{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T07:18:26Z","timestamp":1793085506622,"version":"4.5.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T00:00:00Z","timestamp":1629244800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004622","name":"KWF Kankerbestrijding","doi-asserted-by":"publisher","award":["#VU 2018-11648"],"award-info":[{"award-number":["#VU 2018-11648"]}],"id":[{"id":"10.13039\/501100004622","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value\u2009\u2265\u20094.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUV<jats:sub>peak<\/jats:sub> and the maximal distance between the largest lesion and any other lesion (Dmax<jats:sub>bulk<\/jats:sub>, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUV<jats:sub>peak<\/jats:sub> and Dmax<jats:sub>bulk<\/jats:sub>) and patient-related parameters (WHO performance status and age\u2009&gt;\u200960\u00a0years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Trial registration number and date<\/jats:title>\n                <jats:p>EudraCT: 2006\u2013005,174-42, 01\u201308-2008.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00259-021-05480-3","type":"journal-article","created":{"date-parts":[[2021,8,18]],"date-time":"2021-08-18T04:37:59Z","timestamp":1629261479000},"page":"932-942","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":126,"title":["18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma"],"prefix":"10.1007","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6094-0016","authenticated-orcid":false,"given":"Jakoba J.","family":"Eertink","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3541-9111","authenticated-orcid":false,"given":"Tim","family":"van de Brug","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3698-8121","authenticated-orcid":false,"given":"Sanne E.","family":"Wiegers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9571-9362","authenticated-orcid":false,"given":"Gerben J. C.","family":"Zwezerijnen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6160-3011","authenticated-orcid":false,"given":"Elisabeth A. G.","family":"Pfaehler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6735-8651","authenticated-orcid":false,"given":"Pieternella J.","family":"Lugtenburg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bronno","family":"van der Holt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5454-2804","authenticated-orcid":false,"given":"Henrica C. W.","family":"de Vet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0767-2734","authenticated-orcid":false,"given":"Otto S.","family":"Hoekstra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0313-5686","authenticated-orcid":false,"given":"Ronald","family":"Boellaard","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1074-5922","authenticated-orcid":false,"given":"Jos\u00e9e M.","family":"Zijlstra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,18]]},"reference":[{"key":"5480_CR1","doi-asserted-by":"publisher","first-page":"1800","DOI":"10.1182\/blood-2017-03-769620","volume":"130","author":"M Crump","year":"2017","unstructured":"Crump M, Neelapu SS, Farooq U, Van Den Neste E, Kuruvilla J, Westin J, et al. Outcomes in refractory diffuse large B-cell lymphoma: results from the international SCHOLAR-1 study. Blood. 2017;130:1800\u20138. https:\/\/doi.org\/10.1182\/blood-2017-03-769620.","journal-title":"Blood"},{"key":"5480_CR2","doi-asserted-by":"publisher","first-page":"544","DOI":"10.1200\/JCO.2016.69.0198","volume":"35","author":"GW van Imhoff","year":"2017","unstructured":"van Imhoff GW, McMillan A, Matasar MJ, Radford J, Ardeshna KM, Kuliczkowski K, et al. Ofatumumab versus rituximab salvage chemoimmunotherapy in relapsed or refractory diffuse large B-cell lymphoma: the ORCHARRD study. J Clin Oncol. 