从全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
影响因子:16.00000
分区:生物学2区 Top / 生化研究方法1区
发表日期:2025 Jan
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
摘要
苏木精和曙红染色的全裂片图像（WSI）是癌症诊断的基础。近年来，在计算病理学中基于深度学习的方法的开发使生物标志物直接从WSIS进行了预测。然而，将组织表型与生物标志物的大规模关键相关联仍然是使精确肿瘤学中复杂生物标志物民主化的挑战仍然是一个至关重要的挑战。该协议描述了病理学（邮票）实体瘤联想建模的实用工作流程，从而通过使用深度学习直接从WSI中预测了生物标志物。邮票工作流程是生物标志物不可知论，允许将遗传病理和临床表格数据作为额外的输入以及组织病理学图像包括在内。该协议由五个主要阶段组成，这些阶段已成功应用于各种研究问题：形式的问题定义，数据预处理，建模，评估和临床翻译。 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.作为一个示例任务，我们将邮票应用于大肠癌中微卫星不稳定性（MSI）状态的预测，显示了MSI中高肿瘤的准确性能。此外，我们提供了一个开源代码基础，该代码基础已在全球多家医院部署，以建立计算病理学工作流程。邮票工作流需要一个动手计算执行和基本命令行知识的工作日。
Abstract
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.