Molecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Clinical Research Directorate, Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Mod Pathol. 2021 Feb;34(2):478-489. doi: 10.1038/s41379-020-00674-w. Epub 2020 Sep 3.
Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.
磷酸酶和张力蛋白同源物（PTEN）缺失与前列腺癌的不良预后相关，并具有作为预后生物标志物的临床潜力。本研究旨在开发一种用于自动检测和定位免疫组织化学（IHC）染色切片中 PTEN 缺失的人工智能（AI）系统。使用 IHC 在两个前列腺组织微阵列（TMA）（内部队列，n=272 例；外部队列，n=129 例）中评估 PTEN 缺失。病理学家对 TMA 核心进行视觉评分以评估 PTEN 缺失，如果存在，则进行空间注释。内部 TMA 队列中每位患者的核心分为 90%的交叉验证（N=2048）和 10%的保留测试（N=224）集。使用 ResNet-101 架构通过多分辨率集成方法（×5、×10 和×20）基于核心进行分类训练。对于空间注释，从 ×20 分辨率提取的补丁中训练单分辨率像素分类，插值到 ×40 分辨率，并以滑动窗口方式应用。从多分辨率和像素分类模型组合创建最终的基于 AI 的预测模型。在外部队列的 428 个核心中评估性能。从两个队列共研究了 2700 个核心，内部队列的 PTEN 缺失频率为 14.5%（180/1239），外部队列为 13.5%（43/319）。内部队列交叉验证集的最终基于 AI 的 PTEN 状态预测显示 98.1%的准确率（95.0%的敏感性，98.4%的特异性；中位数 Dice 评分=0.811），内部队列测试集的准确率为 99.1%（100%的敏感性，99.0%的特异性；中位数 Dice 评分=0.804）。在进一步使用队列数据的 15%（124 例患者中的 19 例）进行训练（微调）后，外部队列的整体基于核心的分类得到了显著改善（曲线下面积=0.964，90.6%的敏感性，95.7%的特异性）。这些结果表明，在前列腺癌组织样本中检测和定位 PTEN 缺失的方法具有稳健性和完全自动化。基于 AI 的算法有可能简化研究和临床实验室的样本评估。
