{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T06:58:08Z","timestamp":1790233088207,"version":"4.1.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Dentomaxillofacial Radiology"],"published-print":{"date-parts":[[2023,1,1]]},"abstract":"<jats:sec>\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>To define which are and how the radiomics features of jawbone pathologies are extracted for diagnosis, predicting prognosis and therapeutic response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>A comprehensive literature search was conducted using eight databases and gray literature. Two independent observers rated these articles according to exclusion and inclusion criteria. 23 papers were included to assess the radiomics features related to jawbone pathologies. Included studies were evaluated by using JBI Critical Appraisal Checklist for Analytical Cross-Sectional Studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>Agnostic features were mined from periapical, dental panoramic radiographs, cone beam CT, CT and MRI images of six different jawbone alterations. The most frequent features mined were texture-, shape- and intensity-based features. Only 13 studies described the machine learning step, and the best results were obtained with Support Vector Machine and random forest classifier. For osteoporosis diagnosis and classification, filtering, shape-based and Tamura texture features showed the best performance. For temporomandibular joint pathology, gray-level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), Gray Level Size Zone Matrix (GLSZM), first-order statistics analysis and shape-based analysis showed the best results. Considering odontogenic and non-odontogenic cysts and tumors, contourlet and SPHARM features, first-order statistical features, GLRLM, GLCM had better indexes. For odontogenic cysts and granulomas, first-order statistical analysis showed better classification results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>GLCM was the most frequent feature, followed by first-order statistics, and GLRLM features. No study reported predicting response, prognosis or therapeutic response, but instead diseases diagnosis or classification. Although the lack of standardization in the radiomics workflow of the included studies, texture analysis showed potential to contribute to radiologists\u2019 reports, decreasing the subjectivity and leading to personalized healthcare.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1259\/dmfr.20220225","type":"journal-article","created":{"date-parts":[[2022,11,23]],"date-time":"2022-11-23T09:32:31Z","timestamp":1669195951000},"source":"Crossref","is-referenced-by-count":31,"title":["Radiomics in bone pathology of the jaws"],"prefix":"10.1093","volume":"52","author":[{"given":"Glaucia Nize Martins","family":"Santos","sequence":"first","affiliation":[{"name":"Dentistry Department, Faculty of Health Science, University of Bras\u00edlia, Brasilia, Brazil"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Helbert Eust\u00e1quio Cardoso","family":"da Silva","sequence":"additional","affiliation":[{"name":"Dentistry Department, Faculty of Health Science, University of 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