A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics
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Abstract
Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.
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AI-driven medical imaging analysis significantly improves the accuracy and efficiency of diagnostic processes.
“AI-driven medical imaging analysis has emerged as a pivotal tool in modern healthcare, significantly improving the accuracy and efficiency of diagnostic processes.66,74,76,77,78,79,80,81,82,83”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [77]
The Abstract (page not available in our copy) says AI analysis of pathology slides can extract clinically relevant information and reports accurate prediction of cancer treatment response. The Discussion (page not available) describes faster treatment decisions as a potential benefit requiring clinical validation. The paper does not establish that AI imaging significantly improves the accuracy and efficiency of diagnostic processes generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAI agents show significant potential in personalized treatment by leveraging patient-specific data to tailor individualized treatment plans that optimize therapeutic outcomes.
“AI agents show significant potential in the realm of personalized treatment by leveraging a variety of patient-specific data, including genetic information, medical history, lifestyle factors, and treatment responses.77,88,89,90,91,92”
From A foundational architecture for AI agents in healthcare (2025) · cites this paper as [77]
The Abstract (page not supplied) describes using tumor slides to infer gene expression and predict individual patients’ responses to cancer therapies, supporting the personalized-treatment potential. But this framework does not use the claimed range of patient data, particularly medical history or lifestyle factors; the Results explicitly note that it needs no other clinical features. The Discussion also says clinical use requires further validation, so improved therapeutic outcomes have not been established. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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- Authors
- Hoang, D.-T., Dinstag, G., Shulman, E. D., Hermida, L. C., Ben-Zvi, D. S., Elis, E., Caley, K., Sammut, S.-J., Sinha, S., Sinha, N., Dampier, C. H., Stossel, C., Patil, T., Rajan, A., Lassoued, W., Strauss, J., Bailey, S., Allen, C., Redman, J., Beker, T., Jiang, P., Golan, T., Wilkinson, S., Sowalsky, A. G., Pine, S. R., Caldas, C., Gulley, J. L., Aldape, K., Aharonov, R., Stone, E. A., & Ruppin, E.
- Affiliations
- Australian National University (AU)
- National Cancer Institute (US)
- University of Pittsburgh (US)
- Royal Marsden NHS Foundation Trust (GB)
- Tel Aviv University (IL)
- University of Colorado Anschutz (US)
- University of Cambridge (GB)
- National Institutes of Health (US)
- Corresponding author
- Hoang, Danh-Tai
- Stone, Eric A.
- Ruppin, Eytan
- ORCID
- Hoang, Danh-Tai
- Dinstag, Gal
- Shulman, Eldad D.
- Hermida, Leandro C.
- Ben-Zvi, Doreen S.
- Elis, Efrat
- Caley, Katherine
- Sammut, Stephen-John
- Sinha, Sanju
- Sinha, Neelam
- Dampier, Christopher H.
- Stossel, Chani
- Patil, Tejas
- Rajan, Arun
- Lassoued, Wiem
- Strauss, Julius
- Allen, Clint
- Redman, Jason
- Beker, Tuvik
- Jiang, Peng
- Golan, Talia
- Wilkinson, Scott
- Sowalsky, Adam G.
- Pine, Sharon R.
- Caldas, Carlos
- Gulley, James L.
- Aldape, Kenneth
- Stone, Eric A.
- Ruppin, Eytan
- Venue
- Nature Cancer
- Published
- 2024-07-03
- Volume
- 5
- Issue
- 9
- Pages
- 1305-1317
- ISSN
- 2662-1347 (ISSN-L)
- PMC
- PMC12413935
- OpenAlex
- W4400294055
- Cited by
- 157
- References
- 71
- Citations by year
- 2026: 71 · 2025: 65 · 2024: 19 · 2023: 2
- FWCI
- 22.87
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Cancer (publishedVersion)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (submittedVersion, cc-by)
- ANU Open Research (Australian National University) (submittedVersion)
- Zenodo (CERN European Organization for Nuclear Research)
- Funders
- Centre of Excellence for Electromaterials Science, Australian Research Council (DP190103402)
- 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 › Molecular Biology › Gene expression and cancer classification
- Keywords
- tumor response prediction, histopathological images, H&E staining, tumor mRNA expression, deep learning, targeted therapy response, immunotherapy response, precision oncology, TCGA
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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