Predicting NSCLC surgical outcomes using deep learning on histopathological images: development and multi-omics validation of Sr-PPS model.

Journal: International journal of surgery (London, England)
Published Date: May 29, 2025

Abstract

BACKGROUND: Currently, there remains a critical need for reliable tools to accurately predict post-surgical outcomes in non-small cell lung cancer (NSCLC) patients in clinical practice. We aimed to develop and validate a deep learning-based model utilizing histopathological slides to accurately predict post-surgical outcomes in NSCLC patients.

Authors

  • Shengkun Peng
    Department of Radiology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China.
  • Kegang Jia
    Department of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China.
  • Mei Peng
    College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
  • Yi Chen
    Department of Anesthesiology and Perioperative Medicine, General Hospital of Ningxia Medical University, Yinchuan, China.
  • Anqi Lin
    Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
  • Peng Luo
    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, PR China.
  • Jirui Wang
    Institute of Physical Education and Sport, Shanxi University, Taiyuan, China.
  • Youyu Wang
    Department of Thoracic Surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
  • Yifeng Bai
    Department of Oncology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. baiyifeng@med.uestc.edu.cn.

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