ArticleJournal of digital imaging2019
Automated Detection of Measurements and Their Descriptors in Radiology Reports Using a Hybrid Natural Language Processing Algorithm.
Article in Journal of digital imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 48 citations in OpenAlex.
- A systematic review of natural language processing applied to radiology reports.BMC medical informatics and decision making · 2021Pooled it
- Relevance of advanced imaging analysis units in radiology departments: a narrative review.International journal of computer assisted radiology and surgery · 2026Review
- Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.Journal of imaging informatics in medicine · 2026Article
- Automated Radiology Report Labeling in Chest X-Ray Pathologies: Development and Evaluation of a Large Language Model Framework.JMIR medical informatics · 2025Article
- The clinical application potential assessment of the Deepseek-R1 large language model in lung cancer.Frontiers in oncology · 2025Article
- Efficient labeling of french mammogram reports with MammoBERT.Scientific reports · 2024Article
- Turning radiology reports into epidemiological data to track seasonal pulmonary infections and the COVID-19 pandemic.European radiology · 2024Article
- Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition.JCO clinical cancer informatics · 2024Article
- Article
- Semiautomated pelvic lymph node treatment response evaluation for patients with advanced prostate cancer: based on MET-RADS-P guidelines.Cancer imaging : the official publication of the International Cancer Imaging Society · 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
- Fine-grained spatial information extraction in radiology as two-turn question answering.International journal of medical informatics · 2022Article
- Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing.Radiology. Artificial intelligence · 2022Article
- Use of BERT (Bidirectional Encoder Representations from Transformers)-Based Deep Learning Method for Extracting Evidences in Chinese Radiology Reports: Development of a Computer-Aided Liver Cancer Diagnosis Framework.Journal of medical Internet research · 2021Article
- Deep Learning to Estimate RECIST in Patients with NSCLC Treated with PD-1 Blockade.Cancer discovery · 2021Article
- RadLex Normalization in Radiology Reports.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Radiological measurements are reported in free text reports, and it is challenging to extract such measures for treatment planning such as lesion summarization and cancer response assessment. The purpose of this work is to develop and evaluate a natural language processing (NLP) pipeline that can extract measurements and their core descriptors, such as temporality, anatomical entity, imaging observation, RadLex descriptors, series number, image number, and segment from a wide variety of radiology reports (MR, CT, and mammogram). We created a hybrid NLP pipeline that integrates rule-based feature extraction modules and conditional random field (CRF) model for extraction of the measurements from the radiology reports and links them with clinically relevant features such as anatomical entities or imaging observations. The pipeline was trained on 1117 CT/MR reports, and performance of the system was evaluated on an independent set of 100 expert-annotated CT/MR reports and also tested on 25 mammography reports. The system detected 813 out of 806 measurements in the CT/MR reports; 784 were true positives, 29 were false positives, and 0 were false negatives. Similarly, from the mammography reports, 96% of the measurements with their modifiers were extracted correctly. Our approach could enable the development of computerized applications that can utilize summarized lesion measurements from radiology report of varying modalities and improve practice by tracking the same lesions along multiple radiologic encounters.
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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.