CT-based skeletal muscle loss predicts long term prognosis in patients with distal cholangiocarcinoma undergone pancreaticoduodenectomy
Cholangiocarcinoma (CCA) is a highly lethal and heterogeneous malignancy, according to anatomical position, it can be categorized into intrahepatic cholangiocarcinoma (iCCA), perihilar cholangiocarcinoma (pCCA) and the distal bile duct carcinoma (dCCA) , . Originating from the common bile duct, distal cholangiocarcinoma is a type of cancer found below the gallbladder and hepatic duct junction and above the ampulla of Vater, comprising 20-30% of all cholangiocarcinomas , . Patients with distal cholangiocarcinoma typically present with painless jaundice as a result of biliary obstruction, prompting them to seek medical evaluation. Laboratory tests frequently demonstrate elevated levels of bilirubin, alkaline phosphatase, G-glutamyl transpeptidase, and liver transaminase. EUS-FNA (Endoscopic ultrasonic fine needle aspiration) exhibits higher sensitivity in detecting dCCA . In terms of therapy, the complex tumor microenvironment and presence of genetic heterogeneity may pose an obstacle to the delivery of chemotherapy drugs to the malignancy . Surgical intervention, particularly through procedures like PD or Whipple surgery, is seen as the most effective treatment for early and mid-stage distal cholangiocarcinoma. However, the long-term survival outcomes following PD for distal cholangiocarcinoma remain suboptimal , . For patients with advanced cholangiocarcinoma who cannot undergo surgery or locoregional treatments, the main treatment strategy involves chemotherapy with gemcitabine and cisplatin . Studies have indicated that the combination regimen confers a modest survival advantage compared to gemcitabine monotherapy, with median survival durations of 8.1 months and 11.7 months, respectively . In recent years, immunotherapy has become a crucial treatment option for different advanced cancers, including both solid and non-solid tumors. Immunotherapy has demonstrated efficacy in tumors exhibiting mismatch repair defects (dMMR), high microsatellite instability (MSI), or high tumor mutation load (hTMB) , . Regrettably, cholangiocarcinoma, particularly advanced cholangiocarcinoma, frequently lacks these distinguishing features. Nevertheless, proof exists that CCA is immunogenic, which supports the argument for further study of immunotherapy, both as a standalone treatment and in combination with other therapies – . Malnutrition has been linked to decreased survival rates and diminished quality of life among patients with various types of cancer , . According to research, around 70% of patients with malignant tumors in hospitals are malnourished, and malnutrition is a factor in the deaths of approximately 20% of cancer patients , . Currently, sarcopenia, as assessed by SMI derived from radiological images, has emerged as a critical parameter in the clinical assessment of nutritional status and prognostication for patients with tumors. Sarcopenia is typified by a reduction in skeletal muscle mass and strength and is observed across various conditions including liver, kidney, inflammatory diseases, and malignancies . Sarcopenia serves as an adverse prognostic factor in cancer patients, whereas preservation of muscle mass may potentially extend survival in this population , . A meta-analysis by Liu et al. revealed that preoperative sarcopenia was independently associated with OS (HR = 1.53, P < 0.00001) and progression-free survival (HR = 1.55, P < 0.00001). Findings reveal that preoperative sarcopenia is linked to a higher rate of postoperative complications in esophageal cancer patients and is an independent factor affecting their long-term prognosis , . In terms of non-solid tumor, it has been shown that decreased SMI is an independent risk factor for OS and RFS in diffuse large B-cell lymphomas . Marasco et al. showed that low SMI or sarcopenia was positively associated with poor prognosis in patients with hepatocellular carcinoma who underwent partial hepatectomy or systemic therapy. According to these studies, low SMI or sarcopenia may affect the long-term prognosis of patients with malignant tumors following surgery or systemic treatment. However, there is a shortage of long-term data on the prognosis of DCC patients who have undergone PD with low SMI, dynamic skeletal muscle loss, and inflammatory status. The objective of this study was to evaluate the effects of pre- and post-surgical SMI alterations on the outcomes of distal cholangiocarcinoma patients undergoing PD. 

