Multimodal deep learning model for prognostic prediction in cervical cancer receiving definitive radiotherapy: a multi-center study.

Journal: NPJ digital medicine
Published Date:

Abstract

For patients with locally advanced cervical cancer (LACC), precise survival prediction models could guide personalized treatment. We developed and validated CerviPro, a deep learning-based multimodal prognostic model, to predict disease-free survival (DFS) in 1018 patients with LACC receiving definitive radiotherapy. The model integrates pre- and post-treatment CT imaging, handcrafted radiomic features, and clinical variables. CerviPro demonstrated robust predictive performance in the internal validation cohort (C-index 0.81), and external validation cohorts (C-index 0.70&0.66), significantly stratifying patients into distinct high- and low-risk DFS groups. Multimodal feature fusion consistently outperformed models based on single feature categories (clinical data, imaging, or radiomics alone), highlighting the synergistic value of integrating diverse data sources. By integrating multimodal data to predict DFS and recurrence risk, CerviPro provides a clinically valuable prognostic tool for LACC, offering the potential to guide personalized treatment strategies.

Authors

  • Weiping Wang
    Department of Chemical Engineering, School of Chemistry and Chemical Engineering, Nanjing University.
  • Guang Yang
    National Heart and Lung Institute, Imperial College London, London, UK.
  • Yulin Liu
    Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Lichun Wei
    Department of Radiation Oncology, the First Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
  • Xiaoying Xu
    Department of Radiation Oncology, Second Affiliated Hospital of Dalian Medical University, Dalian, China.
  • Chulong Zhang
    Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
  • Zhaohong Pan
    Medical AI Lab, School of Biomedical Engineering, Health Science Centre, Shenzhen University, Shenzhen, China.
  • Yongguang Liang
    Department of Radiotherapy, Peking Union Medical College Hospital, Beijing, 100730, China.
  • Bo Yang
    Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
  • Jie Qiu
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China. Electronic address: qiujie@pumch.cn.
  • Fuquan Zhang
    Department of Radiation Oncology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China.
  • Xiaorong Hou
    College of Medical Informatics, Chongqing Medical University, Chongqing, China.
  • Ke Hu
    Medical College, Hunan University of Medicine, Huaihua 418000, China.
  • Xiaokun Liang

Keywords

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