{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T07:44:34Z","timestamp":1782719074608,"version":"3.54.5"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T00:00:00Z","timestamp":1778889600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Clinical Trials from the Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University","award":["2021-LCYJ-MS-11"],"award-info":[{"award-number":["2021-LCYJ-MS-11"]}]},{"name":"Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Project","award":["2024-JCYJ-QP-15"],"award-info":[{"award-number":["2024-JCYJ-QP-15"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1007\/s00259-026-07918-y","type":"journal-article","created":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T06:59:24Z","timestamp":1778914764000},"page":"5475-5490","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An interpretable PET\/CT-based radiomic-clinical model for predicting bone marrow involvement in follicular lymphoma: comparison of pelvic and spine-pelvis VOI frameworks"],"prefix":"10.1007","volume":"53","author":[{"given":"Shao-Chun","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun-Peng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fen","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao-Liang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai-Mei","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8425-3771","authenticated-orcid":false,"given":"Jian","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,16]]},"reference":[{"key":"7918_CR1","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.1182\/blood.2024026020","volume":"146","author":"F Araujo-Ayala","year":"2025","unstructured":"Araujo-Ayala F, B\u00e9guelin W. Biology as vulnerability in follicular lymphoma: genetics, epigenetics, and immunogenetics. Blood. 2025;146:1759\u201369. https:\/\/doi.org\/10.1182\/blood.2024026020.","journal-title":"Blood"},{"key":"7918_CR2","doi-asserted-by":"publisher","first-page":"1013","DOI":"10.1182\/bloodadvances.2024014053","volume":"9","author":"JR Day","year":"2025","unstructured":"Day JR, Larson MC, Durani U, Koff JL, Wang Y, Habermann TM, et al. Treatment patterns and outcomes in follicular lymphoma with POD24: an analysis from the LEO Consortium. Blood Adv. 2025;9:1013\u201323. https:\/\/doi.org\/10.1182\/bloodadvances.2024014053.","journal-title":"Blood Adv"},{"key":"7918_CR3","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1182\/blood-2017-11-816405","volume":"132","author":"E Bachy","year":"2018","unstructured":"Bachy E, Maurer MJ, Habermann TM, Gelas-Dore B, Maucort-Boulch D, Estell JA, et al. A simplified scoring system in de novo follicular lymphoma treated initially with immunochemotherapy. Blood. 2018;132:49\u201358. https:\/\/doi.org\/10.1182\/blood-2017-11-816405.","journal-title":"Blood"},{"key":"7918_CR4","doi-asserted-by":"publisher","first-page":"4555","DOI":"10.1200\/jco.2008.21.3991","volume":"27","author":"M Federico","year":"2009","unstructured":"Federico M, Bellei M, Marcheselli L, Luminari S, Lopez-Guillermo A, Vitolo U, et al. Follicular lymphoma international prognostic index 2: a new prognostic index for follicular lymphoma developed by the international follicular lymphoma prognostic factor project. J Clin Oncol. 2009;27:4555\u201362. https:\/\/doi.org\/10.1200\/jco.2008.21.3991.","journal-title":"J Clin Oncol"},{"key":"7918_CR5","doi-asserted-by":"publisher","first-page":"4387","DOI":"10.1007\/s00330-024-11213-5","volume":"35","author":"D Leithner","year":"2025","unstructured":"Leithner D, Neri E, D\u2019Anastasi M, Schlemmer HP, Winkelmann M, Kunz WG, et al. ESR Essentials: imaging of lymphoma-practice recommendations by the European Society of Oncologic Imaging. Eur Radiol. 2025;35:4387\u201394. https:\/\/doi.org\/10.1007\/s00330-024-11213-5.","journal-title":"Eur Radiol"},{"key":"7918_CR6","doi-asserted-by":"publisher","first-page":"3059","DOI":"10.1200\/jco.2013.54.8800","volume":"32","author":"BD Cheson","year":"2014","unstructured":"Cheson BD, Fisher RI, Barrington SF, Cavalli F, Schwartz LH, Zucca E, Lister TA. Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification. J Clin Oncol. 2014;32:3059\u201368. https:\/\/doi.org\/10.1200\/jco.2013.54.8800.","journal-title":"J Clin Oncol"},{"key":"7918_CR7","doi-asserted-by":"publisher","first-page":"4508","DOI":"10.1200\/jco.2012.42.4036","volume":"30","author":"TC El-Galaly","year":"2012","unstructured":"El-Galaly TC, d\u2019Amore F, Mylam KJ, de Nully Brown P, B\u00f8gsted M, Bukh A, et al. Routine bone marrow biopsy has little or no therapeutic consequence for positron emission tomography\/computed tomography-staged treatment-naive patients with Hodgkin lymphoma. J Clin Oncol. 