Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Windreich Department of Artificial Intelligence and Human Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Sci Adv. 2024 Aug 23;10(34):eadi0302. doi: 10.1126/sciadv.adi0302.
High-grade glioma (HGG) is an aggressive brain tumor. Sex is an important factor that differentially affects survival outcomes in HGG. We used an end-to-end deep learning approach on hematoxylin and eosin (H&E) scans to (i) identify sex-specific histopathological attributes of the tumor microenvironment (TME), and (ii) create sex-specific risk profiles to prognosticate overall survival. Surgically resected H&E-stained tissue slides were analyzed in a two-stage approach using ResNet18 deep learning models, first, to segment the viable tumor regions and second, to build sex-specific prognostic models for prediction of overall survival. Our mResNet-Cox model yielded C-index (0.696, 0.736, 0.731, and 0.729) for the female cohort and C-index (0.729, 0.738, 0.724, and 0.696) for the male cohort across training and three independent validation cohorts, respectively. End-to-end deep learning approaches using routine H&E-stained slides, trained separately on male and female patients with HGG, may allow for identifying sex-specific histopathological attributes of the TME associated with survival and, ultimately, build patient-centric prognostic risk assessment models.
高级别胶质瘤（HGG）是一种侵袭性脑肿瘤。性别是影响 HGG 患者生存结果的一个重要因素。我们使用端到端深度学习方法对苏木精和伊红（H&E）扫描进行分析，（i）识别肿瘤微环境（TME）中性别特异性的组织病理学特征，以及（ii）创建性别特异性风险预测模型以预测总生存。使用 ResNet18 深度学习模型，采用两阶段方法对手术切除的 H&E 染色组织切片进行分析，首先，对有活力的肿瘤区域进行分割，其次，构建性别特异性预测总生存的预后模型。我们的 mResNet-Cox 模型在训练和三个独立验证队列中分别为女性队列产生了 C 指数（0.696、0.736、0.731 和 0.729），为男性队列产生了 C 指数（0.729、0.738、0.724 和 0.696）。使用常规 H&E 染色幻灯片，分别对 HGG 男性和女性患者进行端到端深度学习分析，可能有助于识别与生存相关的 TME 性别特异性组织病理学特征，并最终构建以患者为中心的预后风险评估模型。