2017;35:544\u201351. https:\/\/doi.org\/10.1200\/JCO.2016.69.0198.","journal-title":"J Clin Oncol"},{"key":"5480_CR3","doi-asserted-by":"publisher","DOI":"10.1111\/bjh.16691","author":"M Gleeson","year":"2020","unstructured":"Gleeson M, Counsell N, Cunningham D, Lawrie A, Clifton-Hadley L, Hawkes E, et al. Prognostic indices in diffuse large B-cell lymphoma in the rituximab era: an analysis of the UK National Cancer Research Institute R-CHOP 14 versus 21 phase 3 trial. Br J Haematol. 2020. https:\/\/doi.org\/10.1111\/bjh.16691.","journal-title":"Br J Haematol"},{"key":"5480_CR4","doi-asserted-by":"publisher","first-page":"2041","DOI":"10.1182\/blood.2019002729","volume":"135","author":"AS Ruppert","year":"2020","unstructured":"Ruppert AS, Dixon JG, Salles G, Wall A, Cunningham D, Poeschel V, et al. International prognostic indices in diffuse large B-cell lymphoma: a comparison of IPI, R-IPI, and NCCN-IPI. Blood. 2020;135:2041\u20138. https:\/\/doi.org\/10.1182\/blood.2019002729.","journal-title":"Blood"},{"key":"5480_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.ejca.2019.09.027","volume":"124","author":"C Schmitz","year":"2020","unstructured":"Schmitz C, Huttmann A, Muller SP, Hanoun M, Boellaard R, Brinkmann M, et al. Dynamic risk assessment based on positron emission tomography scanning in diffuse large B-cell lymphoma: post-hoc analysis from the PETAL trial. Eur J Cancer. 2020;124:25\u201336. https:\/\/doi.org\/10.1016\/j.ejca.2019.09.027.","journal-title":"Eur J Cancer"},{"key":"5480_CR6","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1007\/s00259-016-3315-7","volume":"43","author":"NG Mikhaeel","year":"2016","unstructured":"Mikhaeel NG, Smith D, Dunn JT, Phillips M, Moller H, Fields PA, et al. Combination of baseline metabolic tumour volume and early response on PET\/CT improves progression-free survival prediction in DLBCL. Eur J Nucl Med Mol Imaging. 2016;43:1209\u201319. https:\/\/doi.org\/10.1007\/s00259-016-3315-7.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR7","doi-asserted-by":"publisher","first-page":"1417","DOI":"10.1007\/s00259-019-04309-4","volume":"46","author":"QA Shagera","year":"2019","unstructured":"Shagera QA, Cheon GJ, Koh Y, Yoo MY, Kang KW, Lee DS, et al. Prognostic value of metabolic tumour volume on baseline (18)F-FDG PET\/CT in addition to NCCN-IPI in patients with diffuse large B-cell lymphoma: further stratification of the group with a high-risk NCCN-IPI. Eur J Nucl Med Mol Imaging. 2019;46:1417\u201327. https:\/\/doi.org\/10.1007\/s00259-019-04309-4.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR8","doi-asserted-by":"publisher","first-page":"2017","DOI":"10.1007\/s00259-014-2822-7","volume":"41","author":"M Sasanelli","year":"2014","unstructured":"Sasanelli M, Meignan M, Haioun C, Berriolo-Riedinger A, Casasnovas RO, Biggi A, et al. Pretherapy metabolic tumour volume is an independent predictor of outcome in patients with diffuse large B-cell lymphoma. Eur J Nucl Med Mol Imaging. 2014;41:2017\u201322. https:\/\/doi.org\/10.1007\/s00259-014-2822-7.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR9","doi-asserted-by":"publisher","first-page":"3801","DOI":"10.1158\/1078-0432.CCR-15-2825","volume":"22","author":"AS Cottereau","year":"2016","unstructured":"Cottereau AS, Lanic H, Mareschal S, Meignan M, Vera P, Tilly H, et al. Molecular profile and FDG-PET\/CT total metabolic tumor volume improve risk classification at diagnosis for patients with diffuse large B-cell lymphoma. Clin Cancer Res. 2016;22:3801\u20139. https:\/\/doi.org\/10.1158\/1078-0432.CCR-15-2825.","journal-title":"Clin Cancer Res"},{"key":"5480_CR10","doi-asserted-by":"publisher","first-page":"4006","DOI":"10.1038\/ncomms5006","volume":"5","author":"HJ Aerts","year":"2014","unstructured":"Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. https:\/\/doi.org\/10.1038\/ncomms5006.","journal-title":"Nat Commun"},{"key":"5480_CR11","doi-asserted-by":"publisher","first-page":"2307","DOI":"10.1007\/s00259-018-4100-6","volume":"45","author":"EJ van Helden","year":"2018","unstructured":"van Helden EJ, Vacher YJL, van Wieringen WN, van Velden FHP, Verheul HMW, Hoekstra OS, et al. Radiomics analysis of pre-treatment [(18)F]FDG PET\/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging. 2018;45:2307\u201317. https:\/\/doi.org\/10.1007\/s00259-018-4100-6.