{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T08:07:07Z","timestamp":1788941227350,"version":"build-2803163510"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Ascent Project of Xuanwu Hospital","award":["HZ2021ZCLJ005"],"award-info":[{"award-number":["HZ2021ZCLJ005"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroradiology"],"DOI":"10.1007\/s00234-026-04077-7","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T07:41:27Z","timestamp":1788939687000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiparametric static hybrid \u00b9\u2078F-FET PET\/MR for preoperative prediction of TERT promoter mutation in glioblastoma"],"prefix":"10.1007","author":[{"given":"Xiaohong","family":"Liang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leiming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huantong","family":"Diao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fengqi","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"issue":"8","key":"4077_CR1","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1093\/neuonc\/noab106","volume":"23","author":"N David","year":"2021","unstructured":"David N, Louis, Arie P, Pieter W et al (2021) The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23(8):1231\u20131251. https:\/\/doi.org\/10.1093\/neuonc\/noab106","journal-title":"Neuro Oncol"},{"issue":"26","key":"4077_CR2","doi-asserted-by":"publisher","first-page":"2499","DOI":"10.1056\/NEJMoa1407279","volume":"372","author":"E Jeanette","year":"2015","unstructured":"Jeanette E, Eckel-Passow DH, Lachance, Annette M, Molinaro et al (2015) Glioma Groups Based on 1p\/19q, IDH, and TERT Promoter Mutations in Tumors. N Engl J Med 372(26):2499\u20132508. https:\/\/doi.org\/10.1056\/NEJMoa1407279","journal-title":"N Engl J Med"},{"issue":"3","key":"4077_CR3","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41571-020-00447-z","volume":"18","author":"M Michael, Weller","year":"2020","unstructured":"Michael, Weller M, van den Bent M, Preusser et al (2020) EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 18(3):170\u2013186. https:\/\/doi.org\/10.1038\/s41571-020-00447-z","journal-title":"Nat Rev Clin Oncol"},{"key":"4077_CR4","doi-asserted-by":"publisher","unstructured":"Anna W, Sandra F, Vega, Teresia K et al Intratumor DNA methylation heterogeneity in glioblastoma: implications for DNA methylation-based classification. Neuro Oncol, 21 (5): 616\u2013627. https:\/\/doi.org\/10.1093\/neuonc\/noz011","DOI":"10.1093\/neuonc\/noz011"},{"issue":"3","key":"4077_CR5","doi-asserted-by":"publisher","first-page":"540","DOI":"10.1007\/s00259-018-4207-9","volume":"46","author":"L Ian","year":"2018","unstructured":"Ian L, Albert NL, Javier A et al (2018) Joint EANM\/EANO\/RANO practice guidelines\/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. Eur J Nucl Med Mol Imaging 46(3):540\u2013557. https:\/\/doi.org\/10.1007\/s00259-018-4207-9","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"3","key":"4077_CR6","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1097\/RLU.0000000000002398","volume":"44","author":"S Kebir","year":"2019","unstructured":"Kebir S, Weber M, Lazaridis L et al (2019) Hybrid 11\u00a0C-MET PET\/MRI Combined With Machine Learning in Glioma Diagnosis According to the Revised Glioma WHO Classification 2016. Clin Nucl Med 44(3):214\u2013220. https:\/\/doi.org\/10.1097\/RLU.0000000000002398","journal-title":"Clin Nucl Med"},{"key":"4077_CR7","doi-asserted-by":"publisher","first-page":"7598","DOI":"10.3390\/ijms21207598","volume":"21","author":"A Cimini","year":"2020","unstructured":"Cimini A, Chiaravalloti A, Ricci M, Villani V, Vanni