Regression-based Deep-Learning predicts molecular biomarkers from pathology slides
Open accessAbstract
Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.
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The integration of deep learning methods with digital pathology presents a promising and potentially cost-effective approach to interrogate the TME and alleviate some issues.
“The integration of deep learning methods with digital pathology presents a promising and potentially cost-effective approach to interrogate the TME and alleviate some of these issues51.”
From Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images (2024) · cites this paper as [51]
The Results section (page unavailable) shows that a deep-learning model predicted tumor, stromal, and immune-cell biomarkers from digital pathology slides, supporting its promise for interrogating the tumor microenvironment. Our copy does not establish that the approach is cost-effective or that it alleviates the unspecified issues in the citing sentence. (LLM, conf=0.87)
Experimental — accuracy not yet measuredThis approach follows standard practices in computational pathology and provides robust performance estimates and better generalization assessment.
“This approach follows standard practices in computational pathology and provides robust performance estimates and better generalization assessment10.”
From Benchmarking foundation models as feature extractors for weakly supervised computational pathology (2025) · cites this paper as [10]
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“The integration of deep learning methods with digital pathology presents a promising and potentially cost-effective approach to interrogate the TME and alleviate some of these issues 51 .”
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- Authors
- El Nahhas, O. S. M., Loeffler, C. M. L., Carrero, Z. I., van Treeck, M., Kolbinger, F. R., Hewitt, K. J., Muti, H. S., Graziani, M., Zeng, Q., Calderaro, J., Ortiz-Brüchle, N., Yuan, T., Hoffmeister, M., Brenner, H., Brobeil, A., Reis-Filho, J. S., & Kather, J. N.
- Affiliations
- University Hospital Carl Gustav Carus (DE)
- Else Kröner Fresenius Center for Digital Health (DE)
- HES-SO University of Applied Sciences and Arts Western Switzerland (CH)
- Inserm (FR)
- German Cancer Research Center (DE)
- Heidelberg University (DE)
- Memorial Sloan Kettering Cancer Center (US)
- University of Leeds (GB)
- Corresponding author
- Kather, Jakob Nikolas
- ORCID
- Venue
- Nature Communications
- Published
- 2024-02-10
- Volume
- 15
- Issue
- 1
- Pages
- 1253-1253
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC10858881
- OpenAlex
- W4391722054
- Cited by
- 108
- References
- 63
- Citations by year
- 2026: 31 · 2025: 53 · 2024: 23 · 2023: 1
- FWCI
- 15.76
- Type
- journal article
- Language
- en
- Update notices
- correction (2024-02-29)
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- HAL (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- RWTH Publications (RWTH Aachen) (submittedVersion, other-oa)
- arXiv (Cornell University) (cc-by)
- RWTH Publications (RWTH Aachen)
- Funders
- AstraZeneca
- Joachim Herz Stiftung
- National Institute for Health and Care Research (NIHR213331, BRC399/NS/RB/101410, CL-2019–19-001)
- Department of Health and Social Care
- European Commission (101096312, 101057091)
- Deutscher Akademischer Austauschdienst (57616814)
- Brain Tumour Charity (GN‐000389)
- Bundesministerium für Bildung und Forschung (01KD2215A, 01EO2101, 01VSF21048, 57616814, 01KD2104C, 031L0312A)
- Bundesministerium für Gesundheit (ZMVI1-2520DAT111, 2520DAT111)
- China Scholarship Council
- Deutsche Krebshilfe (#70113864, 2520DAT111)
- University College London Hospitals NHS Foundation Trust
- Gemeinsame Bundesausschuss (01VSF21048)
- Leeds Biomedical Research Centre (NIHR213331)
- National Cancer Institute (P30CA008748)
- Daiichi Sankyo Europe
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- tumor histology, self-supervised learning, attention-based models, homologous recombination deficiency, colorectal cancer prognosis, computational pathology, pan-cancer biomarker
- MeSH terms
- Deep Learning, Humans, Neoplasms, Technology, Biomarkers, Tumor, Tumor Microenvironment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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