{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T12:34:41Z","timestamp":1789734881682,"version":"4.0.1"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T00:00:00Z","timestamp":1721779200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100007195","name":"Universit\u00e0 degli Studi di Napoli Federico II","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100007195","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Ann Hematol"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>In multiple myeloma (MM) bone marrow infiltration by monoclonal plasma cells can occur in both focal and diffuse manner, making staging and prognosis rather difficult. The aim of our study was to test whether texture analysis of 18\u00a0F-2-deoxy-d-glucose positron emission tomography\/computed tomography (18F-FDG PET\/CT) images can predict survival in MM patients. Forty-six patients underwent 18\u00a0F-FDG-PET\/CT before treatment. We used an automated contouring program for segmenting the hottest focal lesion (FL) and a lumbar vertebra for assessing diffuse bone marrow involvement (DI). Maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean) and texture features such as Coefficient of variation (CoV), were obtained from 46 FL and 46 DI. After a mean follow-up of 51 months, 24 patients died of myeloma and were compared to the 22 survivors. At univariate analysis, FL SUVmax (<jats:italic>p<\/jats:italic>\u2009=\u20090.0453), FL SUVmean (<jats:italic>p<\/jats:italic>\u2009=\u20090.0463), FL CoV (<jats:italic>p<\/jats:italic>\u2009=\u20090.0211) and DI SUVmax (<jats:italic>p<\/jats:italic>\u2009=\u20090.0538) predicted overall survival (OS). At multivariate analysis only FL CoV and DI SUVmax were retained in the model (<jats:italic>p<\/jats:italic>\u2009=\u20090.0154). By Kaplan-Meier method and log-rank testing, patients with FL CoV below the cut-off had significantly better OS than those with FL CoV above the cut-off (<jats:italic>p<\/jats:italic>\u2009=\u20090.0003), as well as patients with DI SUVmax below the threshold versus those with DI SUVmax above the threshold (<jats:italic>p<\/jats:italic>\u2009=\u20090.0006). Combining FL CoV and DI SUVmax by using their respective cut-off values, a statistically significant difference was found between the resulting four survival curves (<jats:italic>p<\/jats:italic>\u2009=\u20090.0001). Indeed, patients with both FL CoV and DI SUVmax below their respective cut-off values showed the best prognosis. Conventional and texture parameters derived from 18F-FDG PET\/CT analysis can predict survival in MM patients by assessing the heterogeneity and aggressiveness of both focal and diffuse infiltration.<\/jats:p>","DOI":"10.1007\/s00277-024-05905-7","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T09:05:27Z","timestamp":1721811927000},"page":"3713-3721","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Coefficient of variation and texture analysis of 18F-FDG PET\/CT images for the prediction of outcome in patients with multiple myeloma"],"prefix":"10.1007","volume":"103","author":[{"given":"Sara","family":"Pellegrino","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Origlia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erica","family":"Di Donna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Martina","family":"Lamagna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberta","family":"Della Pepa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabrizio","family":"Pane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvana","family":"Del Vecchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rosa","family":"Fonti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,24]]},"reference":[{"issue":"8","key":"5905_CR1","doi-asserted-by":"publisher","first-page":"1086","DOI":"10.1002\/ajh.26590","volume":"97","author":"SV Rajkumar","year":"2022","unstructured":"Rajkumar SV (2022) Multiple myeloma: 2022 update on diagnosis, risk stratification, and management. Am