CancerBERT: A cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

Zhou, Sicheng; Wang, Nan; Wang, Liwei; Liu, Hongfang; Zhang, Rui

doi:10.1093/jamia/ocac040

Cited by 119Machine Learning in HealthcareBiomedical Text Mining and OntologiesTopic Modeling

CancerBERT: A cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

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Zhou, Sicheng, Wang, Nan, Wang, Liwei, Liu, Hongfang, Zhang, Rui·2022·Journal of the American Medical Informatics Association

DOI 10.1093/jamia/ocac040PMC PMC9196678

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Abstract

Objective: Accurate extraction of breast cancer patients’ phenotypes is important for clinical decision support and clinical research. This study developed and evaluated cancer domain pretrained CancerBERT models for extracting breast cancer phenotypes from clinical texts. We also investigated the effect of customized cancer-related vocabulary on the performance of CancerBERT models. Materials and Methods: A cancer-related corpus of breast cancer patients was extracted from the electronic health records of a local hospital. We annotated named entities in 200 pathology reports and 50 clinical notes for 8 cancer phenotypes for fine-tuning and evaluation. We kept pretraining the BlueBERT model on the cancer corpus with expanded vocabularies (using both term frequency-based and manually reviewed methods) to obtain CancerBERT models. The CancerBERT models were evaluated and compared with other baseline models on the cancer phenotype extraction task. Results: All CancerBERT models outperformed all other models on the cancer phenotyping NER task. Both CancerBERT models with customized vocabularies outperformed the CancerBERT with the original BERT vocabulary. The CancerBERT model with manually reviewed customized vocabulary achieved the best performance with macro F1 scores equal to 0.876 (95% CI, 0.873–0.879) and 0.904 (95% CI, 0.902–0.906) for exact match and lenient match, respectively. Conclusions: The CancerBERT models were developed to extract the cancer phenotypes in clinical notes and pathology reports. The results validated that using customized vocabulary may further improve the performances of domain specific BERT models in clinical NLP tasks. The CancerBERT models developed in the study would further help clinical decision support.

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Zhou et al. found that pre-training BERT on a cancer-specific dataset substantially improved model performance for extracting breast cancer phenotypes from pathology reports and clinical records.
“Their findings underscore the significance of domain-specific pre-training, as it substantially improved the performance of the model, making it more attuned to the nuances of cancer-related data.”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [105]
Zhou et al. [105] achieved macro-F1 of 0.876 for exact match and 0.904 for lenient match.
“| Zhou et al. [105] | mutil feature | Text | private dataset | exact match and lenient match, macro-F1: 0.876, 0.904 |”
From A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis (2024) · cites this paper as [105]
Deep learning models have demonstrated superior performance across diverse NLP tasks, including data extraction.
“has been particularly effective at modeling the hierarchical structure of natural language and demonstrated superior performance across diverse NLP tasks, including but not limited to data extraction [3].”
From Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review (2025) · cites this paper as [3]

