–3.34) | 0.79 |
| Muscle mass volume (cm3) | <16.95 vs. ≥16.95 | 0.43 (0.23–0.82) | 0.01 | 0.85 (0.38–1.87) | 0.69 |
| Subcutaneous fat volume (cm3) | <17.65 vs. ≥17.65 | 0.43 (0.22–0.82) | 0.01 | 0.72 (0.33–1.57) | 0.41 |
| Visceral fat volume (cm3) | <39.35 vs. ≥39.35 | 0.34 (0.16–0.72) | 0.005 | 0.56 (0.22–1.40) | 0.22 |
HR: hazard ratio; CI: confidence interval.
Discussion
Sarcopenia is a condition characterized by reduced skeletal muscle mass volume and is often associated with poor prognosis as a comorbidity in malignancies [16]. The association of sarcopenia with cancer outcomes has received increasing attention. In particular, it has been shown to potentially impact the prognosis of gastrointestinal tract cancers and has been associated with poor overall survival [17]. Recent meta-analyses evaluated the link between sarcopenia and overall survival and disease-free survival in individuals with colorectal cancer [3,18]. The association with poor survival was consistent across most studies. Therefore, a clinically significant association between sarcopenia and survival parameters was accepted.
Muscle mass volume calculation using cross-sectional CT images at the L3 level is frequently used to assess sarcopenia [3,19]. As a result of the interpretation of different body composition indices with different methods, sarcopenia and its effects on rectal cancer treatment have become increasingly complex [17]. In our study, the volumes of tissues were calculated in cubic centimeters using a radiotherapy treatment planning system that matches image tissue densities one-to-one with real human densities, providing a clear and quantitative assessment of the patient’s body composition. Reduced muscle mass, subcutaneous fat, and visceral fat tissue volumes were found to be an unfavorable prognostic factor for survival. By analyzing CT scans, these models automatically segment muscle mass regions and provide accurate and consistent measurements that are crucial for predicting treatment toxicity, quality of life, and survival outcomes. Integrating such automated assessments into clinical practice can support personalized treatment planning, improving oncological outcomes and radiotherapy side effects [20,21].
Muscle mass volume is quantified using the skeletal muscle index (SMI), expressed in square centimeters per square meter (cm2/m2). To calculate the SMI, the cross-sectional skeletal muscle mass area is measured in square centimeters (cm2) at specific anatomical locations at the L3 level. The calculation of muscle mass volume and assessment of sarcopenia in the context of radiotherapy can be easily and effectively accomplished through the use of automated contouring functions built into modern treatment planning systems. These systems use sophisticated algorithms that analyze imaging data to identify muscle mass tissues and provide an accurate measurement of muscle mass volume [20,21]. In our study, the volume was calculated by manually contouring the muscle mass, subcutaneous fat, and visceral fat area by a radiation oncologist and a radiologist. Patients with a high visceral fat volume were more frequently in the non-operated group, while survival was worse in patients with decreased visceral fat volume. A reduction in all three volumes resulted in worsened survival, as seen in cachexia. Obese patients are reported to have a higher risk of inadequate mesorectal excision and a possible poor oncologic outcome [22].
Several expert groups, including the European Working Group on Sarcopenia in Older People (EWGSOP), the Asian Working Group for Sarcopenia (AWGS), and Prado et al. have made substantial efforts to define and standardize the diagnosis of sarcopenia [13,23–25]. In these formulations, muscle strength parameters such as gait speed and handgrip strength have been incorporated to enhance diagnostic accuracy and to better reflect functional impairment associated with sarcopenia. The Skeletal Muscle Mass Index (SMI) is commonly calculated from computed tomography (CT)–derived cross-sectional areas (CSA) of skeletal muscles (cm2) normalized by height squared (m2) [26]. Low SMI has been defined according to various previously published cut-off values, including Carey’s criteria (<39 cm2/m2 for females and <50 cm2/m2 for males), Montano’s criteria (<42 cm2/m2 for females and <50 cm2/m2 for males), ESPEN criteria (<39 cm2/m2 for females and <55 cm2/m2 for males), and Prado’s criteria (<38.5 cm2/m2 for females and <52.4 cm2/m2 for males) [13,27]. In our study, however, tissue volumes were directly measured from the radiotherapy planning CT system, including skeletal muscle, subcutaneous fat, and visceral fat compartments (expressed in cm³). The volumetric approach we propose differs from the traditional SMI-based formulations. Consequently, the cut-off values obtained in our analysis are distinct from those reported in the literature. We suggest that these volumetric parameters provide a novel, practical, and reproducible method to predict sarcopenia using CT images routinely acquired for radiotherapy planning, without the need for additional imaging or measurements. This method may offer a feasible alternative for identifying sarcopenic patients in oncologic practice. Although the analysis was performed on a single axial CT slice, the radiotherapy treatment planning software automatically provides voxel-based quantitative data in cm³. Consequently, the reported metrics reflect estimated cross-sectional tissue volumes derived from CT density–based segmentation, rather than purely two-dimensional areas.
