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