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

Nature Protocols volume 20, pages 293–316 (2025)Cite this article

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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.

Key points

  • STAMP (solid tumor associative modeling in pathology) is a practical workflow for end-to-end weakly supervised deep learning in computational pathology, enabling prediction of biomarkers directly from whole-slide images.

  • This protocol differentiates itself from others by providing a collaborative framework through which clinical researchers can work with engineers to set up a complete computational pathology project.

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Fig. 1: Conceptual overview of the protocol.
Fig. 2: Computational workflow from WSI to patient-level biomarker prediction.
Fig. 3: Factors influencing the required sample size in computational pathology projects.
Fig. 4: Positioning of the STAMP software.
Fig. 5: Anticipated results of the evaluation phase of the protocol for the analysis of CRC from TCGA and the CPTAC.
Fig. 6: Anticipated results of the translation phase of the protocol for the analysis of CRC from the CPTAC.

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Data availability

Histopathology slides and genomics data from TCGA and CPTAC were used to train and validate the models. The slides for TCGA are available at https://portal.gdc.cancer.gov/. The slides for CPTAC are available at https://proteomics.cancer.gov/data-portal. The molecular and clinical data for TCGA and CPTAC used in the experiments are available at https://github.com/KatherLab/cancer-metadata. Source data are provided with this paper.

Code availability

The open-source STAMP software for the implementation of the MSI experiments is available on GitHub (https://github.com/KatherLab/STAMP).

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Acknowledgements

We thank the testers of the protocol, S. Sainath, O. L. Saldanha, L. Žigutytė, C. Kummer, G. Serna, K. Boehm and L. Shaktah, who executed the STAMP protocol on various systems at cancer centers around the world. O.S.M.E.N. is supported by the German Federal Ministry of Education and Research (BMBF) through grant 1IS23070, Software Campus 3.0 (TU Dresden), as part of the Software Campus project ’MIRACLE-AI’. J.N.K. is supported by the German Federal Ministry of Health (DEEP LIVER, ZMVI1-2520DAT111), the German Cancer Aid (DECADE, 70115166), the German Federal Ministry of Education and Research (PEARL, 01KD2104C; CAMINO, 01EO2101; SWAG, 01KD2215A; TRANSFORM LIVER, 031L0312A; TANGERINE, 01KT2302 through ERA-NET Transcan), the German Academic Exchange Service (SECAI, 57616814), the German Federal Joint Committee (TransplantKI, 01VSF21048), the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091; GENIAL, 101096312) and the National Institute for Health and Care Research (NIHR, NIHR213331) Leeds Biomedical Research Centre. G.W. is supported by Lothian NHS. D.T. is supported by the German Federal Ministry of Education and Research (SWAG, 01KD2215A; TRANSFORM LIVER) and the European Union’s Horizon Europe and innovation programme (ODELIA, 101057091). S.F. is supported by the German Federal Ministry of Education and Research (SWAG, 01KD2215A), the German Cancer Aid (DECADE, 70115166) and the German Research Foundation (504101714). S.J.W. was supported by the Helmholtz Association under the joint research school ‘Munich School for Data Science – MUDS’ and the Add-on Fellowship of the Joachim Herz Foundation. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. This work was funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them.

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Authors and Affiliations

  1. Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany

    Omar S. M. El Nahhas, Marko van Treeck, Michaela Unger, Marta Ligero, Tim Lenz, Katherine J. Hewitt & Jakob Nikolas Kather

  2. StratifAI GmbH, Dresden, Germany

    Omar S. M. El Nahhas, Firas Khader, Daniel Truhn & Jakob Nikolas Kather

  3. School of Computer Science, University of St Andrews, St Andrews, UK

    Georg Wölflein

  4. Helmholtz Munich–German Research Center for Environment and Health, Munich, Germany

    Sophia J. Wagner

  5. School of Computation, Information and Technology, Technical University of Munich, Munich, Germany

    Sophia J. Wagner

  6. Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany

    Firas Khader & Daniel Truhn

  7. Institute of Pathology–University Medical Center Mainz, Mainz, Germany

    Sebastian Foersch

  8. Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany

    Jakob Nikolas Kather

  9. Department of Medicine 1, University Hospital and Faculty of Medicine Carl Gustav Carus, Technical University Dresden, Dresden, Germany

    Jakob Nikolas Kather

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Contributions

O.S.M.E.N. and J.N.K. designed the protocol. O.S.M.E.N., M.v.T., G.W. and T.L. developed the software and wrote technical documentation. O.S.M.E.N., M.v.T., G.W., T.L., M.L., M.U., S.J.W., F.K., S.F. and D.T. tested the software. O.S.M.E.N., J.N.K. and K.J.H. interpreted and analyzed the data. All authors wrote and reviewed the protocol and approved the final version for submission.

Corresponding author

Correspondence to Jakob Nikolas Kather.

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Competing interests

O.S.M.E.N., F.K. and D.T. hold shares in StratifAI GmbH. J.N.K. declares consulting services for Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK,; Scailyte, Switzerland; Mindpeak, Germany; and Histofy, UK; furthermore, he holds shares in StratifAI GmbH, Germany, and has received honoraria for lectures by AstraZeneca, Bayer, Eisai, MSD, BMS, Roche, Pfizer and Fresenius. D.T. received honoraria for lectures by Bayer and holds shares in StratifAI GmbH, Germany. S.F. has received honoraria from MSD and BMS.

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Key references using this protocol

Wagner, S. J. et al. Cancer Cell 41, 1650–1661.e4 (2023): https://doi.org/10.1016/j.ccell.2023.08.002

El Nahhas, O. S. M. et al. Nat. Commun. 15, 1253 (2024): https://doi.org/10.1038/s41467-024-45589-1

Jiang, X. et al. Lancet Digit. Health 6, e33–e43 (2024): https://doi.org/10.1016/S2589-7500(23)00208-X

Hewitt, K. J. et al. Neurooncol. Adv. 5, vdad139 (2023): https://doi.org/10.1093/noajnl/vdad139

Saldanha, O. L. et al. npj Precis. Onc. 7, 35 (2023): https://doi.org/10.1038/s41698-023-00365-0

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El Nahhas, O.S.M., van Treeck, M., Wölflein, G. et al. From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nat Protoc 20, 293–316 (2025). https://doi.org/10.1038/s41596-024-01047-2

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