基于回归的深度学习从病理切片预测分子生物标志物
Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
AI总结：
本文提出一种自监督弱监督回归方法，直接从病理图像预测连续生物标志物，并在11,671例患者中证明其准确性和预后价值优于分类方法。
摘要：
深度学习（DL）能够从癌症组织病理学中预测生物标志物。多项已获临床批准的应用使用了这一技术。然而，大多数方法预测的是分类标签，而生物标志物通常是连续测量值。我们假设基于回归的深度学习优于基于分类的深度学习。因此，我们开发并评估了一种新的自监督、基于注意力的弱监督回归方法，该方法在涵盖九种癌症类型的11,671名患者中直接从图像预测连续生物标志物。我们针对多个具有临床和生物学相关性的生物标志物测试了该方法：同源重组修复缺陷（HRD）评分（一种临床使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，同时相比分类也改善了结果的可解释性。在一个大型结直肠癌患者队列中，基于回归的预测评分提供了比基于分类的评分更高的预后价值。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有前景的替代方案。
英文摘要：
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 hypothesized that regression-based DL outperforms classification-based DL. Therefore, we developed and evaluated a new self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from images in 11,671 patients across nine cancer types. We tested our method for multiple clinically and biologically relevant biomarkers: homologous repair deficiency (HRD) 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 interpretability of the results 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.
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