Research design This retrospective, single-centre study collected information of distal cholangiocarcinoma patients who underwent PD in our hospital from July 2015 to June 2023. Inclusion criteria include: (1) Distal cholangiocarcinoma patients undergoing PD, (2) Absence of neoadjuvant chemotherapy or radiotherapy before surgery, (3) No deaths occurring in the perioperative period. Following this, patients were excluded based on these criteria: (1) Presence of other tumors before surgery, (2) Receipt of neoadjuvant chemotherapy, (3) Lack of complete data during the perioperative period. Patient group selection is represented by a flowchart (Fig. ). Data collection and follow-up Demographic and clinical variables was retrospectively collected from the electronic medical record system. These indicators include: age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA), presence of hypertension and diabetes, preoperative venous routine blood sample test, coagulation function, tumor markers, biochemical examination, tumor size, differentiation, and adjuvant chemotherapy after surgery. NLR was defined as the ratios of neutrophil count to lymphocyte count. To calculate SII, multiply the platelet count by the neutrophil count and divide by the lymphocyte count; for SIRI, multiply the monocyte count by the neutrophil count and divide by the lymphocyte count (monocyte count * neutrophil count/lymphocyte count). Patients underwent monitoring after being discharged, with check-ups every three months until December 2023 or their death. The postoperative follow-up consisted of abdominal CT scans, blood routine tests, and biochemical evaluations. The initial detection of any new lesion suspected to be a recurrence following surgery was termed postoperative recurrence, encompassing areas such as the liver, pancreas, peritoneal lymph nodes, omentum, gastrointestinal tract, lung, bone, brain, etc., and the clinical diagnosis was made through the combination of CT and MRI, or pathological diagnosis was made when necessary. PD to death or last observation was defined as overall survival (OS), and PD to recurrence was defined as recurrence-free survival (RFS). Skeletal muscle loss (SML) calculation by image analysis The skeletal muscle area (SMA) of DCC before surgery based on CT cross-sectional images at the third lumbar vertebra level (L3) was measured using Slice-O-Matic V5.0 (TomoVision, Montreal, QC, Canada) software. Automatic segmentation of the skeletal muscles, including the psoas major, erector spinalis, quadratus psoas, transverse abdominis, and both internal and external obliques along with the rectus abdominis, was achieved using thresholds set between − 29 and 150 Hounsfield units (HU) (Fig. ) . Two radiologists, with 5 and 7 years of experience in imaging respectively, were invited to independently analyze the digital imaging data while blinded to patient information. The L3 skeletal muscle area (cm 2 ) was measured and normalized by height squared to calculate the skeletal muscle index (SMI) for each patient, expressed in cm 2 /m 2 . According to according to working group for creation of sarcopenia assessment criteria of Japan Society of Hepatology, sarcopenia was defined as SMI ≤ 42 cm 2 /m 2 in men and ≤ 38 cm 2 /m 2 in women . The follow-up plan for patients after discharge includes outpatient visits every 1 to 2 months, which involve CT imaging and serum biochemical tests. The SMI value will be obtained within 3–6 months post-surgery. The SML=[(postoperative SMI - preoperative SMI)/preoperative SMI × 100%]. All the information of patients was confidential, all patients signed informed consent, and this retrospective study was conducted in accordance with the Declaration of Helsinki. Statistical analysis The Kolmogorov-Smirnov test was employed to assess the distribution of continuous data. Continuous variables following a normal distribution were expressed as mean ± standard deviation and analyzed using the independent samples t-test. For non-normally distributed data, variables were presented as median (interquartile range) and compared using the Mann-Whitney U test. The categorical variables were analyzed as percentages and compared using Pearson’s chi-square test or Fisher’s exact test. The optimal cut-off values for SML and inflammation biomarkers according to the Youden index related to the endpoints such as OS and RFS was determined through receiver operating characteristics (ROC) curve analysis using R software. Univariate and multivariate Cox regression models were utilized to identify independent prognostic factors associated with OS and RFS. Variables with a significance level of P < 0.05 in the univariate Cox regression analysis were included in the multivariate Cox regression model. Survival differences were evaluated using Kaplan-Meier curves and compared with log-rank tests. All analyses were conducted using R (version 4.1.2), and P -value < 0.05 is considered statistically significant. 

This retrospective, single-centre study collected information of distal cholangiocarcinoma patients who underwent PD in our hospital from July 2015 to June 2023. Inclusion criteria include: (1) Distal cholangiocarcinoma patients undergoing PD, (2) Absence of neoadjuvant chemotherapy or radiotherapy before surgery, (3) No deaths occurring in the perioperative period. Following this, patients were excluded based on these criteria: (1) Presence of other tumors before surgery, (2) Receipt of neoadjuvant chemotherapy, (3) Lack of complete data during the perioperative period. Patient group selection is represented by a flowchart (Fig. ). 

Demographic and clinical variables was retrospectively collected from the electronic medical record system. These indicators include: age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA), presence of hypertension and diabetes, preoperative venous routine blood sample test, coagulation function, tumor markers, biochemical examination, tumor size, differentiation, and adjuvant chemotherapy after surgery. NLR was defined as the ratios of neutrophil count to lymphocyte count. To calculate SII, multiply the platelet count by the neutrophil count and divide by the lymphocyte count; for SIRI, multiply the monocyte count by the neutrophil count and divide by the lymphocyte count (monocyte count * neutrophil count/lymphocyte count). Patients underwent monitoring after being discharged, with check-ups every three months until December 2023 or their death. The postoperative follow-up consisted of abdominal CT scans, blood routine tests, and biochemical evaluations. The initial detection of any new lesion suspected to be a recurrence following surgery was termed postoperative recurrence, encompassing areas such as the liver, pancreas, peritoneal lymph nodes, omentum, gastrointestinal tract, lung, bone, brain, etc., and the clinical diagnosis was made through the combination of CT and MRI, or pathological diagnosis was made when necessary. PD to death or last observation was defined as overall survival (OS), and PD to recurrence was defined as recurrence-free survival (RFS). 