J Hematol 97(8):1086\u20131107. https:\/\/doi.org\/10.1002\/ajh.26590","journal-title":"Am J Hematol"},{"key":"5905_CR2","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1002\/hon.2586","volume":"37","author":"SV Rajkumar","year":"2019","unstructured":"Rajkumar SV (2019) Multiple myeloma: every year a new standard? Hematol Oncol 37:62\u201365. https:\/\/doi.org\/10.1002\/hon.2586","journal-title":"Hematol Oncol"},{"key":"5905_CR3","doi-asserted-by":"publisher","first-page":"3412","DOI":"10.1200\/JCO.2005.04.242","volume":"23","author":"PR Greipp","year":"2005","unstructured":"Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Blade J, Boccadoro M, Child JA, Avet-Loiseau H, Kyle RA, Lahuerta JJ, Ludwig H, Morgan G, Powles R, Shimizu K, Shustik C, Sonneveld P, Tosi P, Turesson I, Westin J (2005) International staging system for multiple myeloma. J Clin Oncol 23:3412\u20133420. https:\/\/doi.org\/10.1200\/JCO.2005.04.242","journal-title":"J Clin Oncol"},{"key":"5905_CR4","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s00259-018-4114-0","volume":"46","author":"SH Jung","year":"2019","unstructured":"Jung SH, Kwon SY, Min JJ, Bom HS, Ahn SY, Jung SY, Lee SS, Park MR, Yang DH, Ahn JS, Kim HJ, Lee JJ (2019) (18)F-FDG PET\/CT is useful for determining survival outcomes of patients with multiple myeloma classified as stage II and III with the revised international staging system. Eur J Nucl Med Mol Imaging 46:107\u2013115. https:\/\/doi.org\/10.1007\/s00259-018-4114-0","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5905_CR5","doi-asserted-by":"publisher","first-page":"1539","DOI":"10.1016\/j.ejca.2005.11.037","volume":"42","author":"BG Durie","year":"2006","unstructured":"Durie BG (2006) The role of anatomic and functional staging in myeloma: description of Durie\/Salmon plus staging system. Eur J Cancer 42:1539\u20131543. https:\/\/doi.org\/10.1016\/j.ejca.2005.11.037","journal-title":"Eur J Cancer"},{"issue":"25","key":"5905_CR6","doi-asserted-by":"publisher","first-page":"2911","DOI":"10.1200\/JCO.2017.72.2975","volume":"35","author":"P Moreau","year":"2017","unstructured":"Moreau P, Caillon F, Bodet-Milin C (2017) Prospective evaluation of magnetic resonance imaging and [18F]fluorodeoxyglucose positron emission tomography-computed tomography at diagnosis and before maintenance therapy in symptomatic patients with multiple myeloma included in the IFM\/DFCI 2009 trial: results of the IMAJEM study. J Clin Oncol 35(25):2911\u20132918. https:\/\/doi.org\/10.1200\/JCO.2017.72.2975","journal-title":"J Clin Oncol"},{"key":"5905_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.3389\/fmed.2019.00069","volume":"6","author":"B Jamet","year":"2019","unstructured":"Jamet B, Bailly C, Carlier T et al (2019) Interest of PET imaging in multiple myeloma. Front Med (Lausanne) 6:69. https:\/\/doi.org\/10.3389\/fmed.2019.00069","journal-title":"Front Med (Lausanne)"},{"issue":"17","key":"5905_CR8","doi-asserted-by":"publisher","first-page":"4323","DOI":"10.3390\/cancers13174323","volume":"13","author":"G Fouquet","year":"2021","unstructured":"Fouquet G, Wartski M, Dechmi A, Willems L, Deau-Fischer B, Franchi P et al (2021) Prognostic value of FDG-PET\/CT parameters in patients with relapse\/refractory multiple myeloma before anti-cd38 based therapy. Cancers 13(17):4323. https:\/\/doi.org\/10.3390\/cancers13174323","journal-title":"Cancers"},{"key":"5905_CR9","doi-asserted-by":"publisher","first-page":"1694","DOI":"10.1007\/s00259-018-3997-0","volume":"45","author":"A Stolzenburg","year":"2018","unstructured":"Stolzenburg A, L\u00fcckerath K, Samnick S et al (2018) Prognostic value of [18F]FDG-PET\/CT in multiple myeloma patients before and after allogeneic hematopoietic cell transplantation. Eur J Nucl Med Mol Imaging 45:1694\u20131704. https:\/\/doi.org\/10.1007\/s00259-018-3997-0","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5905_CR10","doi-asserted-by":"publisher","first-page":"e206","DOI":"10.1016\/S1470-2045(17)30189-4","volume":"18","author":"M