Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
Nat Commun. 2024 Feb 10;15(1):1253. doi: 10.1038/s41467-024-45589-1.
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions' correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
深度学习（DL）可以从癌症组织病理学中预测生物标志物。有几个经过临床批准的应用程序使用这项技术。然而，大多数方法预测的是类别标签，而生物标志物通常是连续测量值。我们假设基于回归的 DL 优于基于分类的 DL。因此，我们开发并评估了一种基于注意力的自监督弱监督回归方法，该方法可直接从 9 种癌症类型的 11671 张患者图像中预测连续的生物标志物。我们针对多个临床和生物学上相关的生物标志物测试了我们的方法：同源重组缺陷评分，一种临床应用的泛癌生物标志物，以及肿瘤微环境中关键生物学过程的标志物。使用回归显著提高了生物标志物预测的准确性，同时也提高了预测与已知临床相关区域的对应程度，优于分类。在一大群结直肠癌患者中，基于回归的预测评分比基于分类的评分具有更高的预后价值。我们的开源回归方法为计算病理学中的连续生物标志物分析提供了一种很有前途的替代方法。
