Implication of Skeletal Muscle Loss in the Prognosis of Patients with Pancreatic Ductal Adenocarcinoma Receiving Chemotherapy

Computed Tomography-Based Sarcopenia and Pancreatic Cancer Survival-A Comprehensive Meta-Analysis Exploring the Influence of Definition Criteria, Prevalence, and Treatment Intention

Background: Sarcopenia has been associated with poor outcomes in pancreatic cancer (PC). However, published results are heterogeneous in terms of study design, oncological outcomes, and sarcopenia measurements. This meta-analysis aims to evaluate the impact of computed tomography (CT)-based sarcopenia on overall survival (OS) and progression-free survival (PFS) in patients with PC, considering potential confounders such as the CT-based method and thresholds used to define sarcopenia, as well as treatment intention. Methods: We systematically searched databases for observational studies reporting hazard ratios (HRs) for OS and PFS in PC patients stratified by CT-based sarcopenia status. Random-effects models were used to calculate pooled crude and adjusted HRs (cHRs and aHRs, respectively), with subgroup analyses based on sarcopenia measurement methods, cutoff values, sarcopenia prevalence, and treatment intention. Heterogeneity was assessed using the I2 and τ2 statistics, and publication bias was evaluated using funnel plots and Egger's test. Results: Data from 48 studies were included. Sarcopenia was significantly associated with worse OS (pooled cHR = 1.58, 95% CI: 1.38-1.82; pooled aHR = 1.39, 95% CI: 1.16-1.66) and worse PFS (pooled cHR = 1.55, 95% CI: 1.29-1.86; pooled aHR = 1.31, 95% CI: 1.11-1.55). Subgroup analyses revealed significantly different, stronger associations in studies using stricter sarcopenia cutoffs (<50 cm2/m2 for males) and in patients undergoing curative treatments. Heterogeneity was substantial across analyses (I2 > 67%), but with generally low τ2 values (0.01-0.25). Egger's test indicated potential publication bias for OS (p < 0.001), but no significant bias was observed for PFS (p = 0.576). Conclusions: Sarcopenia determined by CT is an independent predictor of poor OS and PFS in PC, but this association varies depending on the cutoff used for its definition as well as on the treatment intention. Therefore, its routine assessment in clinical practice could provide valuable prognostic information, but future research should focus on standardizing sarcopenia assessment methods.

Sarcopenia Is a Prognostic Factor of Adverse Effects and Mortality in Patients With Tumour: A Systematic Review and Meta-Analysis

BACKGROUND

The relationship between sarcopenia and the prognosis of patients with tumours who received radio- and/or chemotherapy still needs to be determined. In this study, we aim to investigate the relationship between sarcopenia and adverse effects and mortality in patients with tumours that received radio- and/or chemotherapy, stratified by study design, tumour category, the method sarcopenia assessed, treatment options, study location and among other factors.

METHODS

PubMed, Web of Science and Embase were searched from inception to 15 August 2024, without language restrictions and with a manual search of references for additional articles retrieval. Cohort studies of ≥ 100 patients with tumours that evaluated the association between sarcopenia or muscle mass and the adverse effects or overall survival induced by radio- and/or chemotherapy were included.

RESULTS

Thirty-nine studies were included, involving 8966 patients with tumours, including 3383 patients with sarcopenia. The pooled prevalence of sarcopenia in patients with tumours was 0.42 (95% CI 0.36-0.48, p < 0.001) overall. The prevalence of sarcopenia is higher in Oceania patients 0.60 (95% CI 0.28-0.89, p < 0.001), those with reproductive tumour 0.57 (95% CI 0.30-0.83, p < 0.001), and sarcopenia assessed by the lumbar-skeletal muscle index 0.46 (95% CI 0.39-0.53, p < 0.001) than in other subgroups, but not show significant differences in sex. Sarcopenia was associated with an increased risk of adverse effects in patients who received radio- and/or chemotherapy, with a relative risk (RR) of 1.44 (95% CI 1.21-1.71, p < 0.001). Retrospective studies (RR = 1.49; 95% CI 1.24-1.79; p < 0.001), sarcopenia assessed by other methods (RR = 2.98; 95% CI 1.52-5.87; p < 0.001), and patients in Europe (RR = 1.92; 95% CI 1.15-3.22; p = 0.013), received chemoradiotherapy (RR = 1.47; 95% CI 1.23-1.76; p < 0.001), and with head and neck tumours (RR = 1.54; 95% CI 1.17-2.01; p = 0.010) had higher relative risk than other subgroups. Sarcopenia was also associated with reduced overall survival in patients with tumours, with a pooled hazard ratio (HR) of 1.66 (95% CI 1.40-1.96, p < 0.001). Prospective studies (HR = 1.72; 95% CI 0.97-3.07; p = 0.065), sarcopenia assessed by the cervical-skeletal muscle index (HR = 2.66; 95% CI 1.73-4.09; p < 0.001), and patients in Asia (HR = 1.91; 95% CI 1.50-2.42; p < 0.001), received chemoradiotherapy (HR = 1.85; 95% CI 1.46-2.45; p < 0.001) and with head and neck tumours (HR = 2.35; 95% CI 1.88-2.95; p < 0.001) had higher HR than other subgroups.

