Couture Heather D
Pixel Scientia Labs, Raleigh, NC 27610, USA.
J Pers Med. 2022 Dec 7;12(12):2022. doi: 10.3390/jpm12122022.
Molecular and genomic properties are critical in selecting cancer treatments to target individual tumors, particularly for immunotherapy. However, the methods to assess such properties are expensive, time-consuming, and often not routinely performed. Applying machine learning to H&E images can provide a more cost-effective screening method. Dozens of studies over the last few years have demonstrated that a variety of molecular biomarkers can be predicted from H&E alone using the advancements of deep learning: molecular alterations, genomic subtypes, protein biomarkers, and even the presence of viruses. This article reviews the diverse applications across cancer types and the methodology to train and validate these models on whole slide images. From bottom-up to pathologist-driven to hybrid approaches, the leading trends include a variety of weakly supervised deep learning-based approaches, as well as mechanisms for training strongly supervised models in select situations. While results of these algorithms look promising, some challenges still persist, including small training sets, rigorous validation, and model explainability. Biomarker prediction models may yield a screening method to determine when to run molecular tests or an alternative when molecular tests are not possible. They also create new opportunities in quantifying intratumoral heterogeneity and predicting patient outcomes.
分子和基因组特性在选择针对个体肿瘤的癌症治疗方法时至关重要，尤其是对于免疫疗法。然而，评估这些特性的方法既昂贵又耗时，而且通常不会常规进行。将机器学习应用于苏木精-伊红（H&E）染色图像可以提供一种更具成本效益的筛查方法。在过去几年中，数十项研究表明，利用深度学习的进展，仅通过H&E染色就能预测多种分子生物标志物：分子改变、基因组亚型、蛋白质生物标志物，甚至病毒的存在。本文回顾了在各种癌症类型中的不同应用，以及在全切片图像上训练和验证这些模型的方法。从自下而上的方法到病理学家驱动的方法再到混合方法，主要趋势包括各种基于弱监督深度学习的方法，以及在特定情况下训练强监督模型的机制。虽然这些算法的结果看起来很有前景，但一些挑战仍然存在，包括训练集小、严格的验证和模型可解释性。生物标志物预测模型可能会产生一种筛查方法，以确定何时进行分子检测，或者在无法进行分子检测时提供一种替代方法。它们还在量化肿瘤内异质性和预测患者预后方面创造了新的机会。
