{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:21:13Z","timestamp":1761394873483},"reference-count":34,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Appl Clin Inform"],"published-print":{"date-parts":[[2016,7]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>A structured report for imaging exams aims at increasing the precision in information retrieval and communication between physicians. However, it is more concise than free text and may limit specialists\u2019 descriptions of important findings not covered by pre-defined structures. A computational ontological structure derived from free texts designed by specialists may be a solution for this problem. Therefore, the goal of our study was to develop a methodology for structuring information in radiology reports covering specifications required for the Brazilian Portuguese language, including the terminology to be used.<\/jats:p><jats:p>We gathered 1,701 radiological reports of magnetic resonance imaging (MRI) studies of the lumbosacral spine from three different institutions. Techniques of text mining and ontological conceptualization of lexical units extracted were used to structure information. Ten radiologists, specialists in lumbosacral MRI, evaluated the textual superstructure and terminology extracted using an electronic questionnaire.<\/jats:p><jats:p>The established methodology consists of six steps: 1) collection of radiology reports of a specific MRI examination; 2) textual decomposition; 3) normalization of lexical units; 4) identification of textual superstructures; 5) conceptualization of candidate-terms; and 6) evaluation of superstructures and extracted terminology by experts using an electronic questionnaire. Three different textual superstructures were identified, with terminological variations in the names of their textual categories. The number of candidate-terms conceptualized was 4,183, yielding 727 concepts. There were a total of 13,963 relationships between candidate-terms and concepts and 789 relationships among concepts.<\/jats:p><jats:p>The proposed methodology allowed structuring information in a more intuitive and practical way. Indications of three textual superstructures, extraction of lexicon units and the normalization and ontologically conceptualization were achieved while maintaining references to their respective categories and free text radiology reports.<\/jats:p><jats:p>Citation: Barbosa F, Traina AJ, Muglia VF. Meta-generalis: A novel method for structuring information from radiology reports.<\/jats:p>","DOI":"10.4338\/aci-2016-03-ra-0037","type":"journal-article","created":{"date-parts":[[2016,8,24]],"date-time":"2016-08-24T09:38:16Z","timestamp":1472031496000},"page":"803-816","source":"Crossref","is-referenced-by-count":5,"title":["Meta-generalis: A novel method for structuring information from radiology reports"],"prefix":"10.4338","volume":"07","author":[{"given":"Flavio","family":"Barbosa","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Agma","family":"Traina","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valdair","family":"Muglia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"194","published-online":{"date-parts":[[2017,12,19]]},"reference":[{"key":"10.4338\/ACI-2016-03-RA-0037-1","doi-asserted-by":"crossref","unstructured":"Brentari D. 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