{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:44:41Z","timestamp":1783010681544,"version":"3.54.6"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T00:00:00Z","timestamp":1621209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"The Swedish state under the agreement between the Swedish government and the country councils; the ALF-agreement (70380)."},{"name":"Bor\u00e5s Cancer Foundation"},{"DOI":"10.13039\/501100005760","name":"University of Gothenburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005760","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>To develop an artificial intelligence (AI)-based method for the detection of focal skeleton\/bone marrow uptake (BMU) in patients with Hodgkin\u2019s lymphoma (HL) undergoing staging with FDG-PET\/CT. The results of the AI in a separate test group were compared to the interpretations of independent physicians. The skeleton and bone marrow were segmented using a convolutional neural network. The training of AI was based on 153 un-treated patients. Bone uptake significantly higher than the mean BMU was marked as abnormal, and an index, based on the total squared abnormal uptake, was computed to identify the focal uptake. Patients with an index above a predefined threshold were interpreted as having focal uptake. As the test group, 48 un-treated patients who had undergone a staging FDG-PET\/CT between 2017\u20132018 with biopsy-proven HL were retrospectively included. Ten physicians classified the 48 cases regarding focal skeleton\/BMU. The majority of the physicians agreed with the AI in 39\/48 cases (81%) regarding focal skeleton\/bone marrow involvement. Inter-observer agreement between the physicians was moderate, Kappa 0.51 (range 0.25\u20130.80). An AI-based method can be developed to highlight suspicious focal skeleton\/BMU in HL patients staged with FDG-PET\/CT. Inter-observer agreement regarding focal BMU is moderate among nuclear medicine physicians.<\/jats:p>","DOI":"10.1038\/s41598-021-89656-9","type":"journal-article","created":{"date-parts":[[2021,5,17]],"date-time":"2021-05-17T10:04:53Z","timestamp":1621245893000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Artificial intelligence could alert for focal skeleton\/bone marrow uptake in Hodgkin\u2019s lymphoma patients staged with FDG-PET\/CT"],"prefix":"10.1038","volume":"11","author":[{"given":"May","family":"Sadik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas","family":"L\u00f3pez-Urdaneta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Ul\u00e9n","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olof","family":"Enqvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Armin","family":"Krupic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajender","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Per-Ola","family":"Andersson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elin","family":"Tr\u00e4g\u00e5rdh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,5,17]]},"reference":[{"key":"89656_CR1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1111\/bjh.15933","volume":"186","author":"MA Pedersen","year":"2019","unstructured":"Pedersen, M. 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