ArticleStudies in health technology and informatics2022
Natural Language Processing to Identify Abnormal Breast, Lung, and Cervical Cancer Screening Test Results from Unstructured Reports to Support Timely Follow-up.
Article in Studies in health technology and informatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Trial
- Inclusion of language preference in clinical o ne-Liners and its association with use and documentation of interpreter services among inpatients with non-English language preference.BMC health services research · 2026Article
- Bootstrapping BI-RADS classification using large language models and transformers in breast magnetic resonance imaging reports.Visual computing for industry, biomedicine, and art · 2025Article
- Using REDCap to Address Gaps in Electronic Health Record Data for Strengthening Colorectal Cancer Screening and Navigation.Advances in health information science and practice · 2025Article
- Natural Language Processing for Breast Imaging: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- A scoping review of natural language processing of radiology reports in breast cancer.Frontiers in oncology · 2023Article
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
Cancer screening and timely follow-up of abnormal results can reduce mortality. One barrier to follow-up is the failure to identify abnormal results. While EHRs have coded results for certain tests, cancer screening results are often stored in free-text reports, which limit capabilities for automated decision support. As part of the multilevel Follow-up of Cancer Screening (mFOCUS) trial, we developed and implemented a natural language processing (NLP) tool to assist with real-time detection of abnormal cancer screening test results (including mammograms, low-dose chest CT scans, and Pap smears) and identification of gynecological follow-up for higher risk abnormalities (i.e. colposcopy) from free-text reports. We demonstrate the integration and implementation of NLP, within the mFOCUS system, to improve the follow-up of abnormal cancer screening results in a large integrated healthcare system. The NLP pipelines have detected scenarios when guideline-recommended care was not delivered, in part because the provider mis-identified the text-based result reports.
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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.