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Updated: Nov 12, 2025

Bioluminescent Orthotopic Model of Pancreatic Cancer Progression
Published on: June 28, 2013
Prognostic significance of skeletal muscle decrease in unresectable pancreatic cancer: Survival analysis using the
Hiroki Sato1, Takuma Goto1, Akihiro Hayashi1
1Department of Medicine, Asahikawa Medical University, 2-1 Midorigaoka-Higashi, Asahikawa, Japan.
Background/Objectives:
Decrease in skeletal muscle mass and function is associated with a poor prognosis following surgical resection of pancreatic ductal adenocarcinomas (PDAs). This study evaluated whether skeletal muscle mass decrease affects PDA outcomes.
Methods:
Data of 112 patients with advanced and unresectable PDA who underwent chemotherapy in a single institution were retrospectively analyzed. Information on age, sex, hematological investigations, including systemic inflammation-based markers and nutritional assessment biomarkers, and imaging parameters of skeletal muscle mass and visceral adipose tissue were retrieved from the patients' medical records. The efficiency of the Cox, Weibull, and standardized exponential models were compared using hazard ratios and the Akaike Information Criterion (AIC).
Results:
Results from the Weibull, Cox, and standardized exponential model analyses indicated that low skeletal muscle mass, Eastern Cooperative Oncology Group performance status (PS), and the requirement of biliary drainage were associated with the highest risk of death, followed by carcinoembryonic antigen (CEA) levels and the presence of ascites. The AIC value from the four significant parameters was lowest for the Weibull-exponential distribution (222.3) than that of the Cox (653.7) and standardized exponential models (265.7). We developed a model for estimating the 1-year survival probability using the Weibull-exponential distribution.
Conclusions:
Low-skeletal muscle index, PS, requirement of biliary drainage, CEA levels, and presence of ascites are independent factors for predicting poor patient survival after chemotherapy. Improved survival modeling using a parametric approach may accurately predict the outcome of patients with advanced-stage PDA.
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