ArticleJournal of the American Medical Informatics Association : JAMIA
Automatic classification of mammography reports by BI-RADS breast tissue composition class.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Extracting cancer concepts from clinical notes using natural language processing: a systematic review.BMC bioinformatics · 2023Pooled it
- Range of Normalized Glandular Dose for Mammography Using Patient-Specific Glandular Fractions.ArXiv · 2026Article
- Maximum-likelihood estimation of glandular fraction for mammography and its effect on microcalcification detection.Physical and engineering sciences in medicine · 2025Article
- TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.BMC medical informatics and decision making · 2024Article
- Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma.Journal of digital imaging · 2023Article
- Machine learning-driven clinical decision support system for concept-based searching: a field trial in a Norwegian hospital.BMC medical informatics and decision making · 2023Article
- A scoping review of natural language processing of radiology reports in breast cancer.Frontiers in oncology · 2023Article
- Multi-label annotation of text reports from computed tomography of the chest, abdomen, and pelvis using deep learning.BMC medical informatics and decision making · 2022Article
- Automatic Classification of Thyroid Findings Using Static and Contextualized Ensemble Natural Language Processing Systems: Development Study.JMIR medical informatics · 2021Article
- Natural Language Processing and Machine Learning for Identifying Incident Stroke From Electronic Health Records: Algorithm Development and Validation.Journal of medical Internet research · 2021Article
- A decision support system for mammography reports interpretation.Health information science and systems · 2020Article
- Identification of patients with carotid stenosis using natural language processing.European radiology · 2020Article
- The implementation of natural language processing to extract index lesions from breast magnetic resonance imaging reports.BMC medical informatics and decision making · 2019Article
- Automatic inference of BI-RADS final assessment categories from narrative mammography report findings.Journal of biomedical informatics · 2019Article
- A Scalable Machine Learning Approach for Inferring Probabilistic US-LI-RADS Categorization.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2018Article
- Automated annotation and classification of BI-RADS assessment from radiology reports.Journal of biomedical informatics · 2017Article
- Using automatically extracted information from mammography reports for decision-support.Journal of biomedical informatics · 2016Article
- Observed outcomes on the use of oxidized and regenerated cellulose polymer for breast conserving surgery - A case series.Annals of medicine and surgery (2012) · 2016Article
- Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy.Academic radiology · 2016Article
- Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.Journal of digital imaging · 2015Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Because breast tissue composition partially predicts breast cancer risk, classification of mammography reports by breast tissue composition is important from both a scientific and clinical perspective. A method is presented for using the unstructured text of mammography reports to classify them into BI-RADS breast tissue composition categories. An algorithm that uses regular expressions to automatically determine BI-RADS breast tissue composition classes for unstructured mammography reports was developed. The algorithm assigns each report to a single BI-RADS composition class: 'fatty', 'fibroglandular', 'heterogeneously dense', 'dense', or 'unspecified'. We evaluated its performance on mammography reports from two different institutions. The method achieves >99% classification accuracy on a test set of reports from the Marshfield Clinic (Wisconsin) and Stanford University. Since large-scale studies of breast cancer rely heavily on breast tissue composition information, this method could facilitate this research by helping mine large datasets to correlate breast composition with other covariates.
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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.