{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T08:51:04Z","timestamp":1782204664716,"version":"3.54.5"},"reference-count":59,"publisher":"IOP Publishing","issue":"12","license":[{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"},{"start":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T00:00:00Z","timestamp":1782172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/questions\/alternative-author-rights-policies\/"}],"funder":[{"name":"Novo Nordisk Foundation, Denmark","award":["NFF20OC0062056"],"award-info":[{"award-number":["NFF20OC0062056"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Phys. Med. Biol."],"published-print":{"date-parts":[[2026,6,28]]},"abstract":"Abstract<\/jats:title>\n \n Objective.<\/jats:italic>\n Develop and evaluate a patient-level framework for discriminating Hodgkin lymphoma (HL) from non-HL (NHL) on baseline\n \n \n \n <\/jats:tex-math>\n \n \n \n \n \n 18<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n F-FDG PET by aggregating radiomic descriptors from a variable number of involved lymph nodes in a multi-center setting.\n Approach.<\/jats:italic>\n Each patient was modeled as an unordered set of lesion-level PET radiomic feature vectors. PET images were converted to standardized uptake values (SUV), resampled to a uniform voxel spacing of\n \n \n \n <\/jats:tex-math>\n \n \n 2<\/mml:mn>\n \u00d7<\/mml:mo>\n 2<\/mml:mn>\n \u00d7<\/mml:mo>\n 2<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n mm\n \n \n \n <\/jats:tex-math>\n \n \n \n \n 3<\/mml:mn>\n <\/mml:msup>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n , and radiomic features were extracted using PyRadiomics from the original SUV images with a fixed bin width of 0.25 SUV units, following definitions consistent with the Image Biomarker Standardisation Initiative. A total of 107 first-order, texture, and shape features were extracted per lesion and optionally augmented with demographic variables and normalized node center-of-mass coordinates after rigid registration to a common reference space. We compared statistical pooled tabular baselines with learnable set-based aggregation models, including Transformer, Deep Sets, multiple instance learning, Set Transformer, and a graph-based model. Models were trained using five-fold cross-validation on Center 1 (151 patients; 85 HL, 66 NHL) and evaluated on an independent external Center 2 cohort (80 patients; 36 HL, 44 NHL) under direct transfer and unsupervised test-time adaptation (TTA).\n Main results.<\/jats:italic>\n Combining radiomics with demographic and spatial features consistently improved discrimination. On Center 1, the best-performing deep aggregation models achieved ROC-AUC (mean\n \n \n \n <\/jats:tex-math>\n \n \n \u00b1<\/mml:mo>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n standard deviation) up to\n \n \n \n <\/jats:tex-math>\n \n \n 0.91<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.05<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and accuracy up to\n \n \n \n <\/jats:tex-math>\n \n \n 0.84<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.07<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n . External evaluation on Center 2 revealed a measurable domain shift, with reduced performance under direct transfer. Unsupervised TTA, particularly TENT, improved cross-center generalization for deep aggregation models; for example, the Transformer improved from ROC-AUC\n \n \n \n <\/jats:tex-math>\n \n \n 0.81<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.05<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n <\/jats:tex-math>\n \n \n F<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n 1-score\n \n \n \n <\/jats:tex-math>\n \n \n 0.60<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.23<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n to ROC-AUC\n \n \n \n <\/jats:tex-math>\n \n \n 0.87<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.01<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n \n \n \n <\/jats:tex-math>\n \n \n F<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n 1-score\n \n \n \n <\/jats:tex-math>\n \n \n 0.75<\/mml:mn>\n \u00b1<\/mml:mo>\n 0.01<\/mml:mn>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n . Feature attribution analysis indicated that both demographic and radiomic descriptors contributed to the predictions.\n Significance.<\/jats:italic>\n These results show that multi-nodal PET radiomics combined with demographic and spatial dissemination cues is a promising strategy for patient-level HL versus NHL prediction. The findings also highlight the importance of robustness considerations in multi-center PET radiomics and suggest that TTA may help mitigate cross-center variability.\n <\/jats:p>","DOI":"10.1088\/1361-6560\/ae7955","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T22:56:31Z","timestamp":1780700191000},"page":"125014","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisiting multi-nodal radiomics with advanced feature learning for lymphoma classification: a multi-center study"],"prefix":"10.1088","volume":"71","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0911-367X","authenticated-orcid":true,"given":"Reza","family":"Karimzadeh","sequence":"first","affiliation":[{"name":"University of Copenhagen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-2270-8974","authenticated-orcid":false,"given":"Setareh","family":"Hasanabadi","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0222-116X","authenticated-orcid":true,"given":"Maryam","family":"Cheraghi","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University"},{"name":"Shahid Beheshti University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4864-6161","authenticated-orcid":false,"given":"Mehrdad","family":"Bakhshayesh Karam","sequence":"additional","affiliation":[{"name":"Shahid Beheshti University of Medical Sciences"},{"name":"Nuclear Medicine Department"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6526-0960","authenticated-orcid":true,"given":"Hossein","family":"Arabi","sequence":"additional","affiliation":[{"name":"Geneva University Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7739-7788","authenticated-orcid":false,"given":"Bulat","family":"Ibragimov","sequence":"additional","affiliation":[{"name":"University of Copenhagen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,6,23]]},"reference":[{"key":"pmbae7955bib1","doi-asserted-by":"publisher","first-page":"310","DOI":"10.2214\/AJR.10.4923","type":"journal-article","article-title":"A systematic review of the factors affecting accuracy of suv measurements","volume":"195","author":"Adams","year":"2010","journal-title":"Am. 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