Cavo","year":"2017","unstructured":"Cavo M, Terpos E, Nanni C, Moreau P, Lentzsch S, Zweegman S et al (2017) Role of 18F-FDG positron emission tomography\/computed tomography in the diagnosis and management of multiple myeloma and other plasma cell dyscrasias: a consensus statement by the international myeloma working group. Lancet Oncol 18:e206\u2013e217. https:\/\/doi.org\/10.1016\/S1470-2045(17)30189-4","journal-title":"Lancet Oncol"},{"issue":"2","key":"5905_CR11","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3390\/diagnostics11020210","volume":"11","author":"S Pellegrino","year":"2021","unstructured":"Pellegrino S, Fonti R, Pulcrano A, Del Vecchio S (2021) PET-Based volumetric biomarkers for risk stratification of non-small cell lung cancer patients. Diagnostics (Basel) 11(2):210. https:\/\/doi.org\/10.3390\/diagnostics11020210","journal-title":"Diagnostics (Basel)"},{"key":"5905_CR12","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s00259-012-2247-0","volume":"40","author":"S Chicklore","year":"2013","unstructured":"Chicklore S, Goh V, Siddique M, Roy A, Marsden PK, Cook GJR (2013) Quantifying tumour heterogeneity in 18F-FDG PET\/CT imaging by texture analysis. Eur J Nucl Med Mol Imaging 40:133\u2013140. https:\/\/doi.org\/10.1007\/s00259-012-2247-0","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"2","key":"5905_CR13","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 (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563\u2013577. https:\/\/doi.org\/10.1148\/radiol.2015151169","journal-title":"Radiology"},{"key":"5905_CR14","doi-asserted-by":"publisher","unstructured":"Hicks RJ The value of the standardized uptake value (SUV) and metabolic tumor volume (MTV) in lung cancer (2022). Semin Nucl Med 52(6):734\u2013744. https:\/\/doi.org\/10.1053\/j.semnuclmed.2022.04.007","DOI":"10.1053\/j.semnuclmed.2022.04.007"},{"issue":"12","key":"5905_CR15","doi-asserted-by":"publisher","first-page":"1829","DOI":"10.2967\/jnumed.112.106500","volume":"53","author":"R Fonti","year":"2012","unstructured":"Fonti R, Larobina M, Del Vecchio S, De Luca S, Fabbricini R, Catalano L, Pane F, Salvatore M, Pace L (2012) Metabolic tumor volume assessed by 18F-FDG PET\/CT for the prediction of outcome in patients with multiple myeloma. J Nucl Med 53(12):1829\u20131835. https:\/\/doi.org\/10.2967\/jnumed.112.106500","journal-title":"J Nucl Med"},{"key":"5905_CR16","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1158\/1078-0432.CCR-16-0235","volume":"23","author":"JE McDonald","year":"2017","unstructured":"McDonald JE, Kessler MM, Gardner MW, Buros AF, Ntambi JA, Waheed S, van Rhee F, Zangari M, Heuck CJ, Petty N et al (2017) Assessment of total lesion glycolysis by 18F FDG PET\/CT significantly improves prognostic value of GEP and ISS in myeloma. Clin Cancer Res 23:1981\u20131987. https:\/\/doi.org\/10.1158\/1078-0432.CCR-16-0235","journal-title":"Clin Cancer Res"},{"issue":"1","key":"5905_CR17","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1007\/s00277-019-03852-2","volume":"99","author":"R Fonti","year":"2020","unstructured":"Fonti R, Pellegrino S, Catalano L, Pane F, Del Vecchio S, Pace L (2020) Visual and volumetric parameters by 18F-FDG-PET\/CT: a head to head comparison for the prediction of outcome in patients with multiple myeloma. Ann Hematol 99(1):127\u2013135. https:\/\/doi.org\/10.1007\/s00277-019-03852-2","journal-title":"Ann Hematol"},{"key":"5905_CR18","doi-asserted-by":"publisher","unstructured":"Marchiori S, Cousin F, Papadopoulos I, Bernard C, Thys M, De Prijck B, Pirotte M, Donneau A-F, Hunstinx R, Caers J, Withofs (2024) Prognostic value of visual IMPeTUs criteria and metabolic tumor burden at baseline [18F]FDG PET\/CT in patients with newly diagnosed multiple myeloma. EJNMMI Res 14:51. https:\/\/doi.org\/10.1186\/s13550-024-01113-6","DOI":"10.1186\/s13550-024-01113-6"},{"key":"5905_CR19","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s12149-022-01812-x","volume":"37","author":"B