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR12","doi-asserted-by":"publisher","first-page":"814","DOI":"10.2967\/jnumed.119.228106","volume":"61","author":"G Dissaux","year":"2020","unstructured":"Dissaux G, Visvikis D, Da-Ano R, Pradier O, Chajon E, Barillot I, et al. Pretreatment (18)F-FDG PET\/CT radiomics predict local recurrence in patients treated with stereotactic body radiotherapy for early-stage non-small cell lung cancer: A Multicentric Study. J Nucl Med. 2020;61:814\u201320. https:\/\/doi.org\/10.2967\/jnumed.119.228106.","journal-title":"J Nucl Med"},{"key":"5480_CR13","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/s00259-018-4250-6","volume":"46","author":"V Giannini","year":"2019","unstructured":"Giannini V, Mazzetti S, Bertotto I, Chiarenza C, Cauda S, Delmastro E, et al. Predicting locally advanced rectal cancer response to neoadjuvant therapy with (18)F-FDG PET and MRI radiomics features. Eur J Nucl Med Mol Imaging. 2019;46:878\u201388. https:\/\/doi.org\/10.1007\/s00259-018-4250-6.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR14","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1007\/s00330-020-06815-8","volume":"30","author":"N Aide","year":"2020","unstructured":"Aide N, Fruchart C, Nganoa C, Gac AC, Lasnon C. Baseline (18)F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy. Eur Radiol. 2020;30:4623\u201332. https:\/\/doi.org\/10.1007\/s00330-020-06815-8.","journal-title":"Eur Radiol"},{"key":"5480_CR15","doi-asserted-by":"publisher","first-page":"40","DOI":"10.2967\/jnumed.119.229450","volume":"61","author":"AS Cottereau","year":"2020","unstructured":"Cottereau AS, Nioche C, Dirand AS, Clerc J, Morschhauser F, Casasnovas O, et al. (18)F-FDG PET dissemination features in diffuse large B-cell lymphoma are predictive of outcome. J Nucl Med. 2020;61:40\u20135. https:\/\/doi.org\/10.2967\/jnumed.119.229450.","journal-title":"J Nucl Med"},{"key":"5480_CR16","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.19.03418","author":"PJ Lugtenburg","year":"2020","unstructured":"Lugtenburg PJ, de Nully Brown P, van der Holt B, D\u2019Amore FA, Koene HR, de Jongh E, et al. Rituximab-CHOP with early rituximab intensification for diffuse large B-cell lymphoma: a randomized phase III trial of the HOVON and the Nordic Lymphoma Group (HOVON-84). J Clin Oncol. 2020. https:\/\/doi.org\/10.1200\/JCO.19.03418.","journal-title":"J Clin Oncol"},{"key":"5480_CR17","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1007\/s00259-014-2961-x","volume":"42","author":"R Boellaard","year":"2015","unstructured":"Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET\/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42:328\u201354. https:\/\/doi.org\/10.1007\/s00259-014-2961-x.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR18","doi-asserted-by":"publisher","first-page":"1753","DOI":"10.2967\/jnumed.118.211607","volume":"59","author":"R Boellaard","year":"2018","unstructured":"Boellaard R. Quantitative oncology molecular analysis suite: ACCURATE. J Nucl Med. 2018;59:1753.","journal-title":"J Nucl Med"},{"key":"5480_CR19","doi-asserted-by":"publisher","first-page":"1102","DOI":"10.1007\/s11307-020-01474-z","volume":"22","author":"CN Burggraaff","year":"2020","unstructured":"Burggraaff CN, Rahman F, Kassner I, Pieplenbosch S, Barrington SF, Jauw YWS, et al. Optimizing workflows for fast and reliable metabolic tumor volume measurements in diffuse large B cell lymphoma. Mol Imaging Biol. 2020;22:1102\u201310. https:\/\/doi.org\/10.1007\/s11307-020-01474-z.","journal-title":"Mol Imaging Biol"},{"key":"5480_CR20","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.119.238923","author":"SF Barrington","year":"2020","unstructured":"Barrington SF, Zwezerijnen