Development of a deep learning system for predicting biochemical recurrence in prostate cancer.

Journal: BMC cancer
PMID: 39930342

Abstract

BACKGROUND: Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. However, current preoperative recurrence prediction mainly relies on the use of the Gleason grading system, which omits within-grade morphological patterns and subtle histopathological features, leaving a significant amount of prognostic potential unexplored.

Authors

  • Lu Cao
    FL 8, Ocean International Center E, Chaoyang Rd Side Rd, ShiLiPu, Chaoyang Qu, 100000 Beijing Shi, China.
  • Ruimin He
    Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin, 300060, China.
  • Ao Zhang
    Tianjin University of Science and Technology, Tianjin, 300222, China.
  • Lingmei Li
    Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Tianjin 300060, China.
  • Wenfeng Cao
    Department of Pathology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin; Tianjin's Clinical Research Center for Cancer, Tianjin Medical University, Ministry of Education, Tianjin 300060, China.
  • Ning Liu
    School of Public Health, Hangzhou Normal University, Hangzhou, China.
  • Peisen Zhang
    Tianjin University of Science and Technology, Tianjin, 300222, China. zpsbit@foxmail.com.

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