{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:32:33Z","timestamp":1777509153534,"version":"3.51.4"},"reference-count":17,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Acta Radiol"],"published-print":{"date-parts":[[2023,6]]},"abstract":"\n Background<\/jats:title>\n Incidental imaging findings (incidentalomas) are common, but there is currently no effective means to investigate their clinical relevance.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To introduce a new concept to postprocess a medical imaging examination in a way that incidentalomas are concealed while its diagnostic potential is maintained to answer the referring physician's clinical questions.<\/jats:p>\n <\/jats:sec>\n \n Material and Methods<\/jats:title>\n A deep learning algorithm was developed to automatically eliminate liver, gallbladder, pancreas, spleen, adrenal glands, lungs, and bone from unenhanced computed tomography (CT). This deep learning algorithm was applied to a separately held set of unenhanced CT scans of 27 patients who underwent CT to evaluate for urolithiasis, and who had a total of 32 incidentalomas in one of the aforementioned organs.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Median visual scores for organ elimination on modified CT were 100% for the liver, gallbladder, spleen, and right adrenal gland, 90%\u201399% for the pancreas, lungs, and bones, and 80%\u201389% for the left adrenal gland. In 26 out of 27 cases (96.3%), the renal calyces and pelves, ureters, and urinary bladder were completely visible on modified CT. In one case, a short (<1\u2005cm) trajectory of the left ureter was not clearly visible due to adjacent atherosclerosis that was mistaken for bone by the algorithm. Of 32 incidentalomas, 28 (87.5%) were completely concealed on modified CT.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n This preliminary technical report demonstrated the feasibility of a new approach to postprocess and evaluate medical imaging examinations that can be used by future prospective research studies with long-term follow-up to investigate the clinical relevance of incidentalomas.<\/jats:p>\n <\/jats:sec>","DOI":"10.1177\/02841851231158769","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T19:46:58Z","timestamp":1682711218000},"page":"2170-2179","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?"],"prefix":"10.1177","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9005-8529","authenticated-orcid":false,"given":"Thomas C","family":"Kwee","sequence":"first","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Roest","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00d6mer","family":"Kasalak","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan P","family":"Pennings","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Igle Jan","family":"de Jong","sequence":"additional","affiliation":[{"name":"Department of Urology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4920-9441","authenticated-orcid":false,"given":"Derya","family":"Yakar","sequence":"additional","affiliation":[{"name":"Medical Imaging Center, Departments of Radiology, University of Groningen, University Medical Center Groningen, Groningen, the 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