Interpretable Prognostic Modeling for Postoperative Pancreatic Cancer Using Multi-machine Learning and Habitat Radiomics: A Multi-center Study.

Journal: Academic radiology
Published Date: May 5, 2025

Abstract

RATIONALE AND OBJECTIVES: Accurate risk stratification is critical for guiding personalized treatment in resectable pancreatic cancer (RPC). This retrospective study assessed the utility of habitat radiomics for predicting recurrence-free survival (RFS) in RPC patients.

Authors

  • Qianbiao Gu
    Department of Radiology, The Third Xiangya Hospital of Central South University, Changsha 410013, China; Department of Radiology, The People's Hospital of Hunan Province, The First Hospital Affiliated of Hunan Normal University, Changsha 410005, China.
  • Yan Xing
    School of science, China Pharmaceutical University, Nanjing, China.
  • Xiaoli Hu
    Department of Radiology, First Affiliated Hospital of Hunan University of Chinese Medicine, 410000 Changsha, China.
  • Jiankang Yang
    Department of Radiology, Yueyang Central Hospital, 414000 Yueyang, China.
  • Yong Chen
    Department of Urology, Chongqing University Fuling Hospital, Chongqing, China.
  • Yaqiong He
    Department of Radiology, Hunan Provincial People's Hospital and The First Affiliated Hospital of Hunan Normal University, 410000 Changsha, China (Q.G., Y.H., P.L.).
  • Peng Liu
    Department of Clinical Pharmacy, Dazhou Central Hospital, Dazhou 635000, China.

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