{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T15:53:59Z","timestamp":1792857239911,"version":"4.5.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Nucl Med Mol Imaging"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and purpose<\/jats:title>\n                    <jats:p>Single-session, multiparametric [\u00b9\u2078F]FET PET\/MRI is used to detect tumor recurrence in high-grade glioma, but its prognostic value for overall survival remains uncertain. This study evaluated whether biological tumor volume, tumor-to-background ratio (TBRmax), cerebral blood volume (rCBVmax), and choline\/NAA ratio (Cho\/NAA) could predict survival in recurrent high-grade glioma.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>Twenty-six patients with histopathologically confirmed tumor progression underwent simultaneous [\u00b9\u2078F]FET PET\/MRI. PET-derived biological tumor volume and TBRmax, MRI-derived rCBVmax, and Cho\/NAA ratio were analyzed. A Cox proportional hazards model assessed associations with overall survival, adjusting for the number of lesions and treatment strategy.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Biological tumor volume (hazard ratio\u2009=\u20092.22, 95%-CI: 1.035\u20134.762,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.041) and the number of lesions (hazard ratio\u2009=\u20091.03, 95%-CI 1.00\u20131.06,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.036) were significantly associated with survival. TBRmax (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.089), rCBVmax (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.088), and Cho\/NAA ratio (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.734) were not predictive. Treatment strategy after tumor recurrence diagnosis did not significantly impact overall-survival (HR\u2009=\u20090.208,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.649). PET\/MRI interaction terms did not enhance survival prediction.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Biological tumor volume is a significant prognostic imaging biomarker in recurrent high-grade glioma, emphasizing tumor burden over metabolic activity or perfusion of individual lesions. Volume-based PET metrics may offer better survival prediction than traditional PET or MRI parameters. Prospective multicenter studies are needed to validate these findings and explore automated segmentation and machine learning approaches for improved prognostication.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00259-025-07469-8","type":"journal-article","created":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T02:53:20Z","timestamp":1753325600000},"page":"833-842","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Biological tumor volume predicts survival in recurrent High-Grade glioma: A multiparametric [18F]FET PET\/MRI study"],"prefix":"10.1007","volume":"53","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3915-3034","authenticated-orcid":false,"given":"Dylan","family":"Henssen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael","family":"Rullmann","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anne I.J.","family":"Arens","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Schildan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephan","family":"Striepe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matti","family":"Sch\u00fcrer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cordula","family":"Scherlach","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katja","family":"J\u00e4hne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruth","family":"Stassart","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osama","family":"Sabri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clemens","family":"Seidel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swen","family":"Hesse","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"7469_CR1","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"DN Louis","year":"2021","unstructured":"Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro Oncol. 