G, Schillaci O (2020) MGMT promoter methylation and IDH1 mutations do not affect [18F] FDOPA uptake in primary brain tumors. Int J Mol Sci 21:7598. https:\/\/doi.org\/10.3390\/ijms21207598","journal-title":"Int J Mol Sci"},{"issue":"6","key":"4077_CR8","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1007\/s00259-019-04656-2","volume":"47","author":"S Song","year":"2020","unstructured":"Song S, Cheng Y, Ma J et al (2020) Simultaneous FET-PET and contrast-enhanced MRI based on hybrid PET\/MR improves delineation of tumor spatial biodistribution in gliomas: a biopsy validation study. Eur J Nucl Med Mol Imaging 47(6):1458\u20131467. https:\/\/doi.org\/10.1007\/s00259-019-04656-2","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"6","key":"4077_CR9","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1007\/s00259-024-07053-6","volume":"52","author":"A Shamimeh","year":"2025","unstructured":"Shamimeh A, Timoth\u00e9e Z, Adeline Z et al (2025) Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma. Eur J Nucl Med Mol Imaging 52(6):1989\u20132001. https:\/\/doi.org\/10.1007\/s00259-024-07053-6","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"1","key":"4077_CR10","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1186\/s40364-023-00476-7","volume":"11","author":"Fang-Ying","year":"2023","unstructured":"Fang-Ying (2023) Chiu, Yun, Yen, Imaging biomarkers for clinical applications in neuro-oncology: current status and future perspectives. Biomark Res 11(1):35. https:\/\/doi.org\/10.1186\/s40364-023-00476-7","journal-title":"Biomark Res"},{"issue":"9","key":"4077_CR11","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1093\/neuonc\/now058","volume":"18","author":"L Nathalie","year":"2016","unstructured":"Nathalie L, Albert M, Weller, Bogdana S et al (2016) Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro Oncol 18(9):1199\u20131208. https:\/\/doi.org\/10.1093\/neuonc\/now058","journal-title":"Neuro Oncol"},{"issue":"1","key":"4077_CR12","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1002\/jmri.26562","volume":"50","author":"H Pang","year":"2019","unstructured":"Pang H, Dang X, Ren Y et al (2019) 3D-ASL perfusion correlates with VEGF expression and overall survival in glioma patients: Comparison of quantitative perfusion and pathology on accurate spatial location-matched basis. J Magn Reson Imaging 50(1):209\u2013220. https:\/\/doi.org\/10.1002\/jmri.26562","journal-title":"J Magn Reson Imaging"},{"issue":"4","key":"4077_CR13","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/s00234-018-1993-5","volume":"60","author":"C Brendle","year":"2018","unstructured":"Brendle C, Hempel JM, Schittenhelm J et al (2018) Glioma grading by dynamic susceptibility contrast perfusion and 11\u00a0C-methionine positron emission tomography using different regions of interest. Neuroradiology 60(4):381\u2013389. https:\/\/doi.org\/10.1007\/s00234-018-1993-5","journal-title":"Neuroradiology"},{"issue":"2","key":"4077_CR14","doi-asserted-by":"publisher","first-page":"1202","DOI":"10.1007\/s00330-019-06379-2","volume":"30","author":"RE Yoo","year":"2020","unstructured":"Yoo RE, Yun TJ, Hwang I et al (2020) Arterial spin labeling perfusion-weighted imaging aids in prediction of molecular biomarkers and survival in glioblastomas. Eur Radiol 30(2):1202\u20131211. https:\/\/doi.org\/10.1007\/s00330-019-06379-2","journal-title":"Eur Radiol"},{"issue":"1","key":"4077_CR15","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1186\/s12885-018-4114-2","volume":"18","author":"Y Han","year":"2018","unstructured":"Han Y, Yan LF, Wang XB et al (2018) Structural and advanced imaging in