The skeletal muscle area (SMA) of DCC before surgery based on CT cross-sectional images at the third lumbar vertebra level (L3) was measured using Slice-O-Matic V5.0 (TomoVision, Montreal, QC, Canada) software. Automatic segmentation of the skeletal muscles, including the psoas major, erector spinalis, quadratus psoas, transverse abdominis, and both internal and external obliques along with the rectus abdominis, was achieved using thresholds set between − 29 and 150 Hounsfield units (HU) (Fig. ) . Two radiologists, with 5 and 7 years of experience in imaging respectively, were invited to independently analyze the digital imaging data while blinded to patient information. The L3 skeletal muscle area (cm 2 ) was measured and normalized by height squared to calculate the skeletal muscle index (SMI) for each patient, expressed in cm 2 /m 2 . According to according to working group for creation of sarcopenia assessment criteria of Japan Society of Hepatology, sarcopenia was defined as SMI ≤ 42 cm 2 /m 2 in men and ≤ 38 cm 2 /m 2 in women . The follow-up plan for patients after discharge includes outpatient visits every 1 to 2 months, which involve CT imaging and serum biochemical tests. The SMI value will be obtained within 3–6 months post-surgery. The SML=[(postoperative SMI - preoperative SMI)/preoperative SMI × 100%]. All the information of patients was confidential, all patients signed informed consent, and this retrospective study was conducted in accordance with the Declaration of Helsinki. 

The Kolmogorov-Smirnov test was employed to assess the distribution of continuous data. Continuous variables following a normal distribution were expressed as mean ± standard deviation and analyzed using the independent samples t-test. For non-normally distributed data, variables were presented as median (interquartile range) and compared using the Mann-Whitney U test. The categorical variables were analyzed as percentages and compared using Pearson’s chi-square test or Fisher’s exact test. The optimal cut-off values for SML and inflammation biomarkers according to the Youden index related to the endpoints such as OS and RFS was determined through receiver operating characteristics (ROC) curve analysis using R software. Univariate and multivariate Cox regression models were utilized to identify independent prognostic factors associated with OS and RFS. Variables with a significance level of P < 0.05 in the univariate Cox regression analysis were included in the multivariate Cox regression model. Survival differences were evaluated using Kaplan-Meier curves and compared with log-rank tests. All analyses were conducted using R (version 4.1.2), and P -value < 0.05 is considered statistically significant. 