2021;23:1231\u201351. https:\/\/doi.org\/10.1093\/neuonc\/noab106.","journal-title":"Neuro Oncol"},{"key":"7469_CR2","doi-asserted-by":"publisher","first-page":"1963","DOI":"10.1200\/JCO.2009.26.3541","volume":"28","author":"PY Wen","year":"2010","unstructured":"Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol. 2010;28:1963\u201372. https:\/\/doi.org\/10.1200\/JCO.2009.26.3541.","journal-title":"J Clin Oncol"},{"key":"7469_CR3","doi-asserted-by":"publisher","first-page":"787755","DOI":"10.3389\/fnins.2022.787755","volume":"16","author":"LR Carrete","year":"2022","unstructured":"Carrete LR, Young JS, Cha S. Advanced imaging techniques for newly diagnosed and recurrent gliomas. Front Neurosci. 2022;16:787755.","journal-title":"Front Neurosci"},{"key":"7469_CR4","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1002\/jmri.26171","volume":"48","author":"SC Thust","year":"2018","unstructured":"Thust SC, Van Den Bent MJ, Smits M. Pseudoprogression of brain tumors. J Magn Reson Imaging. 2018;48:571\u201389.","journal-title":"J Magn Reson Imaging"},{"key":"7469_CR5","doi-asserted-by":"publisher","first-page":"505","DOI":"10.2967\/jnumed.119.234757","volume":"61","author":"GD Maurer","year":"2020","unstructured":"Maurer GD, Brucker DP, Stoffels G, Filipski K, Filss CP, Mottaghy FM, et al. 18F-FET PET imaging in differentiating glioma progression from treatment-related changes: a single-center experience. J Nucl Med. 2020;61:505\u201311.","journal-title":"J Nucl Med"},{"key":"7469_CR6","doi-asserted-by":"publisher","first-page":"3577","DOI":"10.3390\/cancers15143577","volume":"15","author":"KJ Langen","year":"2023","unstructured":"Langen KJ, Galldiks N, Mauler J, Kocher M, Fil\u00df CP, Stoffels G, et al. Hybrid PET\/MRI in cerebral glioma: current status and perspectives. Cancers (Basel). 2023;15:3577. https:\/\/doi.org\/10.3390\/cancers15143577.","journal-title":"Cancers (Basel)"},{"key":"7469_CR7","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.3390\/diagnostics12051202","volume":"12","author":"T Van de Weijer","year":"2022","unstructured":"Van de Weijer T, Broen MPG, Moonen RPM, Hoeben A, Anten M, Hovinga K, et al. The use of (18)F-FET-PET-MRI in Neuro-Oncology: the best of both Worlds\u2014A. Narrative Rev Diagnostics (Basel). 2022;12:1205. https:\/\/doi.org\/10.3390\/diagnostics12051202.","journal-title":"Narrative Rev Diagnostics (Basel)"},{"key":"7469_CR8","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1212\/WNL.0000000000001262","volume":"84","author":"B Suchorska","year":"2015","unstructured":"Suchorska B, Jansen NL, Linn J, Kretzschmar H, Janssen H, Eigenbrod S, et al. Biological tumor volume in 18FET-PET before radiochemotherapy correlates with survival in GBM. Neurology. 2015;84:710\u20139. https:\/\/doi.org\/10.1212\/WNL.0000000000001262.","journal-title":"Neurology"},{"key":"7469_CR9","doi-asserted-by":"publisher","first-page":"1594","DOI":"10.2967\/jnumed.123.265725","volume":"64","author":"R Gutsche","year":"2023","unstructured":"Gutsche R, Lowis C, Ziemons K, Kocher M, Ceccon G, Regio Brambilla C, et al. Automated brain tumor detection and segmentation for treatment response assessment using amino acid PET. J Nucl Med. 2023;64:1594\u2013602. https:\/\/doi.org\/10.2967\/jnumed.123.265725.","journal-title":"J Nucl Med"},{"key":"7469_CR10","doi-asserted-by":"publisher","first-page":"918","DOI":"10.2967\/jnumed.120.254243","volume":"62","author":"G Ceccon","year":"2021","unstructured":"Ceccon G, Lohmann P, Werner JM, Tscherpel C, Dunkl V, Stoffels G, et al. Early treatment response assessment using (18)F-FET PET compared with Contrast-Enhanced MRI in glioma patients after adjuvant Temozolomide chemotherapy. J Nucl Med. 2021;62:918\u201325. https:\/\/doi.org\/10.2967\/jnumed.120.254243.","journal-title":"J Nucl Med"},{"key":"7469_CR11","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40644-024-00818-0","volume":"25","author":"Q Wan","year":"2025","unstructured":"Wan Q, Lindsay C, Zhang C, Kim J, Chen X, Li J, et al. Comparative analysis of deep learning and radiomic signatures for overall survival prediction in recurrent high-grade glioma treated with immunotherapy. Cancer Imaging. 