Wan","year":"2023","unstructured":"Wan B, Zhang S, Wang P, Deng P, Dai W (2023) Prognostic value of semi\u2013quantitative parameters of 18F\u2013FDG PET\/CT in newly diagnosed multiple myeloma patients. Ann Nucl Med 37:155\u2013165. https:\/\/doi.org\/10.1007\/s12149-022-01812-x","journal-title":"Ann Nucl Med"},{"key":"5905_CR20","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1038\/s41467-017-00296-y","volume":"8","author":"L Rasche","year":"2017","unstructured":"Rasche L, Chavan SS, Stephens OW, Patel PH, Tytarenko R, Ashby C et al (2017) Spatial genomic heterogeneity in multiple myeloma revealed by multi-region sequencing. Nat Commun 8:268. https:\/\/doi.org\/10.1038\/s41467-017-00296-y","journal-title":"Nat Commun"},{"key":"5905_CR21","doi-asserted-by":"publisher","first-page":"488","DOI":"10.2967\/jnumed.118.222893","volume":"61","author":"ME Mayerhoefer","year":"2020","unstructured":"Mayerhoefer ME, Materka A, Langs G, Haggstrom I, Szczypinski P, Gibbs P, Cook G (2020) Introduction to radiomics. J Nucl Med 61:488\u2013495. https:\/\/doi.org\/10.2967\/jnumed.118.222893","journal-title":"J Nucl Med"},{"key":"5905_CR22","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1007\/s00330-020-07141-9","volume":"31","author":"MR Chetan","year":"2021","unstructured":"Chetan MR, Gleeson FV (2021) Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 31:1049\u20131058. https:\/\/doi.org\/10.1007\/s00330-020-07141-9","journal-title":"Eur Radiol"},{"key":"5905_CR23","doi-asserted-by":"publisher","unstructured":"Lubner MG, Smith AD, Sandrasegaran K, Sahani D, Pickhardt PJ PJ (2017) CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics 37:1483\u20131503. https:\/\/doi.org\/10.1148\/rg.2017170056","DOI":"10.1148\/rg.2017170056"},{"key":"5905_CR24","doi-asserted-by":"publisher","first-page":"380","DOI":"10.3390\/diagnostics11020380","volume":"11","author":"M Pi\u00f1eiro-Fiel","year":"2021","unstructured":"Pi\u00f1eiro-Fiel M, Moscoso A, Pubul V, Ruibal A, Silva-Rodriguez J, Aguiar P (2021) A systematic review of PET textural analysis and radiomics in cancer. Diagnostics 11:380. https:\/\/doi.org\/10.3390\/diagnostics11020380","journal-title":"Diagnostics"},{"key":"5905_CR25","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1007\/s40336-018-0299-2","volume":"6","author":"P Lovinfosse","year":"2018","unstructured":"Lovinfosse P, Hatt M, Visvikis D, Hustinx R (2018) Heterogeneity analysis of 18F\u2013FDG PET imaging in oncology: clinical indications and perspectives. Clin Transl Imaging 6:393\u2013410. https:\/\/doi.org\/10.1007\/s40336-018-0299-2","journal-title":"Clin Transl Imaging"},{"issue":"1","key":"5905_CR26","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s12880-023-01033-2","volume":"23","author":"H Zhong","year":"2023","unstructured":"Zhong H, Huang D, Wu J, Chen X, Chen Y, Huang C (2023) 18F\u2013FDG PET\/CT based radiomics features improve prediction of prognosis: multiple machine learning algorithms and multimodality applications for multiple myeloma. BMC Med Imaging 23(1):87. https:\/\/doi.org\/10.1186\/s12880-023-01033-2","journal-title":"BMC Med Imaging"},{"issue":"6","key":"5905_CR27","doi-asserted-by":"publisher","first-page":"2280","DOI":"10.3390\/jcm12062280","volume":"12","author":"B Ni","year":"2023","unstructured":"Ni B, Huang G, Huang H, Wang T, Han X, Shen L, Chen Y, Hou J (2023) Machine learning model based on optimized Radiomics feature from 18F-FDG-PET\/CT and clinical characteristics predicts prognosis of multiple myeloma: a preliminary study. J Clin Med 12(6):2280. https:\/\/doi.org\/10.3390\/jcm12062280","journal-title":"J Clin Med"},{"issue":"1","key":"5905_CR28","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1007\/s11548-019-02015-y","volume":"15","author":"L Morvan","year":"2020","unstructured":"Morvan L, Carlier T, Jamet B, Bailly C, Bodet-Milin C, Moreau P, Kraeber-Bod\u00e9r\u00e9 F, Mateus D (2020) Leveraging RSF and PET images