CONCLUSIONS

Sarcopenia was associated with a higher risk of adverse effects and mortality in patients with tumours received radio- and/or chemotherapy.

Prevalence of Sarcopenia Determined by Computed Tomography in Pancreatic Cancer: A Systematic Review and Meta-Analysis of Observational Studies

Introduction: Sarcopenia, a condition characterized by a loss of skeletal muscle mass, is increasingly recognized as a significant factor influencing patient outcomes in pancreatic cancer (PC). This systematic review and meta-analysis aimed to estimate the prevalence of sarcopenia in patients with PC using computed tomography and to explore how different measurement methods and cut-off values impact such prevalence. Materials and Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a comprehensive search of PubMed, Web of Science, and EMBASE databases was performed, identifying 48 observational studies involving 9063 patients. Results: The overall pooled prevalence of sarcopenia was 45% (95% CI, 40-50%), but varied significantly by the method used: 47% when measured with the skeletal muscle index and 33% when assessed with the total psoas area. In addition, in studies using SMI, sarcopenia prevalence was 19%, 45%, and 57% for cutoff values <40 cm2/m2, 40-50 cm2/m2, and >50 cm2/m2, respectively. Moreover, the prevalence was higher in patients receiving palliative care (50%) compared to those treated with curative intent (41%). High heterogeneity was observed across all analyses, underscoring the need for standardized criteria in sarcopenia assessment. Conclusions: Our findings highlight the substantial variability in sarcopenia prevalence, which could influence patient outcomes, and stress the importance of consensus in measurement techniques to improve clinical decision making and research comparability.

Pancreatic cancer and sarcopenia: a narrative review of the current status

Pancreatic cancer is still a difficult disease to treat, despite recent advances in surgical techniques and chemotherapeutic drugs. Its incidence continues to rise, as does the number of older patients. Sarcopenia is defined as a progressive and generalized loss of skeletal muscle mass and strength. Sarcopenia is present in approximately 40% in patients with pancreatic cancer. Sarcopenia is primarily diagnosed through imaging, and progress is being made in the development of automated methods and artificial intelligence, as well as biomarker research. Sarcopenia has been linked to a poor prognosis in pancreatic cancer patients. However, some studies suggest that sarcopenia is not always associated with a poor prognosis, depending on the resectability of pancreatic cancer and the nature of treatment, such as surgery or chemotherapy. Recent meta-analyses have found that sarcopenia is not linked to postoperative complications. It is still debated whether there is a link between sarcopenia and drug toxicity during chemotherapy. The relationship between sarcopenia and immunity has been investigated, but the mechanism is still unknown.

Automatic deep learning method for third lumbar selection and body composition evaluation on CT scans of cancer patients

Introduction

The importance of body composition and sarcopenia is well-recognized in cancer patient outcomes and treatment tolerance, yet routine evaluations are rare due to their time-intensive nature. While CT scans provide accurate measurements, they depend on manual processes. We developed and validated a deep learning algorithm to automatically select and segment abdominal muscles [SM], visceral fat [VAT], and subcutaneous fat [SAT] on CT scans.

Materials and Methods

A total of 352 CT scans were collected from two cancer centers. The detection of the third lumbar vertebra and three different body tissues (SM, VAT, and SAT) were annotated manually. The 5-fold cross-validation method was used to develop the algorithm and validate its performance on the training cohort. The results were validated on an external, independent group of CT scans.

Results

The algorithm for automatic L3 slice selection had a mean absolute error of 4 mm for the internal validation dataset and 5.5 mm for the external validation dataset. The median DICE similarity coefficient for body composition was 0.94 for SM, 0.93 for VAT, and 0.86 for SAT in the internal validation dataset, whereas it was 0.93 for SM, 0.93 for VAT, and 0.85 for SAT in the external validation dataset. There were high correlation scores with sarcopenia metrics in both internal and external validation datasets.

Conclusions

Our deep learning algorithm facilitates routine research use and could be integrated into electronic patient records, enhancing care through better monitoring and the incorporation of targeted supportive measures like exercise and nutrition.