BG, de Vet HC, Heymans MW, Mikhaeel NG, Burggraaff CN, et al. Automated segmentation of baseline metabolic total tumor burden in diffuse large B-cell lymphoma: which method is most successful\u202f? J Nucl Med. 2020. https:\/\/doi.org\/10.2967\/jnumed.119.238923.","journal-title":"J Nucl Med"},{"key":"5480_CR21","doi-asserted-by":"publisher","first-page":"469","DOI":"10.2967\/jnumed.119.229724","volume":"61","author":"E Pfaehler","year":"2020","unstructured":"Pfaehler E, van Sluis J, Merema BBJ, van Ooijen P, Berendsen RCM, van Velden FHP, et al. Experimental multicenter and multivendor evaluation of the performance of PET radiomic features using 3-dimensionally printed phantom inserts. J Nucl Med. 2020;61:469\u201376. https:\/\/doi.org\/10.2967\/jnumed.119.229724.","journal-title":"J Nucl Med"},{"key":"5480_CR22","doi-asserted-by":"publisher","first-page":"e0212223","DOI":"10.1371\/journal.pone.0212223","volume":"14","author":"E Pfaehler","year":"2019","unstructured":"Pfaehler E, Zwanenburg A, de Jong JR, Boellaard R. RaCaT: an open source and easy to use radiomics calculator tool. PLoS ONE. 2019;14:e0212223. https:\/\/doi.org\/10.1371\/journal.pone.0212223.","journal-title":"PLoS ONE"},{"key":"5480_CR23","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1148\/radiol.2020191145","volume":"295","author":"A Zwanenburg","year":"2020","unstructured":"Zwanenburg A, Vallieres 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":"5480_CR24","doi-asserted-by":"publisher","first-page":"987","DOI":"10.1056\/NEJM199309303291402","volume":"329","author":"International Non-Hodgkin's Lymphoma Prognostic Factors P","year":"1993","unstructured":"International Non-Hodgkin\u2019s Lymphoma Prognostic Factors P. A predictive model for aggressive non-Hodgkin\u2019s lymphoma. N Engl J Med. 1993;329:987\u201394. https:\/\/doi.org\/10.1056\/NEJM199309303291402.","journal-title":"N Engl J Med"},{"key":"5480_CR25","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837\u201345.","journal-title":"Biometrics"},{"key":"5480_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77244-8","volume-title":"Clinical prediction models","author":"EW Steyerberg","year":"2009","unstructured":"Steyerberg EW. Clinical prediction models. New York: Springer; 2009."},{"key":"5480_CR27","doi-asserted-by":"publisher","first-page":"677","DOI":"10.1007\/s00259-018-3966-7","volume":"45","author":"N Aide","year":"2018","unstructured":"Aide N, Lasnon C, Damaj G. Combining baseline TMTV and gene profiling for a better risk stratification in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 2018;45:677\u20139. https:\/\/doi.org\/10.1007\/s00259-018-3966-7.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR28","doi-asserted-by":"publisher","first-page":"1396","DOI":"10.1182\/blood.2019003526","volume":"135","author":"L Vercellino","year":"2020","unstructured":"Vercellino L, Cottereau AS, Casasnovas O, Tilly H, Feugier P, Chartier L, et al. High total metabolic tumor volume at baseline predicts survival independent of response to therapy. Blood. 2020;135:1396\u2013405. https:\/\/doi.org\/10.1182\/blood.2019003526.","journal-title":"Blood"},{"key":"5480_CR29","doi-asserted-by":"publisher","first-page":"1142","DOI":"10.1007\/s00259-018-3953-z","volume":"45","author":"H Ilyas","year":"2018","unstructured":"Ilyas H, Mikhaeel NG, Dunn JT, Rahman F, Moller H, Smith D, et al. Defining the optimal method for measuring baseline metabolic tumour volume in diffuse large B cell lymphoma. Eur J Nucl Med Mol Imaging. 2018;45:1142\u201354. https:\/\/doi.org\/10.1007\/s00259-018-3953-z.