Patient characteristics A total of 112 patients meeting the inclusion criteria were enrolled in the study and categorized into low-SMI and high-SMI groups, comprising 55 and 57 patients, respectively (Table ). The mean age of the cohort was 64.89 ± 10.74 years, with 70 males (62%) and 42 females (38%). Baseline characteristics, including age, gender, BMI, ASA classification, and the prevalence of hypertension and diabetes, showed no significant differences between the low-SMI and high-SMI groups. Similarly, no statistical differences were observed for preoperative leukocyte, neutrophil, lymphocyte, monocyte, and platelet counts; prothrombin time (PT); international normalized ratio (INR); albumin; creatinine; intraoperative blood loss; adjuvant chemotherapy; NLR; SII; or SIRI ( P > 0.05 for all comparisons). Notably, CA199 (median 61.92 versus 43.91, P = 0.041), total bilirubin (median 184.60 versus 107.00, P = 0.006), SML (median − 2.85 versus − 1.55, P < 0.001) and SML% (mean − 7.96% versus − 3%, P < 0.001) were significantly elevated in low SMI group, and the difference was remarkable. The mean SMI were 36.9 ± 4.52 and 45.25 ± 5.25 in low-SMI group and high-SMI group, respectively. In the pathological information, there was no discrepancy in tumor size and vascular invasion between the two groups ( P > 0.05). Notably, the proportion of lymph node metastasis was significantly higher in the low-SMI group compared to the high-SMI group (60% versus 25%, P = 0.001). The proportion of poor differentiation was higher in low SMI group, compared with high SMI group (45% versus 25%, P = 0.002). Concerning tumor recurrence, 49 cases (44%) recurred, with 25 cases and 24 cases in high SMI and low SMI groups, respectively ( P = 0.9) (Table ). Among the patients, there were 39 cases of liver metastasis, 15 cases of abdominal lymph node metastasis, 4 cases of adrenal gland metastasis, 6 cases involving the brain, lung, and bone, 5 cases affecting the pancreas, colon, and stomach, and 7 cases with metastases in other locations. Notably, some patients with distal cholangiocarcinoma (DCC) presented with multiple simultaneous metastatic sites. The impact of SML and inflammatory markers on OS and RFS The changes in SMI were evaluated in patients with DCC before surgery and 3–6 months postoperatively. Overall, SMI exhibited a significant decline post-surgery compared to preoperative levels (39.02 ± 7.01 versus 41.27 ± 5.94, P < 0.001). This downward trend was observed in both the low-SMI group (34.15 ± 4.41 versus 37.39 ± 4.11, P < 0.001) and the high-SMI group (43.72 ± 5.74 versus 45.02 ± 4.94, P < 0.001). These findings are visually represented in the box plot (Supplementary Fig. ). Additionally, receiver operating characteristic (ROC) curves were utilized to investigate the relationship between SML, preoperative inflammatory biomarkers, and postoperative long-term outcomes following PD surgery. The R software was utilized to determine the optimal cut-off values for the prognostic markers SML, NLR, SII, and SIRI in patients with DCC following PD surgery. The cut-off values for OS established for these markers were − 4.01%, 2.17, 505.05 and 1.48, respectively. Patients were stratified into two groups based on these cut-off values: a high SML and inflammatory markers group, and a low SML and inflammatory markers group. Kaplan-Meier curve analysis with log-rank tests was performed to evaluate overall survival (OS) differences among groups stratified by skeletal muscle loss (SML) and inflammatory biomarkers. Patients in the high SML group (SML ≤ -4.01%) demonstrated significantly poorer prognoses compared to those in the low SML group (SML > -4.01%), with P < 0.001 (Fig. A). Similarly, analysis of inflammatory biomarkers revealed that patients in the high NLR ( P = 0.017), SII ( P = 0.012), and SIRI ( P = 0.002) groups had markedly worse OS compared to those with lower values of these markers (Fig. B–D). Similarly, R software was used to determine the optimal cut-off value of prognostic indicators SML, NLR, SII, and SIRI for RFS after PD in DCC patients. The cut-off values for RFS established for these markers were − 5.99%, 2.26, 469.01 and 1.35, respectively. The study utilized Kaplan-Meier curves with log rank t-tests to assess prognostic differences among various groups of related prognostic markers. Results indicated that the low SML group exhibited significantly improved recurrence-free survival compared to the high SML group ( P < 0.001) (Fig. A). Conversely, no statistically significant difference in RFS was observed between the high NLR and low NLR groups ( P = 0.078) (Fig. B). A similar trend was noted in the comparison between the high SII and low SII groups ( P = 0.084) (Fig. C). However, the low SIRI group exhibited significantly better RFS compared to the high SIRI group ( P = 0.028) (Fig. D). Clinical characteristics analysis of patients in different SML groups As shown in Table , the 112 DCC patients were divided into low SML ( N = 46) and high SML ( N = 66) groups based on the cut-off value of SML for overall survival (OS). Notably, the high SML group exhibited significantly higher levels of leukocytes (mean 7.26 vs. 6.22, P = 0.004), neutrophils (mean 4.94 vs. 4.20, P = 0.004), monocytes (mean 0.51 vs. 0.42, P = 0.015), SII (median 837.28 vs. 775.35, P = 0.042), and SIRI (median 1.70 vs. 1.38, P < 0.001) compared to the low SML group. In terms of clinicopathological features, compared with low SML group, high SML group had a higher proportion of poor differentiation (45% versus 20%, P = 0.005) and lymph metastasis (55% versus 24%, P = 0.001) (Table ). Univariate and multivariate Cox regression analysis for OS Univariate and multivariate Cox regression analyses were performed to identify factors influencing overall survival (OS) following pancreaticoduodenectomy (PD) in patients with DCC. Univariate Cox regression analysis revealed that the following factors were associated with poorer OS: CA199 > 37 (HR = 1.74, 95% CI, 1.02–2.95, P = 0.041), tumor size > 2 cm (HR = 1.89, 95% CI, 1.15–3.09, P = 0.012), poor differentiation (HR = 3.04, 95% CI, 1.84–5.03, P < 0.001), vascular invasion (invasion versus non-invasion, HR = 2.0, 95% CI, 1.1–3.6, P < 0.001), lymph node metastasis (HR = 4.23, 95% CI, 2.51–7.12, P < 0.001), and SML ≤ -4.01% (HR = 4.53, 95% CI, 2.56–8.04, P < 0.001) (Table ). These factors were identified as independent risk factors affecting OS in DCC patients. Variables with a P value < 0.05 in the univariate Cox regression analysis were included in the multivariate Cox regression analysis. Tumor diameter (> 2 cm) (HR = 1.90, (95% CI, 1.12–3.20), P = 0.017), poor differentiation (HR = 2.80, (95% CI, 1.57–4.90), P < 0.001), vascular invasion (HR = 2.10, (95% CI, 1.17–3.70), P = 0.013), lymph metastasis (HR = 4.00, (95% CI, 2.27–7.00), P < 0.001), and high SML (≤-4.01%) (HR = 3.60, (95% CI, 1.96–6.80), P < 0.001) are identified as independent risk factors impacting OS in multivariate Cox regression analysis (Table ). Univariate and multivariate Cox regression analysis for RFS Additionally, univariate and multivariate Cox regression analyses were performed to identify significant factors influencing RFS following PD for DCC. ROC curve analysis determined a critical threshold of -5.99% for SML as a predictor of RFS. Based on this cut-off value, patients were stratified into two groups. Univariate Cox regression analysis identified CA199 > 37 (HR = 2.25, (95% CI, 1.19–4.24), P = 0.013), poor differentiation (HR = 2.25, (95% CI, 1.26–4.02), P = 0.006), lymph metastasis (HR = 3.32, (95% CI, 1.81–6.08), P < 0.001) and SML (≤-5.99%) (HR = 4.50, (95% CI, 2.39–8.47), P < 0.001) as independent risk factors associated with RFS (Table ). Multivariate Cox regression analysis showed that poor differentiation (HR = 1.90, (95% CI, 1.02–3.50), P = 0.043), lymph metastasis (HR = 2.6, (95% CI, 1.39–5.00), P = 0.003) and high SML (≤-5.99%) (HR = 3.80, (95% CI, 1.97–7.50), P < 0.001) were identified as independent risk factors for recurrence free survival (Table ). Prediction for long-term survival by SML combined with BMI and inflammatory biomarker Based on BMI and SML value analysis, patients were grouped into two categories: those with high SML & BMI > 25, and those in other groups. Kaplan-Meier curve showed that compared with the high SML & BMI > 25 group, the OS of the other group was prolonged, but there was no significant difference ( P = 0.08) (Fig. A). Then we investigate the predictive value of combining SML and SIRI in determining OS following PD in DCC. There was significant discrepancy in the OS of Kaplan-Meier curve among the four subgroups. Among the groups categorized as high-SML/low-SIRI (H-SML & L-SIRI), high-SML /high-SIRI (H-SML & H-SIRI), low-SML/high-SIRI (L-SML & H-SIRI) and low-SML/low-SIRI (L-SML & L-SIRI), the group with SML-low/SIRI-low exhibited the longest OS. This was followed by the SML-low/SIRI-high, which had a median survival time of 71 months, then the SML-high/SIRI-low group with a median survival time of 20 months, and finally the low SML-high/SIRI-high group with a median survival time of 15 months ( P < 0.001), respectively (Fig. B). Skeletal muscle loss combined clinical characteristics for predicting long-term survival The ROC curve analyses demonstrated the predictive abilities of SML and clinical characteristics combinations for predicting long-term survival. The AUC of SML combined with clinical characteristics was 0.863 (95% CI: 0.760– 0.966), while the AUC of clinical characteristics alone was 0.801 (95% CI 0.690–0.911) for predicting OS (Fig. A). In terms of predicting RFS, the AUC of SML combined with clinical characteristics was 0.889 (0.778–0.999), and that of single clinical features was 0.755 (95% CI 0.623–0.886) (Fig. B). 

