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<p>Biochemical recurrence (BCR) of prostate cancer (PCa) negatively impacts patients’ post-surgery quality of life, and the traditional predictive models have shown limited accuracy. This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). Data from 545 patients across two centres are used for training and validation. The model showed strong performances, with high accuracy in identifying regions with high AR expression and BCR prediction. This AI model may help identify high-risk patients, aiding in better treatment strategies, particularly in underdeveloped areas.</p>

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AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月1日

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  • Jiawei Zhang,
  • Feng Ding,
  • Yitian Guo,
  • Xiaoying Wei,
  • Jibo Jing,
  • Feng Xu,
  • Huixing Chen,
  • Zhongying Guo,
  • Zonghao You,
  • Baotai Liang,
  • Ming Chen,
  • Dongfang Jiang,
  • Xiaobing Niu,
  • Xiangxue Wang,
  • Yifeng Xue

摘要

Biochemical recurrence (BCR) of prostate cancer (PCa) negatively impacts patients’ post-surgery quality of life, and the traditional predictive models have shown limited accuracy. This study develops an AI-based prognostic model using deep learning that incorporates androgen receptor (AR) regional features from whole-slide images (WSIs). Data from 545 patients across two centres are used for training and validation. The model showed strong performances, with high accuracy in identifying regions with high AR expression and BCR prediction. This AI model may help identify high-risk patients, aiding in better treatment strategies, particularly in underdeveloped areas.