Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer
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Abstract
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.
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Ogier du Terrail and colleagues presented a federated learning approach for predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.
“A recent study by Ogier du Terrail and colleagues (83) presented a federated learning approach for predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [83]
That approach used weakly supervised training to predict response from H&E whole slide images and interpretability methods identified features correlating with higher response predictions, such as TILs and necrosis.
“The approach included weakly supervised training to predict response from H&E whole slide images, with subsequent application of interpretability approaches to identify features that correlate with higher response predictions, such as the presence of tumor-infiltrating lymphocytes (TILs) and necrosis.”
From Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions (2024) · cites this paper as [83]
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Ki67 as a predictor of poor prognosis in patients with triple-negative breast cancer
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Nietner, T., Jarutat, T., & Mertens, A. (2012) · Archiv für Pathologische Anatomie und Physiologie und für Klinische Medicin · doi:10.1007/s00428-012-1248-5 · Cited by 40 · see citing works
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SAKUMA, K., Kurosumi, M., Oba, H., et al. (2011) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2011.212 · Cited by 32 · see citing works
Ahn, K. J., Park, J., & Choi, Y. (2017) · Radiation Oncology Journal · doi:10.3857/roj.2017.00416 · Cited by 32 · see citing works
Multi-Task Multi-Domain Learning for Digital Staining and Classification of Leukocytes
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Desa, D. E., Bhanote, M., Hill, R. L., Majeski, J. B., Buscaglia, B., D’Aguiar, M., Strawderman, R. L., Hicks, D. G., Turner, B. M., & Brown, E. B. (2019) · Journal of Biomedical Optics · doi:10.1117/1.jbo.24.8.086503 · Cited by 25 · see citing works
Privacy-first health research with federated learningPreprint
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Swarm learning for decentralized artificial intelligence in cancer histopathologyPreprint
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Extending Gossip Algorithms to Distributed Estimation of U-StatisticsPreprint
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Predicting Residual Cancer Burden In A Triple Negative Breast Cancer Cohort
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New threshold of ER positivity in early stage HER2- breast cancer.
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Flagged first, then by citations
Cited by
Sorted by citations
Federated learning for medical image analysis: A survey
Guan, H., Yap, P.-T., Bozoki, A., & Liu, M. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110424 · Cited by 518 · see citing works
Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions
Lotter, W., Hassett, M. J., Schultz, N., Kehl, K. L., Van Allen, E. M., & Cerami, E. (2024) · Cancer Discovery · doi:10.1158/2159-8290.cd-23-1199 · Cited by 196 · see citing works
Federated benchmarking of medical artificial intelligence with MedPerf
Karargyris, A., Umeton, R., Sheller, M. J., Aristizabal, A., George, J., Wuest, A., Pati, S., Kassem, H., Zenk, M., Baid, U., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00652-2 · Cited by 188 · see citing works
Ng, F. Y. C., Thirunavukarasu, A. J., Cheng, H., Tan, T. F., Gutierrez, L., Lan, Y., Ong, J. C. L., Chong, Y. S., Ngiam, K. Y., Ho, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101230 · Cited by 113 · see citing works
How will generative AI disrupt data science in drug discovery?
Vert, J.-P. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01789-6 · Cited by 73 · see citing works
Fisher, T. B., Saini, G., Rekha, T. S., Krishnamurthy, J., Bhattarai, S., Callagy, G., Webber, M., Janssen, E. A. M., Kong, J., & Aneja, R. (2024) · Breast Cancer Research · doi:10.1186/s13058-023-01752-y · Cited by 42 · see citing works
“Other recently published studies that have relied on WSI models [77, 78] include one that used a federated learning model to predict NAC response in TNBC, and found hemorrhage, TILs, and necrosis as predictive of pCR and apocrine change, fibrosis, and noncohesive tumor cells being predictive of RD…”
ResultOne label is all you need: Interpretable AI-enhanced histopathology for oncology
Tavolara, T. E., Su, Z., Gurcan, M. N., & Niazi, M. K. K. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.09.006 · Cited by 28 · see citing works
- Authors
- Ogier du Terrail, J., Leopold, A., Joly, C., Béguier, C., Andreux, M., Maussion, C., Schmauch, B., Tramel, E. W., Bendjebbar, E., Zaslavskiy, M., Wainrib, G., Milder, M., Gervasoni, J., Guerin, J., Durand, T., Livartowski, A., Moutet, K., Gautier, C., Djafar, I., Moisson, A.-L., Marini, C., Galtier, M., Balazard, F., Dubois, R., Moreira, J., Simon, A., Drubay, D., Lacroix-Triki, M., Franchet, C., Bataillon, G., & Heudel, P.-E.
- Affiliations
- Centre Léon Bérard (FR)
- Institut Curie (FR)
- Institut Gustave Roussy (FR)
- Institut universitaire du cancer de Toulouse Oncopole (FR)
- ORCID
- Ogier du Terrail, Jean
- Leopold, Armand
- Andreux, Mathieu
- Maussion, Charles
- Schmauch, Benoît
- Tramel, Eric W.
- Guerin, Julien
- Durand, Thierry
- Livartowski, Alain
- Marini, Camille
- Balazard, Félix
- Dubois, Rémy
- Moreira, Jeverson
- Drubay, Damien
- Lacroix-Triki, Magali
- Franchet, Camille
- Bataillon, Guillaume
- Heudel, Pierre-Etienne
- Venue
- Nature Medicine
- Published
- 2023-01
- Volume
- 29
- Issue
- 1
- Pages
- 135-146
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4317498947
- Semantic Scholar
- e0fa5ed16e07ce73be90fd93b98cd240f3995ee7
- Cited by
- 217
- References
- 110
- Citations by year
- 2026: 50 · 2025: 68 · 2024: 58 · 2023: 38 · 2022: 3
- FWCI
- 20.15
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Medicine (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nd)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- Funders
- Owkin Inc.
- Institut Curie
- Centre Léon Bérard
- Institut Gustave Roussy
- Oncopole Toulouse
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- triple-negative breast cancer, neoadjuvant chemotherapy, federated learning, whole slide images, machine learning, data privacy, interpretable models, histological patterns, treatment response biomarkers
- MeSH terms
- Antineoplastic Combined Chemotherapy Protocols, Female, Humans, Treatment Outcome, Neoadjuvant Therapy, Triple Negative Breast Neoplasms
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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