{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T15:50:32Z","timestamp":1791474632993,"version":"4.3.4"},"reference-count":25,"publisher":"Walter de Gruyter GmbH","issue":"4-5","license":[{"start":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T00:00:00Z","timestamp":1777420800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Yancheng Health Commission Scientific Research Project","award":["YK2023046"],"award-info":[{"award-number":["YK2023046"]}]},{"name":"Yancheng Health Commission Scientific Research Project","award":["YK2025007"],"award-info":[{"award-number":["YK2025007"]}]},{"name":"Jiangsu Traditional Chinese Medicine Science and Technology Development Plan Project","award":["MS2024102"],"award-info":[{"award-number":["MS2024102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,26]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"j_bmt-2026-0090_abs_001\">\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>Noninvasive differentiation between Hodgkin lymphoma and aggressive non-Hodgkin lymphoma remains clinically important yet challenging. We developed and internally validated a PET-based radiomic model using routine baseline 18\u202fF-FDG images to assist histological discrimination when tissue diagnosis is difficult.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0090_abs_002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this retrospective single-center study, baseline 18\u202fF-FDG PET scans and clinical records from 190 histologically confirmed lymphoma patients were analyzed. Tumor volumes were segmented and high throughput radiomic features were extracted. Feature selection combined univariate filtering with a sparsity-promoting algorithm, and a multivariable logistic regression produced the final radiomics signature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0090_abs_003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A compact radiomic signature that captures intratumoral heterogeneity separated Hodgkin lymphoma from aggressive non-Hodgkin lymphoma. On an independent validation cohort, the model demonstrated good discriminatory ability (AUC=0.71), and performance metrics indicate potential clinical utility as a noninvasive adjunct to standard assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"j_bmt-2026-0090_abs_004\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics applied to routine baseline 18\u202fF-FDG PET can provide a practical, image-based biomarker to support lymphoma subtyping when biopsy is limited. Prospective, multi-center validation is recommended before clinical deployment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1515\/bmt-2026-0090","type":"journal-article","created":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T21:29:40Z","timestamp":1777498180000},"page":"419-428","source":"Crossref","is-referenced-by-count":1,"title":["A PET-radiomics diagnostic signature to differentiate Hodgkin lymphoma from aggressive non-Hodgkin lymphomas"],"prefix":"10.1515","volume":"71","author":[{"given":"Yongfen","family":"Huang","sequence":"first","affiliation":[{"name":"Department of Hematology , Yancheng First Hospital Affiliated to Medical School of Nanjing University , Yancheng , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tongle","family":"Zhi","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery , Yancheng First Hospital Affiliated to Medical School of Nanjing University , Yancheng , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuqing","family":"Miao","sequence":"additional","affiliation":[{"name":"Department of Hematology , Yancheng First Hospital Affiliated to Medical School of Nanjing University , Yancheng , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengjie","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery , Yancheng First Hospital Affiliated to Medical School of Nanjing University , Yancheng , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2026,4,29]]},"reference":[{"key":"2026100814512974469_j_bmt-2026-0090_ref_001","doi-asserted-by":"crossref","unstructured":"Ansell, SM. Hodgkin lymphoma: 2018 update on diagnosis, risk-stratification, and management. Am J Hematol 2018;93:704\u201315. https:\/\/doi.org\/10.1002\/ajh.25071.","DOI":"10.1002\/ajh.25071"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_002","doi-asserted-by":"crossref","unstructured":"Coiffier, B, Lepage, E, Briere, J, Herbrecht, R, Tilly, H, Bouabdallah, R, et al.. CHOP chemotherapy plus rituximab compared with CHOP alone in elderly patients with diffuse large-B-cell lymphoma. N\u00a0Engl J Med 2002;346:235\u201342. https:\/\/doi.org\/10.1056\/NEJMoa011795.","DOI":"10.1056\/NEJMoa011795"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_003","unstructured":"Gaddey, HL, Riegel, AM. Unexplained lymphadenopathy: evaluation and differential diagnosis. Am Fam Physician 2016;94:896\u2013903."