predicting MGMT promoter methylation of primary glioblastoma: a region of interest based analysis. BMC Cancer 18(1):215. https:\/\/doi.org\/10.1186\/s12885-018-4114-2","journal-title":"BMC Cancer"},{"issue":"2","key":"4077_CR16","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1037\/0033-2909.86.2.420","volume":"86","author":"PE Shrout","year":"1979","unstructured":"Shrout PE, Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psychol Bull 86(2):420\u2013428. https:\/\/doi.org\/10.1037\/0033-2909.86.2.420","journal-title":"Psychol Bull"},{"issue":"2","key":"4077_CR17","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jcm.2016.02.012","volume":"15","author":"TK Koo","year":"2016","unstructured":"Koo TK, Li MY (2016) A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med 15(2):155\u2013163. https:\/\/doi.org\/10.1016\/j.jcm.2016.02.012","journal-title":"J Chiropr Med"},{"issue":"1","key":"4077_CR18","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(1):159\u2013174. https:\/\/doi.org\/10.2307\/2529310","journal-title":"Biometrics"},{"issue":"3","key":"4077_CR19","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.tjem.2018.08.001","volume":"18","author":"H Akoglu","year":"2018","unstructured":"Akoglu H (2018) User\u2019s guide to correlation coefficients. Turkish J Emerg Med 18(3):91\u201393. https:\/\/doi.org\/10.1016\/j.tjem.2018.08.001","journal-title":"Turkish J Emerg Med"},{"issue":"1","key":"4077_CR20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1007\/s00234-023-03245-3","volume":"66","author":"Z Hongbo","year":"2023","unstructured":"Hongbo Z, Hanwen Z, Yuze Z et al (2023) Multiparametric MRI-based fusion radiomics for predicting telomerase reverse transcriptase (TERT) promoter mutations and progression-free survival in glioblastoma: a multicentre study. Neuroradiology 66(1):81\u201392. https:\/\/doi.org\/10.1007\/s00234-023-03245-3","journal-title":"Neuroradiology"},{"issue":"5","key":"4077_CR21","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1002\/jmri.28671","volume":"58","author":"Z Hongbo","year":"2023","unstructured":"Hongbo Z, Hanwen Z, Yuze Z et al (2023) Deep Learning Radiomics for the Assessment of Telomerase Reverse Transcriptase Promoter Mutation Status in Patients With Glioblastoma Using Multiparametric MRI. J Magn Reson Imaging 58(5):1441\u20131451. https:\/\/doi.org\/10.1002\/jmri.28671","journal-title":"J Magn Reson Imaging"},{"issue":"5","key":"4077_CR22","doi-asserted-by":"publisher","first-page":"e3528","DOI":"10.1002\/brb3.3528","volume":"14","author":"L Yao","year":"2024","unstructured":"Yao L, Ling C, Lizhao, Huang et al (2024) A radiomics-based nomogram may be useful for predicting telomerase reverse transcriptase promoter mutation status in adult glioblastoma. Brain Behav 14(5):e3528. https:\/\/doi.org\/10.1002\/brb3.3528","journal-title":"Brain Behav"},{"issue":"3","key":"4077_CR23","doi-asserted-by":"publisher","first-page":"448","DOI":"10.3174\/ajnr.A6983","volume":"42","author":"CJ Park","year":"2021","unstructured":"Park CJ, Han K, Kim H et al (2021) MRI Features May Predict Molecular Features of Glioblastoma in Isocitrate Dehydrogenase Wild-Type Lower-Grade Gliomas. AJNR Am J Neuroradiol 42(3):448\u2013456. https:\/\/doi.org\/10.3174\/ajnr.A6983","journal-title":"AJNR Am J Neuroradiol"},{"issue":"1","key":"4077_CR24","doi-asserted-by":"publisher","first-page":"vdac060","DOI":"10.1093\/noajnl\/vdac060","volume":"4","author":"C Evan","year":"2022","unstructured":"Evan C, Rudie JD, Andreas M, Rauschecker et al (2022) Combining radiomics and deep convolutional neural network features from preoperative MRI for predicting