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.

该研究论文介绍了一种实用的基于深度学习的固体肿瘤相关建模工作流程（STAMP），使得可以直接从组织病理学图像中预测生物标志物。它包括问题定义、数据预处理、建模、评估和临床转化五个主要阶段，并且注重作为一个合作框架，为临床医生和工程师在计算病理学领域开展研究项目提供支持。