A total of 112 patients meeting the inclusion criteria were enrolled in the study and categorized into low-SMI and high-SMI groups, comprising 55 and 57 patients, respectively (Table ). The mean age of the cohort was 64.89 ± 10.74 years, with 70 males (62%) and 42 females (38%). Baseline characteristics, including age, gender, BMI, ASA classification, and the prevalence of hypertension and diabetes, showed no significant differences between the low-SMI and high-SMI groups. Similarly, no statistical differences were observed for preoperative leukocyte, neutrophil, lymphocyte, monocyte, and platelet counts; prothrombin time (PT); international normalized ratio (INR); albumin; creatinine; intraoperative blood loss; adjuvant chemotherapy; NLR; SII; or SIRI ( P > 0.05 for all comparisons). Notably, CA199 (median 61.92 versus 43.91, P = 0.041), total bilirubin (median 184.60 versus 107.00, P = 0.006), SML (median − 2.85 versus − 1.55, P < 0.001) and SML% (mean − 7.96% versus − 3%, P < 0.001) were significantly elevated in low SMI group, and the difference was remarkable. The mean SMI were 36.9 ± 4.52 and 45.25 ± 5.25 in low-SMI group and high-SMI group, respectively. In the pathological information, there was no discrepancy in tumor size and vascular invasion between the two groups ( P > 0.05). Notably, the proportion of lymph node metastasis was significantly higher in the low-SMI group compared to the high-SMI group (60% versus 25%, P = 0.001). The proportion of poor differentiation was higher in low SMI group, compared with high SMI group (45% versus 25%, P = 0.002). Concerning tumor recurrence, 49 cases (44%) recurred, with 25 cases and 24 cases in high SMI and low SMI groups, respectively ( P = 0.9) (Table ). Among the patients, there were 39 cases of liver metastasis, 15 cases of abdominal lymph node metastasis, 4 cases of adrenal gland metastasis, 6 cases involving the brain, lung, and bone, 5 cases affecting the pancreas, colon, and stomach, and 7 cases with metastases in other locations. Notably, some patients with distal cholangiocarcinoma (DCC) presented with multiple simultaneous metastatic sites. 