2025;25:5. https:\/\/doi.org\/10.1186\/s40644-024-00818-0.","journal-title":"Cancer Imaging"},{"key":"7469_CR12","doi-asserted-by":"publisher","first-page":"100308","DOI":"10.1016\/j.wnsx.2024.100308","volume":"23","author":"ST Hansen","year":"2024","unstructured":"Hansen ST, Jacobsen KS, Kofoed MS, Petersen JK, Boldt HB, Dahlrot RH, et al. Prognostic factors to predict postoperative survival in patients with recurrent glioblastoma. Nucl Med. 2024;23:100308. https:\/\/doi.org\/10.1016\/j.wnsx.2024.100308.","journal-title":"Nucl Med"},{"key":"7469_CR13","doi-asserted-by":"publisher","first-page":"vdaf023","DOI":"10.1093\/noajnl\/vdaf023","volume":"7","author":"M Friedrich","year":"2025","unstructured":"Friedrich M, Werner JM, Steinbach JP, Sabel M, Herrlinger U, Piroth M, et al. Functional connectivity between tumor region and resting-state networks as imaging biomarker for overall survival in recurrent gliomas diagnosed by O-(2-[(18)F]fluoroethyl)-l-tyrosine PET. Neurooncol. Adv. 2025;7:vdaf023. https:\/\/doi.org\/10.1093\/noajnl\/vdaf023.","journal-title":"Adv"},{"key":"7469_CR14","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3413\/nuk-2011041","volume":"50","author":"K-J Langen","year":"2011","unstructured":"Langen K-J, Bartenstein P, Boecker H, Brust P, Coenen H, Drzezga A, et al. PET-und SPECT-Untersuchungen von Hirntumoren Mit Radioaktiv markierten aminos\u00e4uren. Nuklearmed. 2011;50:167\u201373.","journal-title":"Nuklearmed"},{"key":"7469_CR15","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejrad.2017.05.036","volume":"93","author":"R Gallicchio","year":"2017","unstructured":"Gallicchio R, Nardelli A, Venetucci A, Capacchione D, Pelagalli A, Sirignano C, et al. F-18 FDG PET\/CT metabolic tumor volume predicts overall survival in patients with disseminated epithelial ovarian cancer. Eur J Radiol. 2017;93:107\u201313. https:\/\/doi.org\/10.1016\/j.ejrad.2017.05.036.","journal-title":"Eur J Radiol"},{"key":"7469_CR16","doi-asserted-by":"publisher","first-page":"901390","DOI":"10.3389\/fonc.2022.901390","volume":"12","author":"M Waltenberger","year":"2022","unstructured":"Waltenberger M, Furkel J, Rohrich M, Salome P, Debus C, Tawk B, et al. The impact of tumor metabolic activity assessed by (18)F-FET amino acid PET imaging in particle radiotherapy of high-grade glioma patients. Front Oncol. 2022;12:901390. https:\/\/doi.org\/10.3389\/fonc.2022.901390.","journal-title":"Front Oncol"},{"key":"7469_CR17","doi-asserted-by":"publisher","first-page":"516","DOI":"10.2310\/7290.2012.00022","volume":"11","author":"N Galldiks","year":"2012","unstructured":"Galldiks N, Dunkl V, Kracht LW, Vollmar S, Jacobs AH, Fink GR, et al. Volumetry of [\u00b9\u00b9C]-methionine positron emission tomographic uptake as a prognostic marker before treatment of patients with malignant glioma. Mol Imaging. 2012;11:516\u201327.","journal-title":"Mol Imaging"},{"key":"7469_CR18","doi-asserted-by":"publisher","first-page":"1904","DOI":"10.2967\/jnumed.112.105544.18","volume":"53","author":"AJ Idema","year":"2012","unstructured":"Idema AJ, Hoffmann AL, Boogaarts HD, Troost EG, Wesseling P, Heerschap A, et al. 3\u2019-Deoxy-3\u2019-18F-fluorothymidine PET-derived proliferative volume predicts overall survival in high-grade glioma patients. J Nucl Med. 2012;53:1904\u201310. https:\/\/doi.org\/10.2967\/jnumed.112.105544.18.","journal-title":"J Nucl Med"},{"key":"7469_CR19","first-page":"776","volume":"47","author":"FW Floeth","year":"2006","unstructured":"Floeth FW, Pauleit D, Sabel M, Reifenberger G, Stoffels G, Stummer W, et al. 18F-FET PET differentiation of ring-enhancing brain lesions. J Nucl Med. 2006;47:776\u201382.","journal-title":"J Nucl Med"},{"key":"7469_CR20","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1093\/neuonc\/nos300","volume":"15","author":"M Hutterer","year":"2013","unstructured":"Hutterer M, Nowosielski M, Putzer D, Jansen NL, Seiz M, Schocke M, et al. [18F]-fluoro-ethyl-L-tyrosine PET: a valuable diagnostic tool in neuro-oncology, but not all that glitters is glioma. Neuro Oncol. 