研究动态
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从整个幻灯片图像到生物标志物预测:计算病理学中的端到端弱监督深度学习。

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

发表日期:2024 Sep 16
作者: Omar S M El Nahhas, Marko van Treeck, Georg Wölflein, Michaela Unger, Marta Ligero, Tim Lenz, Sophia J Wagner, Katherine J Hewitt, Firas Khader, Sebastian Foersch, Daniel Truhn, Jakob Nikolas Kather
来源: Nature Protocols

摘要:

苏木精和伊红染色的全玻片图像 (WSI) 是癌症诊断的基础。近年来,计算病理学中基于深度学习的方法的发展使得能够直接从 WSI 预测生物标志物。然而,大规模准确地将组织表型与生物标志物联系起来仍然是精准肿瘤学中复杂生物标志物民主化的关键挑战。该协议描述了病理学中实体瘤关联建模 (STAMP) 的实用工作流程,能够使用深度学习直接从 WSI 预测生物标志物。 STAMP 工作流程与生物标志物无关,并且允许将遗传和临床病理表格数据与组织病理学图像一起作为附加输入包含在内。该协议由五个主要阶段组成,已成功应用于各种研究问题:正式问题定义、数据预处理、建模、评估和临床翻译。 STAMP 工作流程的独特之处在于它专注于作为一个协作框架,临床医生和工程师可以使用该框架来建立计算病理学领域的研究项目。作为示例任务,我们应用 STAMP 来预测结直肠癌的微卫星不稳定性 (MSI) 状态,显示出识别高 MSI 肿瘤的准确性能。此外,我们提供了一个开源代码库,已在全球多家医院部署,用于建立计算病理学工作流程。 STAMP 工作流程需要一个工作日的实际计算执行和基本命令行知识。© 2024。Springer Nature Limited。
Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.© 2024. Springer Nature Limited.