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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works
Untitled work
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533 · see citing works
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218 · see citing works
Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation
Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., Macherey, K., et al. (2016) · arXiv · doi:10.48550/arxiv.1609.08144 · Cited by 5,319 · see citing works
Breast cancer statistics, 2017, racial disparity in mortality by state
Desantis, C. E., Ma, J., Sauer, A. G., Newman, L. A., & Jemal, A. (2017) · CA A Cancer Journal for Clinicians · doi:10.3322/caac.21412 · Cited by 1,879 · see citing works
Precision medicine for metastatic breast cancer—limitations and solutions
Arnedos, M., Vicier, C., Loi, S., Lefèbvre, C., Michiels, S., Bonnefoi, H. R., & André, F. (2015) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2015.123 · Cited by 317 · see citing works
Portability of an algorithm to identify rheumatoid arthritis in electronic health records
Carroll, R. J., Thompson, W. K., Eyler, A. E., Mandelin, A. M., Cai, T., Zink, R., Pacheco, J. A., Boomershine, C. S., Lasko, T. A., Xu, H., et al. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000583 · Cited by 238 · see citing works
Extracting comprehensive clinical information for breast cancer using deep learning methods
Zhang, X., Zhang, Y., Zhang, Q., Ren, Y., Qiu, T., Ma, J., & Sun, Q. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.103985 · Cited by 160 · see citing works
Clinical concept extraction using transformers
Yang, X., Bian, J., Hogan, W. R., & Wu, Y. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa189 · Cited by 155 · see citing works
Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports
Qiu, J. X., Yoon, H., Fearn, P., & Tourassi, G. D. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2700722 · Cited by 146 · see citing works
A study of deep learning approaches for medication and adverse drug event extraction from clinical text
Wei, Q., Ji, Z., Li, Z., Du, J., Wang, J., Xu, J., Xiang, Y., Tiryaki, F., Wu, S., Zhang, Y., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz063 · Cited by 129 · see citing works
Using machine learning to parse breast pathology reports
Yala, A., Barzilay, R., Salama, L., Griffin, M., Sollender, G. E., Bardia, A., Lehman, C. D., Buckley, J. M., Coopey, S. B., Polubriaginof, F. C. G., et al. (2016) · Breast Cancer Research and Treatment · doi:10.1007/s10549-016-4035-1 · Cited by 114 · see citing works
DeepPhe: A Natural Language Processing System for Extracting Cancer Phenotypes from Clinical Records
Savova, G., Tseytlin, E., Finan, S., Castine, M., Miller, T. A., Medvedeva, O., Harris, D., Hochheiser, H., Lin, C., Chavan, G., et al. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0615 · Cited by 101 · see citing works
Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python
Eyre, H., Chapman, A. B., Peterson, K., Shi, J., Alba, P. R., Jones, M., Box, T. L., DuVall, S. L., & Patterson, O. V. (2021) · PubMed Central · doi:10.48550/arxiv.2106.07799 · Cited by 81 · see citing works
Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking DatasetsPreprint
Peng, Y., Yan, S., & Lu, Z. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05474 · Cited by 72 · see citing works
Precision medicine in breast cancer: reality or utopia?
Bettaı̈eb, A., Paul, C., Plenchette, S., Shan, J., Chouchane, L., & Ghiringhelli, F. (2017) · Journal of Translational Medicine · doi:10.1186/s12967-017-1239-z · Cited by 69 · see citing works
Toward precision medicine of breast cancer
Carels, N., Spinassé, L. B., Tilli, T. M., & Tuszyński, J. A. (2016) · Theoretical Biology and Medical Modelling · doi:10.1186/s12976-016-0035-4 · Cited by 64 · see citing works
Natural language processing for populating lung cancer clinical research data
Wang, L., Luo, L., Wang, Y., Wampfler, J. A., Yang, P., & Liu, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0931-8 · Cited by 41 · see citing works
Self-citation
SciBERT: A Pretrained Language Model for Scientific Text
Beltagy, I., Lo, K., & Cohan, A. (2019) · arXiv · doi:10.48550/arxiv.1903.10676 · Cited by 41 · see citing works
Assessing the Utility of Automatic Cancer Registry Notifications Data Extraction from Free-Text Pathology Reports.
Nguyen, A. N., Moore, J., O'Dwyer, J., & Philpot, S. P. (2015) · PubMed Central · Cited by 39 · see citing works
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Du, J., Xiang, Y., Sankaranarayanapillai, M., Zhang, M., Wang, J., Si, Y., Pham, H. A., Xu, H., Chen, Y., & Tao, C. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab014 · Cited by 34 · see citing works
Coarse-to-fine multi-task training of convolutional neural networks for automated information extraction from cancer pathology reports
Alawad, M., Yoon, H., & Tourassi, G. D. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333408 · Cited by 26 · see citing works
Deep learning approaches for extracting adverse events and indications of dietary supplements from clinical text
Fan, Y., Zhou, S., Li, Y., & Zhang, R. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa218 · Cited by 26 · see citing works
Self-citation
CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From CharactersPreprint
Boukkouri, H. E., Ferret, O., Lavergne, T., Noji, H., Zweigenbaum, P., & Tsujii, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.10392 · Cited by 26 · see citing works
Electronic Health Record Phenotypes for Precision Medicine: Perspectives and Caveats From Treatment of Breast Cancer at a Single Institution
Breitenstein, M. K., Liu, H., Maxwell, K. N., Pathak, J., & Zhang, R. (2017) · Clinical and Translational Science · doi:10.1111/cts.12514 · Cited by 22 · see citing works
Self-citation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805