2013;15:341\u201351.","journal-title":"Neuro Oncol"},{"key":"7469_CR21","doi-asserted-by":"publisher","first-page":"e29","DOI":"10.1016\/S1470-2045(23)00525-9","volume":"25","author":"NL Albert","year":"2024","unstructured":"Albert NL, Galldiks N, Ellingson BM, van den Bent MJ, Chang SM, Cicone F, et al. PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group. Lancet Oncol. 2024;25:e29\u201341. https:\/\/doi.org\/10.1016\/S1470-2045(23)00525-9.","journal-title":"Lancet Oncol"},{"key":"7469_CR22","first-page":"374","volume":"45","author":"D Pauleit","year":"2004","unstructured":"Pauleit D, Floeth F, Tellmann L, Hamacher K, Hautzel H, M\u00fcller HW, et al. Comparison of O-(2-18F-fluoroethyl)-L-tyrosine PET and 3-123I-iodo-alpha-methyl-L-tyrosine SPECT in brain tumors. J Nucl Med. 2004;45:374\u201381.","journal-title":"J Nucl Med"},{"key":"7469_CR23","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. 2010. Available from: https:\/\/www.R-project.org\/"},{"key":"7469_CR24","doi-asserted-by":"publisher","first-page":"6143","DOI":"10.3892\/ol.2019.10954","volume":"18","author":"CY Lin","year":"2019","unstructured":"Lin CY, Chang YC, Wang IT, Hsieh MH, Wang CW, Lin SM, et al. Metabolic tumor volume predicts overall survival in patients with primary pulmonary lymphoepithelioma-like carcinoma. Oncol Lett. 2019;18:6143\u20139. https:\/\/doi.org\/10.3892\/ol.2019.10954.","journal-title":"Oncol Lett"},{"key":"7469_CR25","doi-asserted-by":"publisher","first-page":"1433012","DOI":"10.3389\/fimmu.2024.1433012","volume":"15","author":"L Liu","year":"2024","unstructured":"Liu L, Jin F, Fan H. Metabolic tumor volume and the survival of patients with Non-Hodgkin lymphoma treated with chimeric antigen receptor T cell therapy: a meta-analysis. Front Immunol. 2024;15:1433012. https:\/\/doi.org\/10.3389\/fimmu.2024.1433012.","journal-title":"Front Immunol"},{"key":"7469_CR26","doi-asserted-by":"publisher","first-page":"3744","DOI":"10.1002\/hed.26461","volume":"42","author":"J Rijo-Cede\u00f1o","year":"2020","unstructured":"Rijo-Cede\u00f1o J, Mucientes J, \u00c1lvarez O, Royuela A, Seijas Marcos S, Romero J, et al. Metabolic tumor volume and total lesion Glycolysis as prognostic factors in head and neck cancer: systematic review and meta-analysis. Head Neck. 2020;42:3744\u201354. https:\/\/doi.org\/10.1002\/hed.26461.","journal-title":"Head Neck"},{"key":"7469_CR27","doi-asserted-by":"publisher","first-page":"807","DOI":"10.2174\/1573405618666220329094423","volume":"19","author":"L Evangelista","year":"2023","unstructured":"Evangelista L, Urso L, Caracciolo M, Stracuzzi F, Panareo S, Cistaro A, et al. FDG PET\/CT Volume-Based quantitative data and survival analysis in breast Cancer patients: A systematic review of the literature. Curr Med Imaging. 2023;19:807\u201316. https:\/\/doi.org\/10.2174\/1573405618666220329094423.","journal-title":"Curr Med Imaging"},{"key":"7469_CR28","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1111\/bjh.18016","volume":"197","author":"TC El-Galaly","year":"2022","unstructured":"El-Galaly TC, Villa D, Cheah CY, Gormsen LC. Pre-treatment total metabolic tumour volumes in lymphoma: does quantity matter? Br J Haematol. 