The changes in SMI were evaluated in patients with DCC before surgery and 3–6 months postoperatively. Overall, SMI exhibited a significant decline post-surgery compared to preoperative levels (39.02 ± 7.01 versus 41.27 ± 5.94, P < 0.001). This downward trend was observed in both the low-SMI group (34.15 ± 4.41 versus 37.39 ± 4.11, P < 0.001) and the high-SMI group (43.72 ± 5.74 versus 45.02 ± 4.94, P < 0.001). These findings are visually represented in the box plot (Supplementary Fig. ). Additionally, receiver operating characteristic (ROC) curves were utilized to investigate the relationship between SML, preoperative inflammatory biomarkers, and postoperative long-term outcomes following PD surgery. The R software was utilized to determine the optimal cut-off values for the prognostic markers SML, NLR, SII, and SIRI in patients with DCC following PD surgery. The cut-off values for OS established for these markers were − 4.01%, 2.17, 505.05 and 1.48, respectively. Patients were stratified into two groups based on these cut-off values: a high SML and inflammatory markers group, and a low SML and inflammatory markers group. Kaplan-Meier curve analysis with log-rank tests was performed to evaluate overall survival (OS) differences among groups stratified by skeletal muscle loss (SML) and inflammatory biomarkers. Patients in the high SML group (SML ≤ -4.01%) demonstrated significantly poorer prognoses compared to those in the low SML group (SML > -4.01%), with P < 0.001 (Fig. A). Similarly, analysis of inflammatory biomarkers revealed that patients in the high NLR ( P = 0.017), SII ( P = 0.012), and SIRI ( P = 0.002) groups had markedly worse OS compared to those with lower values of these markers (Fig. B–D). Similarly, R software was used to determine the optimal cut-off value of prognostic indicators SML, NLR, SII, and SIRI for RFS after PD in DCC patients. The cut-off values for RFS established for these markers were − 5.99%, 2.26, 469.01 and 1.35, respectively. The study utilized Kaplan-Meier curves with log rank t-tests to assess prognostic differences among various groups of related prognostic markers. Results indicated that the low SML group exhibited significantly improved recurrence-free survival compared to the high SML group ( P < 0.001) (Fig. A). Conversely, no statistically significant difference in RFS was observed between the high NLR and low NLR groups ( P = 0.078) (Fig. B). A similar trend was noted in the comparison between the high SII and low SII groups ( P = 0.084) (Fig. C). However, the low SIRI group exhibited significantly better RFS compared to the high SIRI group ( P = 0.028) (Fig. D). 

As shown in Table , the 112 DCC patients were divided into low SML ( N = 46) and high SML ( N = 66) groups based on the cut-off value of SML for overall survival (OS). Notably, the high SML group exhibited significantly higher levels of leukocytes (mean 7.26 vs. 6.22, P = 0.004), neutrophils (mean 4.94 vs. 4.20, P = 0.004), monocytes (mean 0.51 vs. 0.42, P = 0.015), SII (median 837.28 vs. 775.35, P = 0.042), and SIRI (median 1.70 vs. 1.38, P < 0.001) compared to the low SML group. In terms of clinicopathological features, compared with low SML group, high SML group had a higher proportion of poor differentiation (45% versus 20%, P = 0.005) and lymph metastasis (55% versus 24%, P = 0.001) (Table ). 

Univariate and multivariate Cox regression analyses were performed to identify factors influencing overall survival (OS) following pancreaticoduodenectomy (PD) in patients with DCC. Univariate Cox regression analysis revealed that the following factors were associated with poorer OS: CA199 > 37 (HR = 1.74, 95% CI, 1.02–2.95, P = 0.041), tumor size > 2 cm (HR = 1.89, 95% CI, 1.15–3.09, P = 0.012), poor differentiation (HR = 3.04, 95% CI, 1.84–5.03, P < 0.001), vascular invasion (invasion versus non-invasion, HR = 2.0, 95% CI, 1.1–3.6, P < 0.001), lymph node metastasis (HR = 4.23, 95% CI, 2.51–7.12, P < 0.001), and SML ≤ -4.01% (HR = 4.53, 95% CI, 2.56–8.04, P < 0.001) (Table ). These factors were identified as independent risk factors affecting OS in DCC patients. Variables with a P value < 0.05 in the univariate Cox regression analysis were included in the multivariate Cox regression analysis. Tumor diameter (> 2 cm) (HR = 1.90, (95% CI, 1.12–3.20), P = 0.017), poor differentiation (HR = 2.80, (95% CI, 1.57–4.90), P < 0.001), vascular invasion (HR = 2.10, (95% CI, 1.17–3.70), P = 0.013), lymph metastasis (HR = 4.00, (95% CI, 2.27–7.00), P < 0.001), and high SML (≤-4.01%) (HR = 3.60, (95% CI, 1.96–6.80), P < 0.001) are identified as independent risk factors impacting OS in multivariate Cox regression analysis (Table ). 

Additionally, univariate and multivariate Cox regression analyses were performed to identify significant factors influencing RFS following PD for DCC. ROC curve analysis determined a critical threshold of -5.99% for SML as a predictor of RFS. Based on this cut-off value, patients were stratified into two groups. Univariate Cox regression analysis identified CA199 > 37 (HR = 2.25, (95% CI, 1.19–4.24), P = 0.013), poor differentiation (HR = 2.25, (95% CI, 1.26–4.02), P = 0.006), lymph metastasis (HR = 3.32, (95% CI, 1.81–6.08), P < 0.001) and SML (≤-5.99%) (HR = 4.50, (95% CI, 2.39–8.47), P < 0.001) as independent risk factors associated with RFS (Table ). Multivariate Cox regression analysis showed that poor differentiation (HR = 1.90, (95% CI, 1.02–3.50), P = 0.043), lymph metastasis (HR = 2.6, (95% CI, 1.39–5.00), P = 0.003) and high SML (≤-5.99%) (HR = 3.80, (95% CI, 1.97–7.50), P < 0.001) were identified as independent risk factors for recurrence free survival (Table ). 

