ArticleJCO clinical cancer informatics2019
Natural Language Processing for Automated Quantification of Brain Metastases Reported in Free-Text Radiology Reports.
Article in JCO clinical cancer informatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.
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- Natural Language Processing of Radiology Reports to Assess Survival in Patients with Advanced Melanoma.Cancers · 2025Article
- Using Generative AI to Extract Structured Information from Free Text Pathology Reports.Journal of medical systems · 2025Article
- Text mining of verbal autopsy narratives to extract mortality causes and most prevalent diseases using natural language processing.PloS one · 2024Article
- Automatic Detection of Distant Metastasis Mentions in Radiology Reports in Spanish.JCO clinical cancer informatics · 2024Article
- Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?The British journal of radiology · 2023Article
- Natural Language Processing and Graph Theory: Making Sense of Imaging Records in a Novel Representation Frame.JMIR medical informatics · 2022Article
- Assessment of Electronic Health Record for Cancer Research and Patient Care Through a Scoping Review of Cancer Natural Language Processing.JCO clinical cancer informatics · 2022Article
- Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.Acta neurochirurgica. Supplement · 2022Article
- Foundations of Machine Learning-Based Clinical Prediction Modeling: Part V-A Practical Approach to Regression Problems.Acta neurochirurgica. Supplement · 2022Article
- Developing a Cancer Digital Twin: Supervised Metastases Detection From Consecutive Structured Radiology Reports.Frontiers in artificial intelligence · 2022Article
- Deep learning to automate the labelling of head MRI datasets for computer vision applications.European radiology · 2022Article
- Foundations of Machine Learning-Based Clinical Prediction Modeling: Part IV-A Practical Approach to Binary Classification Problems.Acta neurochirurgica. Supplement · 2022Article
- Article
- Prediction of Stroke Outcome Using Natural Language Processing-Based Machine Learning of Radiology Report of Brain MRI.Journal of personalized medicine · 2020Article
- A Method for the Prediction of Clinical Outcome Using Diffusion Magnetic Resonance Imaging: Application on Parkinson's Disease.Journal of clinical medicine · 2020Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
purposeAlthough the bulk of patient-generated health data are increasing exponentially, their use is impeded because most data come in unstructured format, namely as free-text clinical reports. A variety of natural language processing (NLP) methods have emerged to automate the processing of free text ranging from statistical to deep learning-based models; however, the optimal approach for medical text analysis remains to be determined. The aim of this study was to provide a head-to-head comparison of novel NLP techniques and inform future studies about their utility for automated medical text analysis. PATIENTS AND
methodsMagnetic resonance imaging reports of patients with brain metastases treated in two tertiary centers were retrieved and manually annotated using a binary classification (single metastasis
resultsA total of 1,479 radiology reports of patients diagnosed with brain metastases were retrieved. The least absolute shrinkage and selection operator (LASSO) regression model demonstrated the best overall performance on the hold-out test set with an area under the receiver operating characteristic curve of 0.92 (95% CI, 0.89 to 0.94), accuracy of 83% (95% CI, 80% to 87%), calibration intercept of -0.06 (95% CI, -0.14 to 0.01), and calibration slope of 1.06 (95% CI, 0.95 to 1.17).
conclusionAmong various NLP techniques, the bag-of-words approach combined with a LASSO regression model demonstrated the best overall performance in extracting binary outcomes from free-text clinical reports. This study provides a framework for the development of machine learning-based NLP models as well as a clinical vignette of patients diagnosed with brain metastases.
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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.