},{"key":"2026100814512974469_j_bmt-2026-0090_ref_004","doi-asserted-by":"crossref","unstructured":"Assaf, N, Nassif, S, Tamim, H, Bazarbachi, A, Zaatari, G, Chakhachiro, Z, et al.. Diagnosing lymphoproliferative disorders using core-needle biopsy versus surgical excisional biopsy. Clin Lymphoma Myeloma Leuk 2020;20:e455\u201360. https:\/\/doi.org\/10.1016\/j.clml.2019.11.013.","DOI":"10.1016\/j.clml.2019.11.013"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_005","doi-asserted-by":"crossref","unstructured":"Johl, A, Lengfelder, E, Hiddemann, W, Klapper, W, The German Low-grade Lymphoma Study Group (GLSG). Core-needle biopsies and surgical excision biopsies in lymphoma diagnosis. Ann Hematol 2016;95:1281\u20136. https:\/\/doi.org\/10.1007\/s00277-016-2704-0.","DOI":"10.1007\/s00277-016-2704-0"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_006","doi-asserted-by":"crossref","unstructured":"Abbott, M, Jiang, L, Youssef, B, Siddiqi, A, Nassar, A. Reliability of lymphoma diagnosis using combined cytologic preparations and core needle biopsy: a single-center analysis. CytoJournal 2022;19:57. https:\/\/doi.org\/10.25259\/Cytojournal_22_2020.","DOI":"10.25259\/Cytojournal_22_2020"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_007","doi-asserted-by":"crossref","unstructured":"Wong, KKL, Tu, JY, Sun, Z, Dissanayake, DW. Methods in research and development of biomedical devices. Singapore: World Scientific Publishing Co.; 2013.","DOI":"10.1142\/8621"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_008","doi-asserted-by":"crossref","unstructured":"Wong, KKL, Chu, WCW. Ethics policies and procedures in imaging and interventional radiology. Australas Phys Eng Sci Med 2015;38:375\u20136. https:\/\/doi.org\/10.1007\/s13246-015-0346-5.","DOI":"10.1007\/s13246-015-0346-5"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_009","doi-asserted-by":"crossref","unstructured":"Wong, CY, Thie, J, Parling-Lynch, KJ, Zakalik, D, Wong, RH, Gaskill, M, et al.. Investigating the existence of quantum metabolic values in non-hodgkin\u2019s lymphoma by 2-deoxy-2-[F-18]fluoro-D-glucose PET. Mol Imag Biol 2007;9:43\u20139. https:\/\/doi.org\/10.1007\/s11307-006-0074-z.","DOI":"10.1007\/s11307-006-0074-z"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_010","doi-asserted-by":"crossref","unstructured":"Tixier, F, Le Rest, C-C, Hatt, M, Albarghach, N, Pradier, O, Metges, J-P, et al.. Intratumor heterogeneity characterized by textural features on baseline 18F-FDG PET images predicts response to concomitant radiochemotherapy in esophageal cancer. J\u00a0Nucl Med 2011;52:369\u201378. https:\/\/doi.org\/10.2967\/jnumed.110.082404.","DOI":"10.2967\/jnumed.110.082404"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_011","doi-asserted-by":"crossref","unstructured":"Zwanenburg, A, Valli\u00e8res, M, Abdalah, MA, Aerts, HJWL, Andrearczyk, V, Apte, A, et al.. The image biomarker standardisation initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295:328\u201338. https:\/\/doi.org\/10.1148\/radiol.2020191145.","DOI":"10.1148\/radiol.2020191145"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_012","doi-asserted-by":"crossref","unstructured":"van Griethuysen, JJM, Fedorov, A, Parmar, C, Hosny, A, Aucoin, N, Narayan, V, et al.. Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104\u20137. https:\/\/doi.org\/10.1158\/0008-5472.CAN-17-0339.","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_013","doi-asserted-by":"crossref","unstructured":"Rizzo, S, Botta, F, Raimondi, S, Origgi, D, Fanciullo, C, Morganti, AG, et al.. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2018;2:36. https:\/\/doi.org\/10.1186\/s41747-018-0068-z.","DOI":"10.1186\/s41747-018-0068-z"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_014","doi-asserted-by":"crossref","unstructured":"Depeursinge, A, Foncubierta-Rodriguez, A, Van De Ville, D, M\u00fcller, H. Three-dimensional solid texture analysis in biomedical imaging: review and opportunities. Med Image Anal 2014;18:176\u201396. https:\/\/doi.org\/10.1016\/j.media.2013.10.005.","DOI":"10.1016\/j.media.2013.10.005"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_015","doi-asserted-by":"crossref","unstructured":"Mayerhoefer, ME, Riedl, CC, Kumar, A, Gibbs, P, Weber, M, Tal, I, et al.. 18FDG-PET\/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: a retrospective study in 97 patients. Cancers (Basel) 2020;12:1138. https:\/\/doi.org\/10.3390\/cancers12051138.","DOI":"10.3390\/cancers12051138"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_016","doi-asserted-by":"crossref","unstructured":"Aide, N, Fruchart, C, Nganoa, C, Gac, A-C, Lasnon, C, Bardet, S, et