for prognosis of multiple myeloma at diagnosis. Int J Comput Assist Radiol Surg 15(1):129\u2013139. https:\/\/doi.org\/10.1007\/s11548-019-02015-y","journal-title":"Int J Comput Assist Radiol Surg"},{"issue":"4","key":"5905_CR29","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1007\/s00259-020-05049-6","volume":"48","author":"B Jamet","year":"2021","unstructured":"Jamet B, Morvan L, Nanni C, Michaud AV, Bailly C, Chauvie S, Moreau P, Touzeau C, Zamagni E, Bodet-Milin C, Kraeber-Bod\u00e9r\u00e9 F, Mateus D, Carlier T (2021) Random survival forest to predict transplant-eligible newly diagnosed multiple myeloma outcome including FDG-PET radiomics: a combined analysis of two independent prospective European trials. Eur J Nucl Med Mol Imaging 48(4):1005\u20131015. https:\/\/doi.org\/10.1007\/s00259-020-05049-6","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"14","key":"5905_CR30","doi-asserted-by":"publisher","first-page":"2448","DOI":"10.3390\/diagnostics13142448","volume":"13","author":"S Pellegrino","year":"2023","unstructured":"Pellegrino S, Fonti R, Hakkak Moghadam Torbati A, Bologna R, Morra R, Damiano V, Matano E, De Placido S, Del Vecchio S (2023) Heterogeneity of glycolytic phenotype determined by 18F-FDG PET\/CT using coefficient of variation in patients with advanced non-small cell lung cancer. Diagnostics (Basel) 13(14):2448. https:\/\/doi.org\/10.3390\/diagnostics13142448","journal-title":"Diagnostics (Basel)"},{"issue":"2","key":"5905_CR31","doi-asserted-by":"publisher","first-page":"279","DOI":"10.3390\/cancers16020279","volume":"16","author":"S Pellegrino","year":"2024","unstructured":"Pellegrino S, Fonti R, Vallone C, Morra R, Matano E, De Placido S, Del Vecchio S (2024) Coefficient of variation in metastatic lymph nodes determined by 18F-FDG PET\/CT in patients with advanced NSCLC: combination with coefficient of variation in primary tumors. Cancers (Basel) 16(2):279. https:\/\/doi.org\/10.3390\/cancers16020279","journal-title":"(Basel)"},{"issue":"16","key":"5905_CR32","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, Reuz\u00e9 S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality Imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78(16):4786\u20134789. https:\/\/doi.org\/10.1158\/0008-5472.CAN-18-0125","journal-title":"Cancer Res"},{"issue":"12","key":"5905_CR33","doi-asserted-by":"publisher","first-page":"937","DOI":"10.1007\/s12149-019-01407-z","volume":"33","author":"S Pellegrino","year":"2019","unstructured":"Pellegrino S, Fonti R, Mazziotti E, Piccin L, Mozzillo E, Damiano V, Matano E, De Placido S, Del Vecchio S (2019) Total metabolic tumor volume by 18F-FDG PET\/CT for the prediction of outcome in patients with non-small cell lung cancer. Ann Nucl Med 33(12):937\u2013944. https:\/\/doi.org\/10.1007\/s12149-019-01407-z","journal-title":"Ann Nucl Med"},{"issue":"2","key":"5905_CR34","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/s00259-014-2903-7","volume":"42","author":"HJ Im","year":"2015","unstructured":"Im HJ, Pak K, Cheon GJ, Kang KW, Kim SJ, Kim IJ, Chung JK, Kim EE, Lee DS (2015) Prognostic value of volumetric parameters of (18)F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging 42(2):241\u2013251. https:\/\/doi.org\/10.1007\/s00259-014-2903-7","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"5905_CR35","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/s40336-018-0292-9","volume":"6","author":"P Lovinfosse","year":"2018","unstructured":"Lovinfosse P, Visvikis D, Hustinx R, Hatt M (2018) FDG PET radiomics: a review of the methodological aspects. Clin Transl Imaging 6:379\u2013391. https:\/\/doi.org\/10.1007\/s40336-018-0292-9","journal-title":"Clin Transl Imaging"},{"issue":"10","key":"5905_CR36","doi-asserted-by":"publisher","first-page":"1509","DOI":"10.2967\/jnumed.121.262928","volume":"63","author":"R Fonti","year":"2022","unstructured":"Fonti R, Panico M, Pellegrino S, Pulcrano A, Vastarella LA, Torbati AHM, Giuliano M, Palmieri G, De