{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T22:38:39Z","timestamp":1791412719333,"version":"4.3.3"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:00:00Z","timestamp":1775174400000},"content-version":"vor","delay-in-days":35,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005760","name":"University of Gothenburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005760","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["EJNMMI Res"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\u00a0Increasing evidence suggests that total metabolic tumor volume (tMTV) measured before treatment in lymphoma patients undergoing [18F]fluorodeoxyglucose (FDG) PET\/CT scans can predict prognosis. However, there is a lack of fast, reliable, and easy-to-perform multilesional segmentation tools with an urgent need to improve tMTV segmentation workflow in clinical practice. Here, we develop an artificial intelligence (AI)-based tool that automatically calculates tMTV in untreated lymphoma patients undergoing FDG PET\/CT. The RECOMIA AI-based tool is a 3D U-Net convolutional neural network trained on a cohort of 1,500 lymphoma patients, mean age 52 years (range 10\u201388), 44% were female. The model was optimized to segment metabolically active tumors in the FDG PET\/CT scans, enabling automated tMTV measurements. The test group consisted of all untreated Hodgkin lymphoma (HL) patients and all Diffuse large B-cell lymphoma (DLBCL) patients who underwent FDG PET\/CT at Sahlgrenska University Hospital between 2017\u20132018 and 2019\u20132022, respectively. There were 117 patients with mean age 50 years (range 7\u201390), 39% were female. Nine nuclear medicine physicians manually segmented lesions for tMTV calculations, with each patient independently segmented by two physicians.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      \u00a0The median of the manual tMTV was 321 cm\n                      <jats:sup>3<\/jats:sup>\n                      \u00a0(interquartile range [IQR]: 92\u2013689 cm\n                      <jats:sup>3<\/jats:sup>\n                      ) and the median of the difference between two tMTV values segmented by different physicians for the same patient was 26 cm\n                      <jats:sup>3<\/jats:sup>\n                      \u00a0(IQR: 9\u201386 cm\n                      <jats:sup>3<\/jats:sup>\n                      ). In 85 of the 117 patients, one of two manual tMTV measurements was closer to the AI tMTV value than the second manual tMTV measurement made by another physician. In 15 of the remaining 32 patients, the difference between the AI tMTV and the manual tMTV was small (&lt;\u200926 cm\n                      <jats:sup>3<\/jats:sup>\n                      , the median difference between two manual tMTV values made on the same patient).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\u00a0The results of this study show that the RECOMIA AI-based tool achieved segmentation similarity within the inter-observer variability of experienced nuclear medicine physicians in 85% (100\/117) of untreated lymphoma patients. This demonstrates the feasibility of using AI to support physicians in quantifying tMTV for assessment of prognosis in clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13550-026-01403-1","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T07:22:39Z","timestamp":1772176959000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated RECOMIA AI-based total metabolic tumor volume in lymphoma \u2013 a retrospective study"],"prefix":"10.1186","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9781-2264","authenticated-orcid":false,"given":"May","family":"Sadik","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johanna","family":"M\u00f6rk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jesus","family":"Lopez Urdaneta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malin","family":"Lewold","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e5ns","family":"Larsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olof","family":"Enqvist","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sally F.","family":"Barrington","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lars","family":"Edenbrandt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elin","family":"Tr\u00e4g\u00e5rdh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"1403_CR1","doi-asserted-by":"publisher","first-page":"561","DOI":"10.1016\/j.cpet.2024.05.009","volume":"19","author":"S Chauvie","year":"2024","unstructured":"Chauvie S, Castellino A, Bergesio F, De Maggi A, Durmo R. Lymphoma: the added value of radiomics, volumes and global disease assessment. PET Clin. 2024;19:561\u20138.","journal-title":"PET Clin"},{"key":"1403_CR2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1424269","volume":"15","author":"A Al-Ibraheem","year":"2024","unstructured":"Al-Ibraheem A, Abdlkadir AS, Al-Adhami DA, Sathekge M, Hee-Seung Bom H, Ma\u2019koseh M, et al. The prognostic utility of 18F-FDG PET parameters in lymphoma patients under CAR-T-cell therapy: a systematic review and meta-analysis. Front Immunol. 2024;15:1424269.","journal-title":"Front Immunol"},{"key":"1403_CR3","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.1182\/bloodadvances.2021005385","volume":"6","author":"TJ Voorhees","year":"2022","unstructured":"Voorhees TJ, Zhao B, Oldan J, Hucks G, Khandani A, Dittus C, et al. Pretherapy metabolic tumor volume is associated with response to CD30 CAR T cells in Hodgkin lymphoma. 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The need for written informed consent was waived in #2019\u201301274 Dnr 2024\u201308225-02, while all participants provided written informed consent before entering the following studies 2016\/417, 2018\/117, 2018\/753, 2021\u201305734-02, respectively. We certify that the study was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments. The datasets from anonymized publicly available publications of the TCIA data was approved by the local ethics committee and data protection officer [6, 7].","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The following authors declare no financial or non-financial interests (MS, JM, JLU, MLe, MLa, OE, LE and ET). SFB acknowledges support from the National Institute for Health and Care Research (NIHR) [RP-2016\u201307-001]. This work was also supported by core funding from the Wellcome\/EPSRC Centre for Medical Engineering at King\u2019s College London [WT203148\/Z\/16\/Z]. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"53"}}