ArticleScientific reports2021
Weakly supervised temporal model for prediction of breast cancer distant recurrence.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evolution of Breast Cancer Recurrence Risk Prediction: A Systematic Review of Statistical and Machine Learning-Based Models.JCO clinical cancer informatics · 2023Pooled it
- Review
- Integrating a Shareable Artificial Intelligence Model Into Clinical Research for Cancer Recurrence in Patients With Breast and Colorectal Cancer.JCO clinical cancer informatics · 2025Article
- Natural language processing for local, regional, and distant breast cancer relapse identification in pathology reports.Breast cancer research and treatment · 2025Article
- Artificial intelligence methods applied to longitudinal data from electronic health records for prediction of cancer: a scoping review.BMC medical research methodology · 2025Article
- Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A Review.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.Current oncology (Toronto, Ont.) · 2024Article
- Development of an Automatic Rule-Based Algorithm for the Detection of Ovarian Cancer Recurrence From Electronic Health Records.JCO clinical cancer informatics · 2024Article
- Artificial intelligence across oncology specialties: current applications and emerging tools.BMJ oncology · 2024Review
- A Traumatic Brain Injury Prescreening Tool for Intimate Partner Violence Patients Using Initial Clinical Reports and Machine Learning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2024Article
- Time series radiomics for the prediction of prostate cancer progression in patients on active surveillance.European radiology · 2023Article
- Machine learning survival models trained on clinical data to identify high risk patients with hormone responsive HER2 negative breast cancer.Scientific reports · 2023Article
- Natural Language Processing for Breast Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Systematic Review of Application Progress on Machine Learning-Based Natural Language Processing in Breast Cancer over the Past 5 Years.Diagnostics (Basel, Switzerland) · 2023Review
- Natural Language Processing Applications for Computer-Aided Diagnosis in Oncology.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches have been developed for recurrence prediction in previous studies, but most of them use only structured electronic health records and only a small training dataset, with limited success in clinical application. While free-text clinic notes may offer the greatest nuance and detail about a patient's clinical status, they are largely excluded in previous predictive models due to the increase in processing complexity and need for a complex modeling framework. In this study, we developed a weak-supervision framework for breast cancer recurrence prediction in which we trained a deep learning model on a large sample of free-text clinic notes by utilizing a combination of manually curated labels and NLP-generated non-perfect recurrence labels. The model was trained jointly on manually curated data from 670 patients and NLP-curated data of 8062 patients. It was validated on manually annotated data from 224 patients with recurrence and achieved 0.94 AUROC. This weak supervision approach allowed us to learn from a larger dataset using imperfect labels and ultimately provided greater accuracy compared to a smaller hand-curated dataset, with less manual effort invested in curation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.