2012;30:4508\u201314. https:\/\/doi.org\/10.1200\/jco.2012.42.4036.","journal-title":"J Clin Oncol"},{"key":"7918_CR8","doi-asserted-by":"publisher","first-page":"298","DOI":"10.1016\/j.annonc.2020.11.008","volume":"32","author":"M Dreyling","year":"2021","unstructured":"Dreyling M, Ghielmini M, Rule S, Salles G, Ladetto M, Tonino SH, et al. Newly diagnosed and relapsed follicular lymphoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2021;32:298\u2013308. https:\/\/doi.org\/10.1016\/j.annonc.2020.11.008.","journal-title":"Ann Oncol"},{"key":"7918_CR9","doi-asserted-by":"publisher","first-page":"1812","DOI":"10.1182\/bloodadvances.2020001579","volume":"4","author":"R Nakajima","year":"2020","unstructured":"Nakajima R, Moskowitz AJ, Michaud L, Mauguen A, Batlevi CL, Dogan A, Sch\u00f6der H. Baseline FDG-PET\/CT detects bone marrow involvement in follicular lymphoma and provides relevant prognostic information. Blood Adv. 2020;4:1812\u201323. https:\/\/doi.org\/10.1182\/bloodadvances.2020001579.","journal-title":"Blood Adv"},{"key":"7918_CR10","doi-asserted-by":"publisher","first-page":"3791","DOI":"10.1007\/s00277-025-06487-8","volume":"104","author":"YY Chen","year":"2025","unstructured":"Chen YY, Wang JJ, Zhong WH, Guo JR, Qiu YY, Liu TX, et al. Rethinking bone marrow biopsy: is PET\/CT enough for staging low-grade follicular lymphomas? Ann Hematol. 2025;104:3791\u2013801. https:\/\/doi.org\/10.1007\/s00277-025-06487-8.","journal-title":"Ann Hematol"},{"key":"7918_CR11","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1148\/radiol.2015151169","volume":"278","author":"RJ Gillies","year":"2016","unstructured":"Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278:563\u201377. https:\/\/doi.org\/10.1148\/radiol.2015151169.","journal-title":"Radiology"},{"key":"7918_CR12","doi-asserted-by":"publisher","first-page":"2403","DOI":"10.1007\/s00277-023-05274-7","volume":"102","author":"Y Zheng","year":"2023","unstructured":"Zheng Y, Liu J, Si J, Xue Q, Chen D, Nuermaimaiti R, Tian C. Positron emission tomography to detect bone marrow involvement for patients with follicular lymphoma: a systematic review and meta-analysis. Ann Hematol. 2023;102:2403\u201312. https:\/\/doi.org\/10.1007\/s00277-023-05274-7.","journal-title":"Ann Hematol"},{"key":"7918_CR13","doi-asserted-by":"publisher","first-page":"23812","DOI":"10.1038\/s41598-021-03278-9","volume":"11","author":"J Faudemer","year":"2021","unstructured":"Faudemer J, Aide N, Gac AC, Damaj G, Vilque JP, Lasnon C. Diagnostic value of baseline (18)FDG PET\/CT skeletal textural features in follicular lymphoma. Sci Rep. 2021;11:23812. https:\/\/doi.org\/10.1038\/s41598-021-03278-9.","journal-title":"Sci Rep"},{"key":"7918_CR14","doi-asserted-by":"publisher","first-page":"103207","DOI":"10.1016\/j.media.2024.103207","volume":"95","author":"A Diaz-Pinto","year":"2024","unstructured":"Diaz-Pinto A, Alle S, Nath V, Tang Y, Ihsani A, Asad M, et al. MONAI Label: A framework for AI-assisted interactive labeling of 3D medical images. Med Image Anal. 2024;95:103207. https:\/\/doi.org\/10.1016\/j.media.2024.103207.","journal-title":"Med Image Anal"},{"key":"7918_CR15","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Valli\u00e8res M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, et al. The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020;295:328\u201338. https:\/\/doi.org\/10.1148\/radiol.2020191145.","journal-title":"Radiology"},{"key":"7918_CR16","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.Can-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","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.","journal-title":"Cancer Res"},{"key":"7918_CR17","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1186\/s41747-022-00294-w","volume":"6","author":"A Demircio\u011flu","year":"2022","unstructured":"Demircio\u011flu A. The effect of preprocessing filters on predictive performance in radiomics. Eur Radiol Exp. 2022;6:40. https:\/\/doi.org\/10.1186\/s41747-022-00294-w.","journal-title":"Eur Radiol Exp"},{"key":"7918_CR18","doi-asserted-by":"publisher","first-page":"2452","DOI":"10.21037\/tlcr-22-248","volume":"11","author":"S Volpe","year":"2022","unstructured":"Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, et al. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res. 2022;11:2452\u201363. https:\/\/doi.org\/10.21037\/tlcr-22-248.","journal-title":"Transl Lung Cancer Res"},{"key":"7918_CR19","doi-asserted-by":"publisher","unstructured":"Mayerhoefer ME, Riedl CC, Kumar A, Dogan A, Gibbs P, Weber M, et al. [18F]FDG-PET\/CT radiomics for prediction of bone marrow involvement in mantle cell lymphoma: a retrospective study in 97 patients. Cancers (Basel). 