从全切片图像到生物标志物预测：弱监督深度学习在计算病理学中的端到端应用

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

Nature Protocols

影响因子:16

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

DOI: 10.1038/s41596-024-01047-2

摘要

苏木精-伊红染色的全切片图像（WSIs）是癌症诊断的基础。近年来，基于深度学习的计算病理学方法的发展，使得可以直接从WSIs预测生物标志物。然而，将组织表型与大规模生物标志物准确关联仍是实现精准肿瘤学复杂标志物普及的重要挑战。本方案描述了一个实用的Solid Tumor Associative Modeling in Pathology（STAMP）工作流程，利用深度学习实现直接从WSIs预测生物标志物。STAMP工作流程对生物标志物无偏差，并可将遗传信息和临床病理表格数据作为额外输入，与组织病理图像联合使用。该方案包括五个主要阶段：正式问题定义、数据预处理、建模、评估及临床转化，已成功应用于多个研究课题。例如，我们用STAMP预测结直肠癌的微卫星不稳定性（MSI）状态，表现出高准确率。我们还提供了开源代码库，已在全球多家医院部署，用于建立计算病理学工作流程。该工作流程仅需一天的手动计算操作和基础命令行知识。

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.