{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T01:51:11Z","timestamp":1784253071239,"version":"3.55.0"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100011102","name":"Seventh Framework Programme","doi-asserted-by":"publisher","award":["92003552"],"award-info":[{"award-number":["92003552"]}],"id":[{"id":"10.13039\/100011102","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>Based on germline and somatic mutation profiles, pheochromocytomas and paragangliomas (PPGLs) can be classified into different clusters. We investigated the use of [<jats:sup>18<\/jats:sup>F]FDG-PET\/CT radiomics, SUV<jats:sub>max<\/jats:sub> and biochemical profile for the identification of the genetic clusters of PPGLs.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>In this single-centre cohort, 40 PPGLs (13 cluster 1, 18 cluster 2, 9 sporadic) were delineated using a 41% adaptive threshold of SUV<jats:sub>peak<\/jats:sub> ([<jats:sup>18<\/jats:sup>F]FDG-PET) and manually (low-dose CT; ldCT). Using PyRadiomics, 211 radiomic features were extracted. Stratified 5-fold cross-validation for the identification of the genetic cluster was performed using multinomial logistic regression with dimensionality reduction incorporated per fold. Classification performances of biochemistry, SUV<jats:sub>max<\/jats:sub> and PET(\/CT) radiomic models were compared and presented as mean (multiclass) test AUCs over the five\u00a0folds. Results were validated using a sham experiment, randomly shuffling the outcome labels.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The model with biochemistry only could identify the genetic cluster (multiclass AUC 0.60). The three-factor PET model had the best classification performance (multiclass AUC 0.88). A simplified model with only SUV<jats:sub>max<\/jats:sub> performed almost similarly. Addition of ldCT features and biochemistry decreased the classification performances. All sham AUCs were approximately 0.50.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>PET radiomics achieves a better identification of PPGLs compared to biochemistry, SUV<jats:sub>max<\/jats:sub>, ldCT radiomics and combined approaches, especially for the differentiation of sporadic PPGLs. Nevertheless, a model with SUV<jats:sub>max<\/jats:sub> alone might be preferred clinically, weighing model performances against laborious radiomic analysis. The limited added value of radiomics to the overall classification performance for PPGL should be validated in a larger external cohort.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>Radiomics derived from [<\/jats:italic><jats:sup><jats:italic>18<\/jats:italic><\/jats:sup><jats:italic>F]FDG-PET\/CT has the potential to improve the identification of the genetic clusters of pheochromocytomas and paragangliomas.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>A simplified model with SUV<\/jats:italic><jats:sub><jats:italic>max<\/jats:italic><\/jats:sub><jats:italic>only might be preferred clinically, weighing model performances against the laborious radiomic analysis.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Cluster 1 and 2 PPGLs generally present distinctive characteristics that can be captured using [<\/jats:italic><jats:sup><jats:italic>18<\/jats:italic><\/jats:sup><jats:italic>F]FDG-PET imaging. Sporadic PPGLs appear more heterogeneous, frequently resembling cluster 2 PPGLs and occasionally resembling cluster 1 PPGLs.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-022-09034-5","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T11:06:10Z","timestamp":1661339170000},"page":"7227-7236","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["[18F]FDG-PET\/CT radiomics for the identification of genetic clusters in pheochromocytomas and paragangliomas"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0450-951X","authenticated-orcid":false,"given":"Wyanne A.","family":"Noortman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5762-6766","authenticated-orcid":false,"given":"Dennis","family":"Vriens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1817-2743","authenticated-orcid":false,"given":"Lioe-Fee","family":"de Geus-Oei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6544-4567","authenticated-orcid":false,"given":"Cornelis H.","family":"Slump","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3809-0182","authenticated-orcid":false,"given":"Erik H.","family":"Aarntzen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9672-8500","authenticated-orcid":false,"given":"Anouk","family":"van Berkel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1208-5748","authenticated-orcid":false,"given":"Henri J. 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P. van Velden, Leiden University Medical Center, Leiden, the Netherlands.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}},{"value":"One of the authors has significant statistical expertise.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and Biometry"}},{"value":"This retrospective database study has been reviewed and approved by the Commission on Medical Research Involving Human Subjects Region Arnhem-Nijmegen, the Netherlands (protocol code: 2018-4655, date of approval: 10 December 2018). Informed consent was waived because of the retrospective nature of the study. Patients that objected to the use of their anonymised data were excluded.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Some study subjects or cohorts have been previously reported in [, , ].","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Retrospective\u2022 Diagnostic or prognostic study\u2022 Performed at one institution","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}