2020;12. https:\/\/doi.org\/10.3390\/cancers12051138.","DOI":"10.3390\/cancers12051138"},{"key":"7918_CR20","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1186\/s12911-025-03110-8","volume":"25","author":"X Zhu","year":"2025","unstructured":"Zhu X, Lu D, Wu Y, Lu Y, He L, Deng Y, et al. An interpretable machine learning model for predicting bone marrow invasion in patients with lymphoma via (18)F-FDG PET\/CT: a multicenter study. BMC Med Inf Decis Mak. 2025;25:264. https:\/\/doi.org\/10.1186\/s12911-025-03110-8.","journal-title":"BMC Med Inf Decis Mak"},{"key":"7918_CR21","doi-asserted-by":"publisher","first-page":"847","DOI":"10.1016\/j.ijrobp.2009.11.040","volume":"79","author":"JA Hayman","year":"2011","unstructured":"Hayman JA, Callahan JW, Herschtal A, Everitt S, Binns DS, Hicks RJ, Mac Manus M. Distribution of proliferating bone marrow in adult cancer patients determined using FLT-PET imaging. Int J Radiat Oncol Biol Phys. 2011;79:847\u201352. https:\/\/doi.org\/10.1016\/j.ijrobp.2009.11.040.","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"7918_CR22","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1148\/radiol.2019190357","volume":"293","author":"SA Mattonen","year":"2019","unstructured":"Mattonen SA, Davidzon GA, Benson J, Leung ANC, Vasanawala M, Horng G, et al. Bone Marrow and Tumor Radiomics at (18)F-FDG PET\/CT: Impact on Outcome Prediction in Non-Small Cell Lung Cancer. Radiology. 2019;293:451\u20139. https:\/\/doi.org\/10.1148\/radiol.2019190357.","journal-title":"Radiology"},{"key":"7918_CR23","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1182\/blood-2003-12-4434","volume":"104","author":"P Solal-C\u00e9ligny","year":"2004","unstructured":"Solal-C\u00e9ligny P, Roy P, Colombat P, White J, Armitage JO, Arranz-Saez R, et al. Follicular lymphoma international prognostic index. Blood. 2004;104:1258\u201365. https:\/\/doi.org\/10.1182\/blood-2003-12-4434.","journal-title":"Blood"},{"key":"7918_CR24","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.3324\/haematol.11502","volume":"92","author":"M Federico","year":"2007","unstructured":"Federico M, Guglielmi C, Luminari S, Mammi C, Marcheselli L, Gianelli U, et al. Prognostic relevance of serum beta2 microglobulin in patients with follicular lymphoma treated with anthracycline-containing regimens. A GISL study. Haematologica. 2007;92:1482\u20138. https:\/\/doi.org\/10.3324\/haematol.11502.","journal-title":"Haematologica"},{"key":"7918_CR25","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s41747-023-00325-0","volume":"7","author":"A Demircio\u011flu","year":"2023","unstructured":"Demircio\u011flu A. Are deep models in radiomics performing better than generic models? A systematic review. Eur Radiol Exp. 2023;7:11. https:\/\/doi.org\/10.1186\/s41747-023-00325-0.","journal-title":"Eur Radiol Exp"},{"key":"7918_CR26","doi-asserted-by":"publisher","unstructured":"Demircio\u011flu A. Deep features from pretrained networks do not outperform hand-crafted features in radiomics. Diagnostics (Basel). 2023;13. https:\/\/doi.org\/10.3390\/diagnostics13203266.","DOI":"10.3390\/diagnostics13203266"},{"key":"7918_CR27","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell. 2002;24:971\u201387. https:\/\/doi.org\/10.1109\/TPAMI.2002.1017623.","journal-title":"IEEE Trans Pattern Anal Mach Intell"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-026-07918-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00259-026-07918-y","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-026-07918-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T06:47:49Z","timestamp":1782715669000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00259-026-07918-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,16]]},"references-count":27,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["7918"],"URL":"https:\/\/doi.org\/10.1007\/s00259-026-07918-y","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,16]]},"assertion":[{"value":"24 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This retrospective study was conducted at Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, approved by the Institutional Ethics Committee (Approval No: 2024-851-01) with waived informed consent.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"This retrospective study used anonymised patient data and was granted a waiver of informed consent by the Institutional Ethics Committee (Approval No: 2024-851-01). All data were de-identified prior to analysis to ensure participant confidentiality.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}