arXiv 2312.109442023-12-18cs.CV

从全切片图像到生物标志物预测：计算病理学中端到端深度学习的操作流程

From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

Technical University Dresden（德累斯顿工业大学）；University of St Andrews（圣安德鲁斯大学）；Helmholtz Munich – German Research Center for Environment and Health（亥姆霍兹慕尼黑中心-德国环境与健康研究中心）；Technical University of Munich（慕尼黑工业大学）；University Hospital Aachen（亚琛大学医院）；University Medical Center Mainz（美因茨大学医学中心）；University of Leeds（利兹大学）；National Center for Tumor Diseases (NCT)（国家肿瘤疾病中心 (NCT)）；University Hospital Heidelberg（海德堡大学医院）；Technische Universität Dresden（德累斯顿工业大学）

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 F… 展开作者

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

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AI总结：

本研究提出计算病理学领域的STAMP端到端深度学习工作流，可结合病理图像与多组学数据预测生物标志物，含五大核心阶段，经结直肠癌MSI预测验证有效，配套开源代码已在全球多家医院部署。

摘要：

苏木精-伊红（H&E）染色的全切片图像（WSIs）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直接从WSIs预测生物标志物成为可能。然而，大规模地将组织表型与生物标志物准确关联，仍是在精准肿瘤学中普及复杂生物标志物的关键挑战。本操作流程介绍了病理学实体肿瘤关联建模（STAMP）的实用工作流，可利用深度学习直接从WSIs预测生物标志物。STAMP工作流不依赖特定生物标志物，除组织病理学图像外，还可将遗传学及临床病理学表格数据作为额外输入纳入。该流程包含五个已成功应用于各类研究问题的主要阶段：正式问题定义、数据预处理、建模、评估以及临床转化。STAMP工作流的独特之处在于其聚焦于作为协作框架，可供临床医生和工程师 alike用于搭建计算病理学领域的研究项目。作为示例任务，我们将STAMP应用于结直肠癌微卫星不稳定性（MSI）状态的预测，在识别MSI-high肿瘤方面展现出准确的性能。此外，我们提供了一个开源代码库，该代码库已在全球多家医院部署，用于搭建计算病理学工作流。STAMP工作流需要一个工作日的实际计算操作时间，且要求使用者具备基础的命令行知识。

英文摘要：

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology 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 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 which 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 MSI-high tumors. Moreover, we provide an open-source codebase 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.

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