The association between the protein-albumin ratio and sarcopenia in patients with rectal cancer has been of interest in recent research due to the important effects of nutritional status and muscle mass on cancer outcomes. The MeSH (Medical Subject Headings) terms used in recent literature include ‘colorectal,’ ‘rectal,’ ‘myopenia,’ and ‘sarcopenia,’ among others [3]. In the context of rectal cancer, low serum albumin levels often indicate malnutrition, a common concern in cancer patients. The protein-to-albumin ratio considers the total protein level and albumin levels in the blood, providing a more comprehensive view of patients’ nutritional and physiological status. Studies suggest that a lower protein-albumin ratio may be associated with increased severity of sarcopenia, reflecting a decrease in muscle mass and a decline in overall health [28]. The protein albumin ratio can serve as a biomarker to assess the nutritional and inflammatory status of patients [29]. The neutrophil/leukocyte ratio may be a biomarker for complete response in patients with rectal cancer receiving neo-adjuvant therapy [30]. This may indicate a higher risk of sarcopenia, as a low serum albumin level is often associated with malnutrition and systemic inflammation, both of which may contribute to muscle mass loss [31]. In our study, low albumin levels before and after treatment were associated with both a worse regression response score and poor survival. Moreover, according to multivariate analysis, low albumin levels after treatment remained an important prognostic variable for overall survival. The loss of significance in multivariable models is likely attributable to collinearity among body composition parameters and the limited number of events, which attenuated the independent predictive effect of each variable.
The Fib-4 score is a noninvasive method that provides additional prognostic information for most types of cancer by calculating a formula based on age, transaminase levels, and platelet count to evaluate liver fibrosis[5]. The Fib-4 score, which predicts liver fibrosis using routine blood tests, has been found to be high in patients diagnosed with sarcopenia [32]. In colorectal cancer patients, a high Fib-4 score may reflect general health status and liver dysfunction and may be associated with worse clinical outcomes. It has been reported that a high Fib-4 score is associated with sarcopenia in rectal cancer patients, may complicate cancer management, and may worsen survival outcomes [29,32]. In our study, univariate Cox regression analysis results for the prediction of overall survival revealed that a high Fib-4 score is associated with poor survival.
Accurate diagnosis of sarcopenia is crucial for evaluating and managing patients with rectal cancer, especially those undergoing neo-adjuvant therapy. Early detection allows for timely interventions, including pre-rehabilitation programs that increase physical fitness, nutritional support that maximizes protein intake to maintain muscle mass, and tailored exercise regimens that enhance muscle mass strength [5,33,34]. Standardizing assessment methods for sarcopenia is vital to ensure consistent diagnosis in clinical settings [5]. Sarcopenia can be effectively assessed using biochemical tests, including serum albumin levels and the Fib-4 score, and by analyzing skeletal muscle mass area on CT scans [3,33–35]. Thanks to the early diagnosis of sarcopenia and the measures that can be taken, it can increase patient tolerance to treatment, reduce post-treatment complications, and improve oncological outcomes, including overall survival. In our analysis, we observed that biochemical indicators such as serum albumin and the Fib-4 score showed trends consistent with the sarcopenia-related parameters. Specifically, lower albumin levels and higher Fib-4 scores were both associated with poorer overall survival, similar to the prognostic impact of reduced muscle volume. These findings suggest a positive correlation between sarcopenia, hypoalbuminemia, and elevated Fib-4 scores, supporting the notion that impaired nutritional and hepatic status may coexist with or exacerbate muscle depletion.
This study has several limitations. First, its retrospective design and the non-homogeneous nature of the cohort limit the generalizability of the findings. As a single-center study involving patients predominantly from the Eastern Black Sea region of Turkey, dietary and demographic differences may restrict applicability to broader populations. Additionally, a considerable number of patients were excluded due to unavailable CT scans or because they received treatment on a different radiotherapy device, ensuring that only patients treated on the same device—using the same planning algorithms—were included. This resulted in a relatively small final sample size. Nonetheless, all eligible patients who met the predefined criteria were included, representing the entire accessible institutional cohort, and a post-hoc power analysis indicated sufficient statistical power to detect the observed associations. Despite these strengths, the findings—particularly regarding the relationship between radiologic and biochemical markers of sarcopenia and survival outcomes in rectal cancer—require confirmation in larger, prospective cohorts.
Conclusion
Accurate identification of sarcopenia is critical in rectal cancer patients receiving neoadjuvant therapy, as early recognition of muscle depletion enables timely supportive interventions. Incorporating muscle mass quantification into radiotherapy treatment planning systems (TPS) offers a simple, reproducible, and clinically accessible method that can be integrated into routine oncologic workflows to support personalized treatment planning. Modern TPS platforms use advanced voxel-based algorithms to automatically segment and quantify muscle tissue in cm³, ensuring precise and reproducible measurements. Although our analysis utilized a single axial CT slice, the system generates quantitative data based on CT density segmentation, reflecting estimated cross-sectional tissue volumes rather than purely two-dimensional areas.
In our study, reduced muscle mass, depletion of visceral and subcutaneous fat, low serum protein and albumin levels, and elevated Fib-4 scores were identified as significant predictors of poorer overall survival. These findings highlight the importance of monitoring both muscle status and liver fibrosis to optimize treatment strategies. Further investigation is warranted to elucidate their combined impact and to inform integrated management strategies aimed at improving oncologic outcomes.
Acknowledgements
The authors would like to thank the secretaries, nurses, and radiotherapy technicians of the radiation oncology department for their support in carrying out this study.
Funding Statement
This study has been supported by the Recep Tayyip Erdogan University Development Foundation (Grant number: 02025008022716).
Ethics approval and consent to participate
The study has received local ethics committee approval with the number E-40465587-050.01.04-787, dated 01.09.2023, and decision number 2023/188. Permission for research and data use was obtained from the Rize Governorship Provincial Health Directorate with the number E-64960800-799-217546661 and the date 09.06.2023, on the condition that the study is carried out by taking security measures regarding patient privacy and information security. The study used de-identified retrospective data, so it was exempt from the requirement for informed consent and was conducted according to the ethical principles outlined in the Declaration of Helsinki.
Consent for publication
Not applicable.
Disclosure statement
The authors declare no competing interests.
Data availability statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.