clinically relevant genetic biomarkers in glioblastoma. Neurooncol Adv 4(1):vdac060. https:\/\/doi.org\/10.1093\/noajnl\/vdac060","journal-title":"Neurooncol Adv"},{"issue":"13","key":"4077_CR25","doi-asserted-by":"publisher","first-page":"4415","DOI":"10.1007\/s00259-021-05526-6","volume":"48","author":"K Zhicong, Li, Lena","year":"2021","unstructured":"Zhicong, Li, Lena K, Adrien H et al (2021) Prediction of TERTp-mutation status in IDH-wildtype high-grade gliomas using pre-treatment dynamic [(18)F]FET PET radiomics. Eur J Nucl Med Mol Imaging 48(13):4415\u20134425. https:\/\/doi.org\/10.1007\/s00259-021-05526-6","journal-title":"Eur J Nucl Med Mol Imaging"},{"issue":"0","key":"4077_CR26","doi-asserted-by":"publisher","first-page":"645316","DOI":"10.3389\/fonc.2021.645316","volume":"11","author":"U Marcus","year":"2021","unstructured":"Marcus U, Viktoria, Ruf K, von Rohr et al (2021) TERT-Promoter Mutational Status in Glioblastoma - Is There an Association With Amino Acid Uptake on Dynamic (18)F-FET PET? Front Oncol 11(0):645316. https:\/\/doi.org\/10.3389\/fonc.2021.645316","journal-title":"Front Oncol"},{"issue":"1","key":"4077_CR27","doi-asserted-by":"publisher","first-page":"e29","DOI":"10.1016\/S1470-2045(23)00525-9","volume":"25","author":"L Nathalie","year":"2024","unstructured":"Nathalie L, Albert, Norbert, Galldiks, Benjamin M, Ellingson et al (2024) PET-based response assessment criteria for diffuse gliomas (PET RANO 1.0): a report of the RANO group. Lancet Oncol 25(1):e29\u2013e41. https:\/\/doi.org\/10.1016\/S1470-2045(23)00525-9","journal-title":"Lancet Oncol"},{"issue":"35","key":"4077_CR28","doi-asserted-by":"publisher","first-page":"59492","DOI":"10.18632\/oncotarget.17752","volume":"8","author":"S Alexey","year":"2017","unstructured":"Alexey S (2017) Meyer, Andreas, Wienke,Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. Oncotarget 8(35):59492\u201359499. https:\/\/doi.org\/10.18632\/oncotarget.17752","journal-title":"Oncotarget"},{"issue":"3","key":"4077_CR29","doi-asserted-by":"publisher","first-page":"779","DOI":"10.1007\/s00259-023-06468-x","volume":"51","author":"X Li","year":"2024","unstructured":"Li X, Cheng Y, Han X et al (2024) Exploring the association of glioma tumor residuals from incongruent [18F] FET PET\/MR imaging with tumor proliferation using a multiparametric MRI radiomics nomogram. Eur J Nucl Med Mol Imaging 51(3):779\u2013796. https:\/\/doi.org\/10.1007\/s00259-023-06468-x","journal-title":"Eur J Nucl Med Mol Imaging"}],"container-title":["Neuroradiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-026-04077-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00234-026-04077-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00234-026-04077-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T07:41:28Z","timestamp":1788939688000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00234-026-04077-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":29,"alternative-id":["4077"],"URL":"https:\/\/doi.org\/10.1007\/s00234-026-04077-7","relation":{},"ISSN":["0028-3940","1432-1920"],"issn-type":[{"value":"0028-3940","type":"print"},{"value":"1432-1920","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"assertion":[{"value":"4 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was performed in accordance with the Declaration of Helsinki. This study was approved by the Institutional Review Boards of Xuanwu Hospital, Capital Medical University. Written informed consent was obtained from all patients prior to hybrid PET\/MR examinations.","order":2,"name":"Ethics","label":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}}]}}