Based on BMI and SML value analysis, patients were grouped into two categories: those with high SML & BMI > 25, and those in other groups. Kaplan-Meier curve showed that compared with the high SML & BMI > 25 group, the OS of the other group was prolonged, but there was no significant difference ( P = 0.08) (Fig. A). Then we investigate the predictive value of combining SML and SIRI in determining OS following PD in DCC. There was significant discrepancy in the OS of Kaplan-Meier curve among the four subgroups. Among the groups categorized as high-SML/low-SIRI (H-SML & L-SIRI), high-SML /high-SIRI (H-SML & H-SIRI), low-SML/high-SIRI (L-SML & H-SIRI) and low-SML/low-SIRI (L-SML & L-SIRI), the group with SML-low/SIRI-low exhibited the longest OS. This was followed by the SML-low/SIRI-high, which had a median survival time of 71 months, then the SML-high/SIRI-low group with a median survival time of 20 months, and finally the low SML-high/SIRI-high group with a median survival time of 15 months ( P < 0.001), respectively (Fig. B). 

The ROC curve analyses demonstrated the predictive abilities of SML and clinical characteristics combinations for predicting long-term survival. The AUC of SML combined with clinical characteristics was 0.863 (95% CI: 0.760– 0.966), while the AUC of clinical characteristics alone was 0.801 (95% CI 0.690–0.911) for predicting OS (Fig. A). In terms of predicting RFS, the AUC of SML combined with clinical characteristics was 0.889 (0.778–0.999), and that of single clinical features was 0.755 (95% CI 0.623–0.886) (Fig. B). 

