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This makes them challenging and time-consuming to extract for secondary analyses, including training artificial intelligence (AI) models.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Materials and methods<\/jats:title>\n            <jats:p>This study presents a rule-based RAdiology Data EXtraction tool (RADEX) to enable biomedical researchers and healthcare professionals to automate information extraction from clinical documents. RADEX simplifies the translation of domain expertise into regular-expression models, enabling context-dependent searching without specialist expertise in Natural Language Processing. Its utility was demonstrated in the multi-label classification of fourteen clinical features in a large retrospective dataset (<jats:italic>n<\/jats:italic>\u2009=\u200916,246) of thyroid ultrasound reports from five hospitals in the United Kingdom (UK). A tuning subset (<jats:italic>n<\/jats:italic>\u2009=\u2009200) was used to iteratively develop the search strategy, and a holdout test subset (<jats:italic>n<\/jats:italic>\u2009=\u2009202) was used to evaluate the performance against reference-standard labels.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>The dataset cardinality was 3.06, and the label density was 0.34. Cohen\u2019s Kappa was 0.94 for rater 1 and 0.95 for rater 2. For RADEX, micro-average sensitivity, specificity, and F1-score were 0.97, 0.96, and 0.94, respectively. The processing time was 12.3 milliseconds per report, enabling fast and reliable information extraction.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusion<\/jats:title>\n            <jats:p>RADEX is a versatile tool for bespoke research and audit applications, where access to labelled data or computing infrastructure is limited, or explainability and reproducibility are priorities. This offers a time-saving and freely available option to accelerate structured data collection, enabling new insights and improved patient care.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Key Points<\/jats:title>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Question<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>Radiology reports contain vital information that is buried in unstructured free-text fields. Can we extract this information effectively for research and audit applications?<\/jats:italic>\n            <\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Findings<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>A rule-based RAdiology Data Extraction tool (RADEX) is described and used to classify fourteen key findings from thyroid ultrasound reports with sensitivity and specificity &gt;\u20090.95<\/jats:italic>.<\/jats:p>\n            <jats:p>\n              <jats:bold>\n                <jats:italic>Clinical relevance<\/jats:italic>\n              <\/jats:bold>\n              <jats:italic>RADEX offers clinicians and researchers a time-saving tool to accelerate structured data collection. This practical approach prioritises transparency, repeatability, and usability, enabling new insights into improved patient care<\/jats:italic>.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Graphical Abstract<\/jats:title>\n          <\/jats:sec>","DOI":"10.1007\/s00330-025-11416-4","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T12:50:27Z","timestamp":1739451027000},"page":"4506-4517","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["RADEX: a rule-based clinical and radiology data extraction tool demonstrated on thyroid ultrasound reports"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4807-4087","authenticated-orcid":false,"given":"Lewis","family":"Howell","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Zarei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tze Min","family":"Wah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"James H.","family":"Chandler","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shishir","family":"Karthik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zara","family":"Court","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Helen","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5795-4372","authenticated-orcid":false,"given":"James R.","family":"McLaughlan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,13]]},"reference":[{"key":"11416_CR1","doi-asserted-by":"publisher","first-page":"837","DOI":"10.1148\/radiol.2016161553","volume":"283","author":"DL Rubin","year":"2017","unstructured":"Rubin DL, Kahn CE (2017) Common data elements in radiology. 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