2022;197:139\u201355. https:\/\/doi.org\/10.1111\/bjh.18016.","journal-title":"Br J Haematol"},{"key":"7469_CR29","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.2967\/jnumed.124.267789","volume":"65","author":"R Boellaard","year":"2024","unstructured":"Boellaard R, Buvat I, Nioche C, Ceriani L, Cottereau AS, Guerra L, et al. International benchmark for total metabolic tumor volume measurement in baseline (18)F-FDG PET\/CT of lymphoma patients: A milestone toward clinical implementation. J Nucl Med. 2024;65:1343\u20138. https:\/\/doi.org\/10.2967\/jnumed.124.267789.","journal-title":"J Nucl Med"},{"key":"7469_CR30","doi-asserted-by":"publisher","DOI":"10.2967\/jnumed.124.269271","author":"R Boellaard","year":"2025","unstructured":"Boellaard R, Zwezerijnen GJC, Buvat I, Champion L, Hovhannisyan-Baghdasarian N, Orlhac F, et al. Measuring total metabolic tumor volume from (18)F-FDG PET: A reality check. J Nucl Med. 2025. https:\/\/doi.org\/10.2967\/jnumed.124.269271. (Epub ahead of print).","journal-title":"J Nucl Med"},{"key":"7469_CR31","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.3390\/cancers13133163","volume":"13","author":"LM Mittlmeier","year":"2021","unstructured":"Mittlmeier LM, Suchorska B, Ruf V, Holzgreve A, Brendel M, Herms J, et al. (18)F-FET PET uptake characteristics of Long-Term IDH-Wildtype diffuse glioma survivors. Cancers (Basel). 2021;13:3163. https:\/\/doi.org\/10.3390\/cancers13133163.","journal-title":"Cancers (Basel)"},{"key":"7469_CR32","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/s13014-020-01744-8","volume":"16","author":"M Carles","year":"2021","unstructured":"Carles M, Popp I, Starke MM, Mix M, Urbach H, Schimek-Jasch T, et al. FET-PET radiomics in recurrent glioblastoma: prognostic value for outcome after re-irradiation? Radiat Oncol. 2021;16:46. https:\/\/doi.org\/10.1186\/s13014-020-01744-8.","journal-title":"Radiat Oncol"},{"key":"7469_CR33","doi-asserted-by":"publisher","first-page":"3061","DOI":"10.2174\/0929867325666171123202644","volume":"25","author":"B Muoio","year":"2018","unstructured":"Muoio B, Giovanella L, Treglia G. Recent developments of 18F-FET PET in Neuro-oncology. Curr Med Chem. 2018;25:3061\u201373. https:\/\/doi.org\/10.2174\/0929867325666171123202644.","journal-title":"Curr Med Chem"},{"key":"7469_CR34","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.2967\/jnumed.111.102533","volume":"53","author":"T Singhal","year":"2012","unstructured":"Singhal T, Narayanan TK, Jacobs MP, Bal C, Mantil JC. 11\u00a0C-methionine PET for grading and prognostication in gliomas: a comparison study with 18F-FDG PET and contrast enhancement on MRI. J Nucl Med. 2012;53:1709\u201315. https:\/\/doi.org\/10.2967\/jnumed.111.102533.","journal-title":"J Nucl Med"},{"key":"7469_CR35","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1093\/neuonc\/nos112","volume":"14","author":"LS Hu","year":"2012","unstructured":"Hu LS, Eschbacher JM, Heiserman JE, Dueck AC, Shapiro WR, Liu S, et al. Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro Oncol. 2012;14:919\u201330. https:\/\/doi.org\/10.1093\/neuonc\/nos112.","journal-title":"Neuro Oncol"},{"key":"7469_CR36","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1017\/cjn.2019.225","volume":"46","author":"C Fong","year":"2019","unstructured":"Fong C, Parpia S, Yemen B, Tsai S, Greenspoon J. Using magnetic resonance perfusion to stratify overall survival in treated High-Grade gliomas. Can J Neurol Sci. 2019;46:533\u20139. https:\/\/doi.org\/10.1017\/cjn.2019.225.","journal-title":"Can J Neurol Sci"},{"key":"7469_CR37","doi-asserted-by":"publisher","first-page":"1149292","DOI":"10.3389\/fnins.2023.1149292","volume":"17","author":"B Sacli-Bilmez","year":"2023","unstructured":"Sacli-Bilmez B, Danyeli AE, Yakicier MC, Aras FK, Pamir MN, \u00d6zduman K, et al. Magnetic resonance spectroscopic correlates of progression free and overall survival in glioblastoma, IDH-wildtype, WHO grade-4. Front Neurosci. 