Placido S, Del Vecchio S (2022) Heterogeneity of SSTR2 expression assessed by 68Ga-DOTATOC PET\/CT using coefficient of variation in patients with neuroendocrine tumors. J Nucl Med 63(10):1509\u20131514. https:\/\/doi.org\/10.2967\/jnumed.121.262928","journal-title":"J Nucl Med"},{"issue":"6","key":"5905_CR37","doi-asserted-by":"publisher","first-page":"e0157836","DOI":"10.1371\/journal.pone.0157836","volume":"11","author":"X Dong","year":"2016","unstructured":"Dong X, Sun X, Sun L, Maxim PG, Xing L, Huang Y, Li W, Wan H, Zhao X, Xing L, Yu J (2016) Early change in metabolic tumor heterogeneity during Chemoradiotherapy and its prognostic value for patients with locally advanced non-small cell lung cancer. PLoS ONE 11(6):e0157836. https:\/\/doi.org\/10.1371\/journal.pone.0157836","journal-title":"PLoS ONE"},{"issue":"19","key":"5905_CR38","doi-asserted-by":"publisher","first-page":"5901","DOI":"10.3748\/wjg.v21.i19.5901","volume":"21","author":"K Grabinska","year":"2015","unstructured":"Grabinska K, Pelak MJ, Wydmanski J, Tukiendorf A, D\u2019Amico A (2015) Prognostic value and clinical correlations of 18-fluorodeoxyglucose metabolism quantifiers in gastric cancer. World J Gastroenterol 21(19):5901\u20135909. https:\/\/doi.org\/10.3748\/wjg.v21.i19.5901","journal-title":"World J Gastroenterol"},{"issue":"6","key":"5905_CR39","doi-asserted-by":"publisher","first-page":"891","DOI":"10.2967\/jnumed.113.127340","volume":"55","author":"RA Bundschuh","year":"2014","unstructured":"Bundschuh RA, Dinges J, Neumann L, Seyfried M, Zs\u00f3t\u00e9r N, Papp L, Rosenberg R, Becker K, Astner ST, Henninger M, Herrmann K, Ziegler SI, Schwaiger M, Essler M (2014) Textural parameters of tumor heterogeneity in \u00b9\u2078F-FDG PET\/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 55(6):891\u2013897. https:\/\/doi.org\/10.2967\/jnumed.113.127340","journal-title":"J Nucl Med"},{"issue":"3","key":"5905_CR40","doi-asserted-by":"publisher","first-page":"472","DOI":"10.3390\/biomedicines12030472","volume":"12","author":"A Hakkak Moghadam Torbati","year":"2024","unstructured":"Hakkak Moghadam Torbati A, Pellegrino S, Fonti R, Morra R, De Placido S, Del Vecchio S (2024) Machine learning and texture analysis of [18F]FDG PET\/CT images for the prediction of distant metastases in non-small-cell lung cancer patients. Biomedicines 12(3):472. https:\/\/doi.org\/10.3390\/biomedicines12030472","journal-title":"Biomedicines"},{"issue":"7","key":"5905_CR41","doi-asserted-by":"publisher","first-page":"2056","DOI":"10.3390\/cancers15072056","volume":"15","author":"C Ortega","year":"2023","unstructured":"Ortega C, Eshet Y, Prica A, Anconina R, Johnson S, Constantini D, Keshavarzi S, Kulanthaivelu R, Metser U, Veit-Haibach P (2023) Combination of FDG PET\/CT radiomics and clinical parameters for Outcome Prediction in patients with Hodgkin\u2019s lymphoma. Cancers (Basel) 15(7):2056. https:\/\/doi.org\/10.3390\/cancers15072056","journal-title":"Cancers (Basel)"},{"issue":"11","key":"5905_CR42","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.1097\/MNM.0000000000001447","volume":"42","author":"N Coskun","year":"2021","unstructured":"Coskun N, Okudan B, Uncu D, Kitapci MT (2021) Baseline 18F-FDG PET textural features as predictors of response to chemotherapy in diffuse large B-cell lymphoma. Nucl Med Commun 42(11):1227\u20131232. https:\/\/doi.org\/10.1097\/MNM.0000000000001447","journal-title":"Nucl Med Commun"},{"issue":"24","key":"5905_CR43","doi-asserted-by":"publisher","first-page":"7669","DOI":"10.3390\/jcm12247669","volume":"12","author":"L Manco","year":"2023","unstructured":"Manco L, Albano D, Urso L, Arnaboldi M, Castellani M, Florimonte L, Guidi G, Turra A, Castello A, Panareo S (2023) Positron emission tomography-derived radiomics and artificial intelligence in multiple myeloma: state-of-the-art. J Clin Med 12(24):7669. https:\/\/doi.org\/10.3390\/jcm12247669","journal-title":"J Clin