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5480_CR30","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/s12149-018-1260-1","volume":"32","author":"A Parvez","year":"2018","unstructured":"Parvez A, Tau N, Hussey D, Maganti M, Metser U. (18)F-FDG PET\/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin\u2019s lymphoma as predictors of treatment outcome and survival. Ann Nucl Med. 2018;32:410\u20136. https:\/\/doi.org\/10.1007\/s12149-018-1260-1.","journal-title":"Ann Nucl Med"},{"key":"5480_CR31","doi-asserted-by":"publisher","first-page":"2286","DOI":"10.1182\/bloodadvances.2020001816","volume":"4","author":"H Senjo","year":"2020","unstructured":"Senjo H, Hirata K, Izumiyama K, Minauchi K, Tsukamoto E, Itoh K, et al. High metabolic heterogeneity on baseline 18FDG-PET\/CT scan as a poor prognostic factor for newly diagnosed diffuse large B-cell lymphoma. Blood Adv. 2020;4:2286\u201396. https:\/\/doi.org\/10.1182\/bloodadvances.2020001816.","journal-title":"Blood Adv"},{"key":"5480_CR32","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1182\/bloodadvances.2019001201","volume":"4","author":"L Ceriani","year":"2020","unstructured":"Ceriani L, Gritti G, Cascione L, Pirosa MC, Polino A, Ruberto T, et al. SAKK38\/07 study: integration of baseline metabolic heterogeneity and metabolic tumor volume in DLBCL prognostic model. Blood Adv. 2020;4:1082\u201392. https:\/\/doi.org\/10.1182\/bloodadvances.2019001201.","journal-title":"Blood Adv"},{"key":"5480_CR33","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.1158\/0008-5472.CAN-18-0125","volume":"78","author":"C Nioche","year":"2018","unstructured":"Nioche C, Orlhac F, Boughdad S, Reuze S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78:4786\u20139. https:\/\/doi.org\/10.1158\/0008-5472.CAN-18-0125.","journal-title":"Cancer Res"},{"key":"5480_CR34","doi-asserted-by":"publisher","first-page":"145","DOI":"10.2967\/jnumed.114.148056","volume":"56","author":"JJ Sunderland","year":"2015","unstructured":"Sunderland JJ, Christian PE. Quantitative PET\/CT scanner performance characterization based upon the society of nuclear medicine and molecular imaging clinical trials network oncology clinical simulator phantom. J Nucl Med. 2015;56:145\u201352. https:\/\/doi.org\/10.2967\/jnumed.114.148056.","journal-title":"J Nucl Med"},{"key":"5480_CR35","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s00259-017-3740-2","volume":"44","author":"N Aide","year":"2017","unstructured":"Aide N, Lasnon C, Veit-Haibach P, Sera T, Sattler B, Boellaard R. EANM\/EARL harmonization strategies in PET quantification: from daily practice to multicentre oncological studies. Eur J Nucl Med Mol Imaging. 2017;44:17\u201331. https:\/\/doi.org\/10.1007\/s00259-017-3740-2.","journal-title":"Eur J Nucl Med Mol Imaging"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-021-05480-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00259-021-05480-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-021-05480-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,31]],"date-time":"2022-01-31T12:04:01Z","timestamp":1643630641000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00259-021-05480-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,18]]},"references-count":35,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,2]]}},"alternative-id":["5480"],"URL":"https:\/\/doi.org\/10.1007\/s00259-021-05480-3","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,18]]},"assertion":[{"value":"7 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The HOVON-84 study was approved by the institutional review board of the Erasmus MC (2007\u2013055) and was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"All individual participants included in the HOVON-84 study gave written informed consent to participate in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"J.J.E., T.v.d.B., S.E.W., G.J.C.Z., E.A.G.P., B.v.d.H., H.C.W.d.V., O.S.H., and R.B. declare no competing financial interests. P.J.L. received research funding from Takeda, Servier, and Roche and received honoraria for advisory boards from Takeda, Servier, Genentech, Genmab, Celgene, and Incyte. J.M.Z. received research funding from Roche and received honoraria for advisory boards from Takeda, Gilead, and Roche.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}