2023;17:1149292. https:\/\/doi.org\/10.3389\/fnins.2023.1149292.","journal-title":"Front Neurosci"},{"key":"7469_CR38","doi-asserted-by":"publisher","unstructured":"Cepeda S, P\u00e9rez-Nu\u00f1ez A, Garc\u00eda-Garc\u00eda S, Garc\u00eda-P\u00e9rez D, Arrese I, Jim\u00e9nez-Rold\u00e1n L, et al. Predicting Short-Term survival after gross total or near total resection in glioblastomas by machine Learning-Based radiomic analysis of preoperative MRI. Cancers (Basel). 2021;13. https:\/\/doi.org\/10.3390\/cancers13205047.","DOI":"10.3390\/cancers13205047"},{"key":"7469_CR39","doi-asserted-by":"publisher","unstructured":"Ben Ahmed K, Hall LO, Goldgof DB, Gatenby R. Ensembles of convolutional neural networks for survival time Estimation of High-Grade glioma patients from multimodal MRI. Diagnostics (Basel). 2022;12. https:\/\/doi.org\/10.3390\/diagnostics12020345.","DOI":"10.3390\/diagnostics12020345"},{"key":"7469_CR40","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1093\/neuonc\/noae017","volume":"26","author":"A Chelliah","year":"2024","unstructured":"Chelliah A, Wood DA, Canas LS, Shuaib H, Currie S, Fatania K, et al. Glioblastoma and radiotherapy: A multicenter AI study for survival predictions from MRI (GRASP study). Neurooncology. 2024;26:1138\u201351. https:\/\/doi.org\/10.1093\/neuonc\/noae017.","journal-title":"Neurooncology"},{"key":"7469_CR41","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","volume":"14","author":"P Lambin","year":"2017","unstructured":"Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, et al. Radiomics: the Bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749\u201362. https:\/\/doi.org\/10.1038\/nrclinonc.2017.141.","journal-title":"Nat Rev Clin Oncol"},{"key":"7469_CR42","doi-asserted-by":"publisher","unstructured":"Garcia-Garcia S, Garcia-Galindo M, Arrese I, Sarabia R, Cepeda S. Current evidence, limitations and future challenges of survival prediction for glioblastoma based on advanced noninvasive methods: A narrative review. Med (Kaunas). 2022;58. https:\/\/doi.org\/10.3390\/medicina58121746.","DOI":"10.3390\/medicina58121746"},{"key":"7469_CR43","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s40001-025-02339-3","volume":"30","author":"WA Awuah","year":"2025","unstructured":"Awuah WA, Ben-Jaafar A, Roy S, Nkrumah-Boateng PA, Tan JK, Abdul-Rahman T, et al. Predicting survival in malignant glioma using artificial intelligence. Eur J Med Res. 2025;30:61. https:\/\/doi.org\/10.1186\/s40001-025-02339-3.","journal-title":"Eur J Med Res"}],"container-title":["European Journal of Nuclear Medicine and Molecular Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-025-07469-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00259-025-07469-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00259-025-07469-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T02:46:03Z","timestamp":1769222763000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00259-025-07469-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":43,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["7469"],"URL":"https:\/\/doi.org\/10.1007\/s00259-025-07469-8","relation":{},"ISSN":["1619-7070","1619-7089"],"issn-type":[{"value":"1619-7070","type":"print"},{"value":"1619-7089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]},"assertion":[{"value":"18 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 July 2025","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 study was conducted according to the guidelines of the Declaration of Helsinki. Ethical approval was approved by the Institutional Review Board (or Ethics Committee) of the medical faculty of the University of Leipzig (EC number 014\/21-ek). Clinical trial number not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Informed consent was obtained from all subjects involved in this study following a central opt-in\/opt-out procedure at our university medical center.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare that no conflicts of interest exist.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}