Med"},{"key":"5905_CR44","doi-asserted-by":"publisher","first-page":"195","DOI":"10.2967\/jnumed.107.045641","volume":"49","author":"R Fonti","year":"2008","unstructured":"Fonti R, Salvatore B, Quarantelli M, Sirignano C, Segreto S, Petruzziello F, Catalano L, Liuzzi R, Rotoli B, Del Vecchio S, Pace L, Salvatore M (2008) 18F-FDG PET\/CT, 99mTc-MIBI, and MRI in evaluation of patients with multiple myeloma. J Nucl Med 49:195\u2013200. https:\/\/doi.org\/10.2967\/jnumed.107.045641","journal-title":"J Nucl Med"},{"key":"5905_CR45","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1007\/s00259-017-3909-8","volume":"45","author":"C Nanni","year":"2018","unstructured":"Nanni C, Versari A, Chauvie S, Bertone E, Bianchi A, Rensi M, Bello M, Gallamini A, Patriarca F, Gay F, Gamberi B, Ghedini P, Cavo M, Fanti S, Zamagni E (2018) Interpretation criteria for FDG PET\/CT in multiple myeloma (IMPeTUs): final results. IMPeTUs (Italian myeloma criteria for PET USe). Eur J Nucl Med Mol Imaging 45:712\u2013719. https:\/\/doi.org\/10.1007\/s00259-017-3909-8","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"4","key":"5905_CR46","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1097\/RLU.0000000000000696","volume":"40","author":"R Fonti","year":"2015","unstructured":"Fonti R, Pace L, Cerchione C, Catalano L, Salvatore B, De Luca S, Pane F, Salvatore M, Del Vecchio S (2015) 18F-FDG PET\/CT, 99mTc-MIBI, and MRI in the prediction of outcome of patients with multiple myeloma: a comparative study. Clin Nucl Med 40(4):303\u2013308. https:\/\/doi.org\/10.1097\/RLU.0000000000000696","journal-title":"Clin Nucl Med"},{"issue":"1","key":"5905_CR47","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1186\/s13550-021-00846-y","volume":"11","author":"C Sachpekidis","year":"2021","unstructured":"Sachpekidis C, Merz M, Raab M, Bertsch U, Weru V, Kopp-Schneider A, Jauch A, Goldschmidt H, Dimitrakopoulou-Strauss A (2021) The prognostic significance of [18F]FDG PET\/CT in multiple myeloma according to novel interpretation criteria (IMPeTUs). EJNMMI Res 11(1):100. https:\/\/doi.org\/10.1186\/s13550-021-00846-y","journal-title":"EJNMMI Res"},{"issue":"2","key":"5905_CR48","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1007\/s13246-023-01265-0","volume":"46","author":"E Milara","year":"2023","unstructured":"Milara E, Alonso R, Masseing L, Seiffert AP, G\u00f3mez-Grande A, G\u00f3mez EJ, Mart\u00ednez-L\u00f3pez J, S\u00e1nchez-Gonz\u00e1lez P (2023) Radiomics analysis of bone marrow biopsy locations in [18F]FDG PET\/CT images for measurable residual disease assessment in multiple myeloma. Phys Eng Sci Med 46(2):903\u2013913. https:\/\/doi.org\/10.1007\/s13246-023-01265-0","journal-title":"Phys Eng Sci Med"}],"container-title":["Annals of Hematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00277-024-05905-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00277-024-05905-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00277-024-05905-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T09:18:19Z","timestamp":1724836699000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00277-024-05905-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,24]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2024,9]]}},"alternative-id":["5905"],"URL":"https:\/\/doi.org\/10.1007\/s00277-024-05905-7","relation":{},"ISSN":["0939-5555","1432-0584"],"issn-type":[{"value":"0939-5555","type":"print"},{"value":"1432-0584","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,24]]},"assertion":[{"value":"28 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 July 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2024","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 research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. This retrospective study was approved by by Ethics Committee of University of Naples \u201cFederico II\u201d (16\/01\/2019; Protocol N. 352\/18).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all subjects involved in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}