Flagged first, then by citations

Research works printed but not linked: 6

[7]Zhou S. Extracting phenotypes of cancer patients from electronic health records. In: 2019 IEEE international conference on healthcare informatics (ICHI); June 10, 2019; IEEE, Xi'an China; pp. 1– 2.
[18]Gu Y, Tinn R, Cheng H, et al Domain-specific language model pretraining for biomedical natural language processing. arXiv preprint arXiv:2007.15779, July 31, 2020. · arXiv
[22]Ma W, Cui Y, Si C, Liu T, Wang S, Hu G. CharBERT: character-aware pre-trained language model. arXiv preprint arXiv:2011.01513, November 3, 2020. · arXiv
[25]Klie J, Bugert M, Boullosa B, Eckart de Castilho R, Gurevych I. The inception platform: machine-assisted and knowledge-oriented interactive annotation. In: proceedings of the 27th international conference on computational linguistics: system demonstrations; August 20, 2018; Santa Fe, New Mexico; pp. 5– 9.
[28]Ritter A, Clark S, Etzioni O. Named entity recognition in tweets: an experimental study. In: proceedings of the 2011 conference on empirical methods in natural language processing; July 27, 2011; Edinburgh, UK; pp. 1524– 34.
[31]Pennington J, Socher R, Manning C. Glove: global vectors for word representation. In: proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP); October 25, 2014; Doha, Qatar; pp. 1532– 43.

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Paraschiv, E.-A., Cîrnu, C. E., & VEVERA, A. V. (2024) · Biomedical engineering · doi:10.5772/intechopen.1007041 · Cited by 8 · see citing works
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Gondara, L., Simkin, J., Arbour, G., Devji, S., & Ng, R. T. (2024) · JCO Clinical Cancer Informatics · doi:10.1200/cci.24.00110 · Cited by 8 · see citing works
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Mehan, N., Desinghe, T. D., & Saha, A. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000980 · Cited by 7 · see citing works
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He, G., & Huang, C. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129752 · Cited by 7 · see citing works
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Dahl, S. C., Bøgsted, M., Sagi, T., & Vesteghem, C. (2025) · JMIR Medical Informatics · doi:10.2196/68707 · Cited by 6 · see citing works
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Li, M., Zhan, Z., Huang, J., Yeung, J., Ding, K., Blaes, A. H., Johnson, S., Liu, H., Xu, H., & Zhang, R. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02441-8 · Cited by 32 · see citing works
Self-citation
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Kaufmann, B., Busby, D., Das, C. K., Tillu, N., Menon, M., Tewari, A. K., & Gorin, M. A. (2024) · European Urology Focus · doi:10.1016/j.euf.2024.01.009 · Cited by 6 · see citing works
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Lilli, L., Santoro, M., Masiello, V., Patarnello, S., Tagliaferri, L., Marazzi, F., & Capocchiano, N. D. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02994-w · Cited by 6 · see citing works
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Paolo, D., Greco, C., Cortellini, A., Ramella, S., Soda, P., Bria, A., & Sicilia, R. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02998-6 · Cited by 6 · see citing works
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Das, R., Maheswari, K., Siddiqui, B. S., Arora, N., Paul, A., Nanshi, J., Udbalkar, V., Sarvade, A., Chaturvedi, H., Shvartsman, T., et al. (2024) · medRxiv · doi:10.1101/2024.09.20.24314076 · Cited by 6 · see citing works
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Alkalbani, A. M., Alrawahi, A. S., Salah, A., Haghighi, V., Zhang, Y., Alkindi, S. S. A., & Sheng, Q. Z. (2025) · Research Square · doi:10.21203/rs.3.rs-5128451/v2 · Cited by 6 · see citing works
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Watabe, S., Watanabe, T., Yada, S., Aramaki, E., Yajima, H., Kizaki, H., & Hori, S. (2024) · PLoS ONE · doi:10.1371/journal.pone.0305496 · Cited by 5 · see citing works
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Tan, J., Zhang, H., Yang, J., Liu, Y., Zheng, D., & Liu, X. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113706 · Cited by 5 · see citing works
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Lu, Y., Srinivasan, G., Preum, S. M., Pettus, J. R., Davis, M., Greenburg, J., Vaickus, L. J., & Levy, J. (2023) · medRxiv · doi:10.1101/2023.09.10.23295318 · Cited by 5 · see citing works
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Pradeepa, S., Ramya, K., Vimal, S., Subbulakshmi, P., Dhiman, G., Rashid, T. A., & Aladdin, A. M. (2025) · Smart Health · doi:10.1016/j.smhl.2025.100598 · Cited by 4 · see citing works
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Danish, A. A. K., & Chatterjee, S. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130068 · Cited by 4 · see citing works
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Self-citation
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Scoarta, S., Küçükosmanoğlu, A., Bindt, F., Pouwer, M. G., & Westerman, B. A. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00096 · Cited by 1 · see citing works
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Chung, Y. G., Cho, J., Kim, Y. H., Kim, H. W., Kim, H., Koo, Y. S., Lee, S.-Y., & Shon, Y.-M. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1521001 · Cited by 1 · see citing works
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Alkalbani, A. M., Alrawahi, A. S., Salah, A., Haghighi, V., Zhang, Y., Alkindi, S. S. A., & Sheng, Q. Z. (2024) · Research Square · doi:10.21203/rs.3.rs-5128451/v1 · Cited by 1 · see citing works
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Yu, A., Weile, J., & Courtot, M. (2026) · medRxiv · doi:10.64898/2026.01.19.26344287 · Cited by 2 · see citing works