al.. Baseline 18FDG-PET radiomic features as predictors of 2-year event-free survival in diffuse large B-cell lymphoma treated with immunochemotherapy. Eur Radiol 2020;30:4623\u201332. https:\/\/doi.org\/10.1007\/s00330-020-06815-8.","DOI":"10.1007\/s00330-020-06815-8"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_022","doi-asserted-by":"crossref","unstructured":"Chen, S, Harmon, S, Perk, T, Li, X, Chen, M, Li, Y, et al.. Diagnostic classification of solitary pulmonary nodules using dual-time 18F-FDG PET\/CT texture features. Sci Rep 2017;7:9370. https:\/\/doi.org\/10.1038\/s41598-017-08764-7.","DOI":"10.1038\/s41598-017-08764-7"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_017","doi-asserted-by":"crossref","unstructured":"de Jesus, FM, Yin, Y, Mantzorou-Kyriaki, E, Kahle, XU, de Haas, RJ, Yakar, D, et al.. Machine learning in the differentiation of follicular lymphoma from diffuse large B-cell lymphoma with radiomic 18FDG-PET\/CT features. Eur J Nucl Med Mol Imag 2022;49:1535\u201343. https:\/\/doi.org\/10.1007\/s00259-021-05626-3.","DOI":"10.1007\/s00259-021-05626-3"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_025","doi-asserted-by":"crossref","unstructured":"Lippi, M, Gianotti, S, Fama, A, Casali, M, Barbolini, E, Ferrari, A, et al.. Texture analysis and multiple-instance learning for classification of malignant lymphomas. Comput Methods Progr Biomed 2020;185:105153. https:\/\/doi.org\/10.1016\/j.cmpb.2019.105153.","DOI":"10.1016\/j.cmpb.2019.105153"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_018","doi-asserted-by":"crossref","unstructured":"Ha, S, Choi, H, Paeng, JC, Cheon, GJ, Lee, DS, Chung, JK, et al.. Radiomics in oncological PET\/CT: a methodological overview. Nucl Med Mol Imaging 2019;53:14\u201329. https:\/\/doi.org\/10.1007\/s13139-019-00571-4.","DOI":"10.1007\/s13139-019-00571-4"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_023","doi-asserted-by":"crossref","unstructured":"Feng, Z, Rong, P, Cao, P, Zhou, Q, Zhu, W, Yan, Z, et al.. Machine-learning-based quantitative texture analysis of CT images of small renal masses. Eur Radiol 2018;28:1625\u201333. https:\/\/doi.org\/10.1007\/s00330-017-5118-z.","DOI":"10.1007\/s00330-017-5118-z"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_024","doi-asserted-by":"crossref","unstructured":"Ha, S, Choi, H, Cheon, GJ, Kang, KW, Chung, JK, Kim, EE, et al.. Autoclustering of non-small cell lung carcinoma subtypes on 18F-FDG PET using texture analysis. Nucl Med Mol Imaging 2014;48:278\u201386. https:\/\/doi.org\/10.1007\/s13139-014-0283-3.","DOI":"10.1007\/s13139-014-0283-3"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_019","doi-asserted-by":"crossref","unstructured":"Kong, Z, Jiang, C, Zhu, R, Feng, S, Wang, Y, Li, J, et al.. 18FDG-PET-based radiomics features to distinguish primary central nervous system lymphoma from glioblastoma. Neuroimage, Clin 2019;23:101912. https:\/\/doi.org\/10.1016\/j.nicl.2019.101912.","DOI":"10.1016\/j.nicl.2019.101912"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_020","doi-asserted-by":"crossref","unstructured":"Xu, H, Guo, W, Cui, X, Zhuo, H, Xiao, Y, Ou, X, et al.. Three-dimensional texture analysis based on PET\/CT images to distinguish hepatocellular carcinoma and hepatic lymphoma. Front Oncol 2019;9:844. https:\/\/doi.org\/10.3389\/fonc.2019.00844.","DOI":"10.3389\/fonc.2019.00844"},{"key":"2026100814512974469_j_bmt-2026-0090_ref_021","doi-asserted-by":"crossref","unstructured":"Ou, X, Zhang, J, Wang, J, Pang, F, Wang, Y, Wei, X, et al.. Radiomics based on 18F-FDG PET\/CT differentiates breast carcinoma from breast lymphoma using machine learning: a preliminary study. Cancer Med 2020;9:496\u2013506. https:\/\/doi.org\/10.1002\/cam4.2711.","DOI":"10.1002\/cam4.2711"}],"container-title":["Biomedical Engineering \/ Biomedizinische Technik"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/bmt-2026-0090\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/bmt-2026-0090\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T14:52:48Z","timestamp":1791471168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.degruyterbrill.com\/document\/doi\/10.1515\/bmt-2026-0090\/html"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,29]]},"references-count":25,"journal-issue":{"issue":"4-5","published-online":{"date-parts":[[2026,6,8]]},"published-print":{"date-parts":[[2026,10,26]]}},"alternative-id":["10.1515\/bmt-2026-0090"],"URL":"https:\/\/doi.org\/10.1515\/bmt-2026-0090","relation":{},"ISSN":["0013-5585","1862-278X"],"issn-type":[{"value":"0013-5585","type":"print"},{"value":"1862-278X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,29]]}}}