Cao Lu, He Ruimin, Zhang Ao, Li Lingmei, Cao Wenfeng, Liu Ning, Zhang Peisen
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.
Tianjin University of Science and Technology, Tianjin, 300222, China.
BMC Cancer. 2025 Feb 10;25(1):232. doi: 10.1186/s12885-025-13628-9.
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.
We collected and selected a total of 1585 prostate biopsy images with tumor regions from 317 patients (5 Whole Slide Images per patient) to develop a deep learning system for predicting BCR of PCa before prostatectomy. The Inception_v3 neural network was employed to train and test models developed from patch-level images. The multiple instance learning method was used to extract whole slide image-level features. Finally, patient-level artificial intelligence models were developed by integrating deep learning -generated pathology features with several machine learning algorithms.
The BCR prediction system demonstrated great performance in the testing cohort (AUC = 0.911, 95% Confidence Interval: 0.840-0.982) and showed the potential to produce favorable clinical benefits according to Decision Curve Analyses. Increasing the number of WSIs for each patient improves the performance of the prediction system. Additionally, the study explores the correlation between deep learning -generated features and pathological findings, emphasizing the interpretative potential of artificial intelligence models in pathology.
Deep learning system can use biopsy samples to predict the risk of BCR in PCa, thereby formulating targeted treatment strategies.
在接受根治性前列腺切除术的前列腺癌（PCa）男性患者中，20%-40%会发生生化复发（BCR）。提前预测哪些患者会发生BCR有助于制定更具针对性的前列腺切除手术。然而，目前术前复发预测主要依赖于Gleason分级系统的使用，该系统忽略了分级内的形态学模式和细微的组织病理学特征，大量的预后潜力未被挖掘。
我们从317例患者中收集并选择了总共1585张带有肿瘤区域的前列腺活检图像（每位患者5张全切片图像），以开发一种深度学习系统，用于在前列腺切除术前预测PCa的BCR。采用Inception_v3神经网络对从切片级图像开发的模型进行训练和测试。使用多实例学习方法提取全切片图像级特征。最后，通过将深度学习生成的病理特征与几种机器学习算法相结合，开发出患者级人工智能模型。
BCR预测系统在测试队列中表现出色（AUC = 0.911，95%置信区间：0.840 - 0.982），并且根据决策曲线分析显示出产生良好临床益处的潜力。增加每位患者的全切片图像数量可提高预测系统的性能。此外，该研究探索了深度学习生成的特征与病理结果之间的相关性，强调了人工智能模型在病理学中的解释潜力。
深度学习系统可以利用活检样本来预测PCa中BCR的风险，从而制定有针对性的治疗策略。