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Zhou, S., Wang, L., Wang, N., Liu, H., & Zhang, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2108.11303 · Cited by 2 · see citing works
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Duong, T. T., & Thieu, T. (2024) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi62660.2024.10913931 · Cited by 1 · see citing works
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Faiz, A. B., Shehzad, A. K., Afzal, A., Tariq, M., Siddiqi, M. H., Shahid, M. U., Awan, M. N., & Farooq, M. (2026) · arXiv (Cornell University) · doi:10.48550/arxiv.2604.06208
Use of Natural Language Processing to Extract Clinical Cancer Phenotypes from Electronic Medical Records
Savova, G. K., Danciu, I., Alamudun, F., Miller, T., Lin, C., Bitterman, D. S., Tourassi, G., & Warner, J. L. (2019) · Cancer Research · doi:10.1158/0008-5472.can-19-0579 · Cited by 170 · see citing works

Authors: Zhou, S., Wang, N., Wang, L., Liu, H., & Zhang, R.
Affiliations: University of Minnesota (US)
Mayo Clinic (US)
Corresponding author: Zhang, Rui
ORCID: Zhou, Sicheng
Wang, Nan
Wang, Liwei
Liu, Hongfang
Zhang, Rui
Venue: Journal of the American Medical Informatics Association
Publisher: Oxford University Press (OUP)
Published: 2022-03-25
Volume: 29
Issue: 7
Pages: 1208-1216
ISSN: 1067-5027 (ISSN-L)
1527-974X
DOI: 10.1093/jamia/ocac040
PMC: PMC9196678
OpenAlex: W4220989182
Cited by: 119
References: 21
Citations by year: 2026: 19 · 2025: 43 · 2024: 32 · 2023: 22 · 2022: 3
FWCI: 8.23
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by-nc-nd
creativecommons.org/licenses/by-nc-nd/4.0/ (vor)
Locations: Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc-nd)
PubMed Central (publishedVersion, cc-by-nc-nd)
PubMed (publishedVersion)
Funders: National Center for Complementary and Integrative Health (R01AT009457)
University of Minnesota Clinical and Translational Science Institute
National Center for Advancing Translational Sciences (UL1TR002494)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: breast cancer phenotypes, named entity recognition, clinical text, electronic health records, domain-specific language models, pathology reports, clinical notes, clinical decision support
MeSH terms: Breast Neoplasms, Female, Humans, Language, Natural Language Processing, Phenotype, Electronic Health Records

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CancerBERT: A cancer domain-specific language model for extracting breast cancer phenotypes from electronic health records

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