Zhang Jiawei, Ding Feng, Guo Yitian, Wei Xiaoying, Jing Jibo, Xu Feng, Chen Huixing, Guo Zhongying, You Zonghao, Liang Baotai, Chen Ming, Jiang Dongfang, Niu Xiaobing, Wang Xiangxue, Xue Yifeng
Department of Urology, Zhongda Hospital, Southeast University, Nanjing, China.
Department of Medical College, Southeast University, Nanjing, China.
Sci Rep. 2025 Feb 1;15(1):3985. doi: 10.1038/s41598-025-88199-7.
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.
前列腺癌（PCa）的生化复发（BCR）对患者术后生活质量产生负面影响，且传统预测模型的准确性有限。本研究利用深度学习开发了一种基于人工智能的预后模型，该模型整合了来自全切片图像（WSIs）的雄激素受体（AR）区域特征。来自两个中心的545例患者的数据用于训练和验证。该模型表现出色，在识别高AR表达区域和BCR预测方面具有很高的准确性。这种人工智能模型可能有助于识别高危患者，有助于制定更好的治疗策略，尤其是在欠发达地区。
