Interpretable machine learning model for predicting clinically significant prostate cancer: integrating intratumoral and peritumoral radiomics with clinical and metabolic features.

Journal: BMC medical imaging
PMID: 39736623

Abstract

BACKGROUND: To develop and validate an interpretable machine learning model based on intratumoral and peritumoral radiomics combined with clinicoradiological features and metabolic information from magnetic resonance spectroscopy (MRS), to predict clinically significant prostate cancer (csPCa, Gleason score ≥ 3 + 4) and avoid unnecessary biopsies.

Authors

  • Wenjun Zhao
    Department of MRI, Xinxiang Central Hospital (The Fourth Clinical College of Xinxiang Medical University), 56 Jinsui Road, Xinxiang, Henan, 453000, China. 594861640@qq.com.
  • Mengyan Hou
    Department of MRI, Xinxiang Central Hospital (The Fourth Clinical College of Xinxiang Medical University), 56 Jinsui Road, Xinxiang, Henan, 453000, China.
  • Juan Wang
    Institute of Electrical Engineering, Yanshan University, Qinhuangdao, China.
  • Dan Song
    Department of Ophthalmology, Beijing Tisnghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, China.
  • Yongchao Niu
    Department of MRI, Xinxiang Central Hospital (The Fourth Clinical College of Xinxiang Medical University), 56 Jinsui Road, Xinxiang, Henan, 453000, China.

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