DCC, a subtype of cholangiocarcinoma, originates in the area between the cystic duct and the ampulla of Vater. While PD is the treatment of choice for DCC, the long-term survival results are not optimal , . Factors such as lymph node metastasis, resection margin status, lymphatic and vascular invasion, and severe postoperative complications have been identified as predictors of poor prognosis in DCC patients undergoing PD , . Unfortunately, some of these predictors are related to the surgeon, while some can only rely on postoperative pathology, which is not conducive to the implementation of individualized preoperative treatment strategies. Therefore, it is necessary to develop imaging information and blood and biochemical indicators to evaluate the prognosis of DCC after PD. Malnutrition has been linked to a poor prognosis in various types of tumors and is often a direct cause of death in patients with advanced-stage tumors. The SMI, derived from CT imaging data, is considered a more accurate indicator of nutritional status. The use of SMI to evaluate sarcopenia for predicting cancer patients’ long-term prognosis is gaining attention. In our retrospective study, we examined 112 patients with DCC who underwent PD. Of these patients, 55 (49%) were classified as low SMI and 57 (51%) as high SMI. Our findings suggest that dynamic changes in skeletal muscle index (i.e. SML) are effective predictors of OS and RFS. Sarcopenia, assessed by the SMI at the third lumbar vertebra level on CT images, has been recognized as a significant prognostic factor in various malignancies. The use of CT imaging to quantify fat and muscle areas for prognostic assessment has emerged as a straightforward and accurate method. Jang et al.’s . suggests that while using SMI as a static measurement is valid, applying it as a dynamic variable may be more appropriate for predicting survival in cancer patients. This is because body composition may change following cancer treatment, leading to subsequent alterations in SMI. Therefore, in our study, we measured the SMI of patients with DCC measured by CT imaging data before PD and the SMI 3–6 months after PD, and predicted the long-term OS and RFS through the SML. Multiple studies have demonstrated that among patients with hepatocellular carcinoma undergone hepatectomy, the prevalence of preoperative low SMI ranges from 14 to 40.3% – . This higher prevalence may be attributed to variations in tumor types and the presence of jaundice in certain patients, which can impact liver metabolism and the digestion and absorption functions of related organs, leading to persistent malnutrition. Within the low SMI group, levels of total bilirubin and CA199 were significantly elevated compared to those in the high SMI group. This shows that in the low SMI group, the proportion of patients with obstructive jaundice is slightly higher, and the continuously rising jaundice further affects liver function, even induces cholangitis and liver dysfunction, which will also promote the increase of CA199. Our study aimed to assess changes in skeletal muscle and their impact on OS and RFS in patients with DCC. The optimal cut-off values for SML in relation to OS and RFS were determined to be -4.01% and − 5.99%, respectively. Kaplan-Meier analysis revealed a significant reduction in OS among patients with SML ≤ -4.01% compared to those with SML > -4.01%. Multivariate Cox regression analysis identified tumor diameter ≥ 2 cm, poor differentiation, vascular invasion, lymph node metastasis, and SML ≤ -4.01% as independent risk factors for decreased OS in DCC. In the analysis of factors affecting RFS after PD in DCC, poor differentiation, lymph metastasis and SML≤-5.99% were independent risk factors. Kuwada et al. demonstrated that SML serves as an independent risk prognostic factor impacting the OS of gastric cancer patients who have undergone gastrectomy (hazard ratio [HR] = 2.65; 95% confidence interval [CI] = 1.68–4.20, P < 0.0001). Furthermore, the 5-year RFS rate was significantly lower in the SML group compared to the non-SML group (73% versus 86%, P = 0.006 and 56% versus 87%, P < 0.0001). In contrast to previous research, our study focuses on the specific reduction in SML value that influences the prognosis of DCC in the period before and after PD. By examining the specific SML value, patients with DCC can be categorized into various risk groups, allowing for personalized and preventive treatment approaches to improve outcomes. Yang et al.’s research demonstrated that in patients receiving adjuvant transarterial chemoembolization (TACE) following hepatectomy, SML<-2.42% serves as a significant predictor of decreased cause-specific survival (CSS). Our own investigation indicates that SML≤-4.01% and SML≤-5.99% are the most reliable indicators of shortened OS and RFS. The difference in specific values of SML may be due to the difference in tumor types, and the effect of PD on the recovery of nutritional status and physical function of DCC patients lasts longer. Currently, SML is recognized as a prognostic indicator for individuals with malignancies, specifically cancer cachexia, which has been shown to substantially elevate mortality rates in cancer patients . SML has been observed in patients with various types of tumors post-treatment, such as gastric cancer, liver cancer, non-small cell lung cancer, and pancreatic cancer – . Our findings align with previous studies, as evidenced by a mean SML of -5.81% in 112 DCC patients. PD entails the resection of multiple digestive organs and complex reconstruction of the digestive tract, which significantly impairs normal physiological functions. Partial pancreatic resection reduces pancreatic enzyme secretion, resulting in impaired digestion and absorption of proteins and fats. This disruption ultimately hinders patients’ long-term nutritional status and immune recovery. Additionally, complex reconstruction of the digestive tract postoperatively can result in gastrointestinal dysfunction, further impacting the nutritional and immune status of patients following surgery. This may be an important reason for the loss of skeletal muscle in most patients with DCC after PD. Therefore, early assessment of nutritional status in patients with DCC undergoing PD, coupled with timely nutritional intervention for those experiencing skeletal muscle loss, may contribute to improved overall nutritional outcomes. Inflammatory biomarkers, such as NLR, SII and SIRI, are closely correlated to the prognosis of a variety of tumors, which has been confirmed in many studies – . There is a correlation between nutritional status and immune inflammatory response. Nutritional deficiency triggers the release of immune cells and inflammatory mediators, impairing patients’ immune surveillance and response functions. Our findings indicate that combining SML with the SIRI can improve risk stratification for patients with DCC after PD. This information is crucial for tailoring nutritional interventions for individual DCC patients, allowing for the development of personalized follow-up strategies based on specific risk profiles. Previous studies have indicated that excess body weight, specifically overweight or obesity, is a significant risk factor for the development of various malignancies and chronic conditions, ultimately impacting the long-term prognosis . Individuals with cancer who exhibit reduced skeletal muscle mass and increased BMI are linked to less favorable long-term prognoses compared to those with greater muscle mass and lower BMI . Baracos’s research showed that obese patients with sarcopenia exhibit diminished functional status and short-term and long-term prognosis than obese patients without sarcopenia. During our research, we categorized patients with DCC post-PD into two groups (BMI > 25 kg/m 2 & high SML group and other groups). The overall survival for the other groups was somewhat longer than for the group with a BMI over 25 kg/m 2 and high SML, but this difference was not statistically significant. It may be that we chose the dynamic combination of SMI, and there were few samples with high SML & BMI > 25 kg/m 2 . Therefore, a large sample size multicenter study should explore the value of SML combined with obesity in the long-term prognosis after surgery in malignancy. An increasing body of research is investigating the impact of sarcopenia on tumor prognosis, alongside the development of associated clinical recommendations. However, limited attention has been directed toward the temporal variations in SMI during the perioperative period of malignancy, particularly pre- and post-surgery. Furthermore, the formulation of targeted intervention strategies based on these dynamic changes remains underexplored, presenting a critical gap in the current evidence base to guide clinical practice. CT is a reliable and efficient tool for evaluating muscle quality, offering numerous benefits. By utilizing CT to quantitatively assess SMI and SML in patients with DCC both pre- and post-operatively, one can effectively analyze long-term prognosis and risk stratification. Our study reveals a marked decline in skeletal muscle mass among patients with DCC undergoing surgery, which adversely affects OS and RFS. To counteract this trend, timely nutritional support interventions should be initiated postoperatively upon identifying SML. Additionally, the assessment of SML may assist in stratifying the prognostic risk of patients with DCC undergoing surgical intervention, as well as identifying individuals who may benefit from early nutritional and exercise interventions to enhance food intake, skeletal muscle quality and function, and mitigate preoperative inflammatory responses. The limitations of our study include its retrospective nature and being conducted at a single center, emphasizing the importance of future multi-center prospective studies for validation. Secondly, the SMI time interval measured by imaging in DCC patients with after PD is not consistent, which may have a certain degree of bias. Finally, although we dynamically measure the changes of SML, we also need to further measure the impact of dynamic changes of inflammatory biomarkers on DCC prognosis. 

SML were identified as reliable independent prognostic factors for long-term overall survival and recurrence-free survival in DCC patients undergone PD. The combination of SML and clinical features has superior performance in predicting the long-term prognosis of DCC. However, multicenter and extensive sample data are still needed to accurately evaluate the prognosis of patients with DCC, so as to provide more theoretical support for precise treatment. 

Below is the link to the electronic supplementary material. Supplementary Material 1 

