Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

Journal: Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Published Date: Apr 11, 2024

Abstract

PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML models to predict biochemical recurrence (BCR) of prostate cancer (PCa).

Authors

  • Wenhao Lu
    School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, 639798, Singapore.
  • Lin Zhao
    c Key Laboratory of Birth Defects and Related Diseases of Women and Children (Ministry of Education) , West China Second University Hospital Sichuan University , Chengdu , China.
  • Shenfan Wang
    Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, 200433, People's Republic of China.
  • Huiyong Zhang
    Key Laboratory of Noise and Vibration Research, Institute of Acoustics, Chinese Academy of Sciences, Beijing, China.
  • Kangxian Jiang
    Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000, Fujian, China.
  • Jin Ji
    Department of Urology, Shanghai Changhai Hospital, Second Military Medical University, Shanghai, 200433, China.
  • Shaohua Chen
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Chengbang Wang
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Chunmeng Wei
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China.
  • Rongbin Zhou
    Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-Constructed By the Province and Ministry, Guangxi Medical University, No. 22, Shuangyong Road, Qingxiu District, Nanning City, 530021, Guangxi Zhuang Autonomous Region, People's Republic of China.
  • Zuheng Wang
    Department of Urology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, China.
  • Xiao Li
    Department of Inner Mongolia Clinical Medicine College, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China.
  • Fubo Wang
    Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, Guangxi Collaborative Innovation Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, 530021, Guangxi, China. wangbofengye@163.com.
  • Xuedong Wei
    Department of Urology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
  • Wenlei Hou
    Information Technology School of Guangxi Police College, Nanning, 530021, Guangxi, People's Republic of China. wenleihou@163.com.

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