{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:54:10Z","timestamp":1781607250295,"version":"3.54.5"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005788","name":"Medical University of Vienna","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005788","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>\n                      Immune checkpoint inhibitors (ICI) have transformed cancer therapy, improving outcomes in malignancies like lung cancer, melanoma, and lymphoma by targeting PD-1, PD-L1, and CTLA-4 to enhance T cell-mediated tumor destruction. However, ICI often induce immune-related adverse events (irAEs) across multiple organs. [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG PET\/CT is a valuable tool for assessing immune activation, but whole-organ inflammation evaluation remains time-consuming and prone to variability. This study investigates AI-driven organ-level [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG PET\/CT uptake changes pre- and post-ICI therapy to opportunistically detect irAEs.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      A total of 64 patients with lung cancer, melanoma, or lymphoma who underwent [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG PET\/CT before and after ICI therapy were included. Automated delineation of anatomical structures was performed using an artificial intelligence approach involving multiple subsequently applied deep learning models. SUV\n                      <jats:sub>max<\/jats:sub>\n                      and SUV\n                      <jats:sub>mean<\/jats:sub>\n                      were quantified for irAE-related organs (thyroid, myocardium, pancreas, liver, bone, spleen, and adrenal glands) after automated tumor lesion removal. Statistical analysis identified uptake changes linked to clinically observed inflammation-related adverse events.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Among 64 patients (mean scan interval 4.5 months), thyroid uptake increased post-ICI (\u0394SUV\n                      <jats:sub>max<\/jats:sub>\n                      =0.44,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.04; \u0394SUV\n                      <jats:sub>mean<\/jats:sub>\n                      =0.22,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.08). Increased uptake occurred in 55% (SUV\n                      <jats:sub>max<\/jats:sub>\n                      ) and 53% (SUV\n                      <jats:sub>mean<\/jats:sub>\n                      ) of patients. Thyroid-related adverse events were more frequent in those with increased uptake (29% vs. 3%,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008), with uptake increase being the most pronounced in the cases of hypothyroidism (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.002 for SUV\n                      <jats:sub>max<\/jats:sub>\n                      ,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.03 for SUV\n                      <jats:sub>mean<\/jats:sub>\n                      ). Myocardial, pancreatic, and hepatic uptake increased but without statistical significance (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&gt;\u20090.05).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      AI-enabled opportunistic [\n                      <jats:sup>18<\/jats:sup>\n                      F]FDG PET\/CT screening effectively detects thyroid-related irAEs, particularly hypothyroidism, through uptake quantification. This approach offers a promising biomarker for early irAE detection, enhancing patient management and immunotherapy optimization.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07364-2","type":"journal-article","created":{"date-parts":[[2025,5,28]],"date-time":"2025-05-28T23:37:22Z","timestamp":1748475442000},"page":"4963-4971","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Artificial intelligence-enabled opportunistic identification of immune checkpoint inhibitor-related adverse events using [18F]FDG PET\/CT"],"prefix":"10.1007","volume":"52","author":[{"given":"Clemens P.","family":"Spielvogel","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksa","family":"Lazarevic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucia","family":"Zisser","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Haberl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christophoros","family":"Eseroglou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucian","family":"Beer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcus","family":"Hacker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8018-3884","authenticated-orcid":false,"given":"Raffaella","family":"Calabretta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"7364_CR1","doi-asserted-by":"publisher","first-page":"1350","DOI":"10.1126\/science.aar4060","volume":"359","author":"A Ribas","year":"2018","unstructured":"Ribas A, Wolchok JD. 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The remaining authors have nothing to declare.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}