PDF(Pubmed)
Abstract：
The Gleason score is an important predictor of prognosis in prostate cancer. However, its subjective nature can result in over- or under-grading. Our objective was to train an artificial intelligence (AI)-based algorithm to grade prostate cancer in specimens from patients who underwent radical prostatectomy (RP) and to assess the correlation of AI-estimated proportions of different Gleason patterns with biochemical recurrence-free survival (RFS), metastasis-free survival (MFS), and overall survival (OS). Training and validation of algorithms for cancer detection and grading were completed with three large datasets containing a total of 580 whole-mount prostate slides from 191 RP patients at two centers and 6218 annotated needle biopsy slides from the publicly available Prostate Cancer Grading Assessment dataset. A cancer detection model was trained using MobileNetV3 on 0.5 mm × 0.5 mm cancer areas (tiles) captured at 10× magnification. For cancer grading, a Gleason pattern detector was trained on tiles using a ResNet50 convolutional neural network and a selective CutMix training strategy involving a mixture of real and artificial examples. This strategy resulted in improved model generalizability in the test set compared with three different control experiments when evaluated on both needle biopsy slides and whole-mount prostate slides from different centers. In an additional test cohort of RP patients who were clinically followed over 30 years, quantitative Gleason pattern AI estimates achieved concordance indexes of 0.69, 0.72, and 0.64 for predicting RFS, MFS, and OS times, outperforming the control experiments and International Society of Urological Pathology system (ISUP) grading by pathologists. Finally, unsupervised clustering of test RP patient specimens into low-, medium-, and high-risk groups based on AI-estimated proportions of each Gleason pattern resulted in significantly improved RFS and MFS stratification compared with ISUP grading. In summary, deep learning-based quantitative Gleason scoring using a selective CutMix training strategy may improve prognostication after prostate cancer surgery.
摘要：
Gleason评分是前列腺癌预后的重要预测因子。然而,它的主观性会导致等级过高或过低。我们的目标是训练一种基于人工智能（AI）的算法来对接受根治性前列腺切除术（RP）的患者标本中的前列腺癌进行分级，并评估AI估计的不同Gleason模式的比例与生化无复发生存率的相关性（RFS）。无转移生存率（MFS），总生存率（OS）。使用三个大型数据集完成了癌症检测和分级算法的训练和验证，其中包含来自两个中心的191名RP患者的总共580个完整的前列腺载玻片和来自公开可用的前列腺癌分级评估数据集的6218个注释的针活检载玻片。使用MobileNetV3在以10倍放大捕获的0.5mmX0.5mm癌症区域(瓦片)上训练癌症检测模型。对于癌症分级，使用ResNet50卷积神经网络和涉及真实和人工示例的混合的选择性CutMix训练策略在图块上训练Gleason模式检测器。当在来自不同中心的针吸活检载玻片和整体安装前列腺载玻片上进行评估时，与三个不同的对照实验相比，该策略导致测试集中的模型泛化性得到改善。在临床随访超过30年的RP患者的额外测试队列中，定量格里森模式AI估计实现了0.69、0.72和0.64的一致性指数，用于预测RFS，MFS,和OS时间，优于病理学家的对照实验和国际泌尿外科病理学学会（ISUP）分级。最后,将测试RP患者标本无监督聚类为低，medium-,与ISUP分级相比,基于每种Gleason模式的AI估计比例的高风险组可显著改善RFS和MFS分层.总之,使用选择性CutMix训练策略的基于深度学习的定量Gleason评分可能会改善前列腺癌手术后的预后。
