{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T08:20:32Z","timestamp":1792052432894,"version":"4.4.0"},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100004912","name":"Sichuan University","doi-asserted-by":"publisher","award":["GYYX24004"],"award-info":[{"award-number":["GYYX24004"]}],"id":[{"id":"10.13039\/501100004912","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["ZYGX2022YGRH008"],"award-info":[{"award-number":["ZYGX2022YGRH008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013365","name":"West China Hospital, Sichuan University","doi-asserted-by":"publisher","award":["ZYAI24010"],"award-info":[{"award-number":["ZYAI24010"]}],"id":[{"id":"10.13039\/501100013365","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016079","name":"Science and Technology Department of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2025E01032"],"award-info":[{"award-number":["2025E01032"]}],"id":[{"id":"10.13039\/100016079","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZYGD23003"],"award-info":[{"award-number":["ZYGD23003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82441007"],"award-info":[{"award-number":["82441007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["ZYYC21004"],"award-info":[{"award-number":["ZYYC21004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thegreenjournal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Radiotherapy and Oncology"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.radonc.2025.111180","type":"journal-article","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:34:26Z","timestamp":1759336466000},"page":"111180","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":6,"special_numbering":"C","title":["An intelligent postoperative management system for glioblastoma integrating automated segmentation, risk stratification, and recurrence spatial mapping"],"prefix":"10.1016","volume":"213","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7345-8640","authenticated-orcid":false,"given":"Yan","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3178-7761","authenticated-orcid":false,"given":"Zekun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhihao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2916-0911","authenticated-orcid":false,"given":"Yuehao","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deng","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3722-3334","authenticated-orcid":false,"given":"Yanhui","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Lui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7733-2498","authenticated-orcid":false,"given":"Min","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.radonc.2025.111180_b0005","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.7150\/ijbs.57168","article-title":"Identification of low-dose radiation-induced exosomal circ-METRN and miR-4709-3p\/GRB14\/PDGFR\u03b1 pathway as a key regulatory mechanism in glioblastoma progression and radioresistance: Functional validation and clinical theranostic significance","volume":"17","author":"Wang","year":"2021","journal-title":"Int J Biol Sci"},{"key":"10.1016\/j.radonc.2025.111180_b0010","article-title":"Preoperative magnetic resonance imaging radiomics for predicting early recurrence of glioblastoma","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0015","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.7150\/thno.13842","article-title":"High precision imaging of microscopic spread of glioblastoma with a targeted ultrasensitive SERRS molecular imaging probe","volume":"6","author":"Huang","year":"2016","journal-title":"Theranostics"},{"key":"10.1016\/j.radonc.2025.111180_b0020","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1007\/s00401-007-0293-7","article-title":"Diffuse glioma growth: a guerilla war","volume":"114","author":"Claes","year":"2007","journal-title":"Acta Neuropathol"},{"key":"10.1016\/j.radonc.2025.111180_b0025","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1038\/nrn3765","article-title":"A neurocentric perspective on glioma invasion","volume":"15","author":"Cuddapah","year":"2014","journal-title":"Nat Rev Neurosci"},{"key":"10.1016\/j.radonc.2025.111180_b0030","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/BF00598951","article-title":"Magnetic resonance imaging and histopathology of cerebral gliomas","volume":"34","author":"Watanabe","year":"1992","journal-title":"Neuroradiology"},{"key":"10.1016\/j.radonc.2025.111180_b0035","doi-asserted-by":"crossref","first-page":"592","DOI":"10.3390\/diagnostics11040592","article-title":"Visualizing glioma infiltration by the combination of multimodality imaging and artificial intelligence, a systematic review of the literature","volume":"11","author":"d'Este","year":"2021","journal-title":"Diagnostics (Basel)"},{"key":"10.1016\/j.radonc.2025.111180_b0040","first-page":"418","article-title":"Accurate patient-specific machine learning models of glioblastoma invasion using transfer learning","volume":"40","author":"Hu","year":"2019","journal-title":"AJNR Am J Neuroradiol"},{"key":"10.1016\/j.radonc.2025.111180_b0045","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1093\/neuonc\/noz180","article-title":"Improved detection of diffuse glioma infiltration with imaging combinations: a diagnostic accuracy study","volume":"22","author":"Verburg","year":"2020","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0050","doi-asserted-by":"crossref","first-page":"57","DOI":"10.2214\/AJR.16.16234","article-title":"Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images","volume":"208","author":"Chang","year":"2017","journal-title":"AJR Am J Roentgenol"},{"key":"10.1016\/j.radonc.2025.111180_b0055","doi-asserted-by":"crossref","first-page":"908","DOI":"10.3174\/ajnr.A5127","article-title":"Classification of high-grade glioma into tumor and nontumor components using support vector machine","volume":"38","author":"Blumenthal","year":"2017","journal-title":"AJNR Am J Neuroradiol"},{"key":"10.1016\/j.radonc.2025.111180_b0060","article-title":"Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning","volume":"5","author":"Rathore","year":"2018","journal-title":"J Med Imaging (Bellingham)"},{"key":"10.1016\/j.radonc.2025.111180_b0065","doi-asserted-by":"crossref","first-page":"1875","DOI":"10.1109\/TMI.2019.2902044","article-title":"Personalized radiotherapy design for glioblastoma: integrating mathematical tumor models, multimodal scans, and bayesian inference","volume":"38","author":"Lipkova","year":"2019","journal-title":"IEEE Trans Med Imaging"},{"key":"10.1016\/j.radonc.2025.111180_b0070","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1007\/s00259-018-4180-3","article-title":"Feasibility of multi-parametric PET and MRI for prediction of tumour recurrence in patients with glioblastoma","volume":"46","author":"Lundemann","year":"2019","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"10.1016\/j.radonc.2025.111180_b0075","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1227\/NEU.0000000000001202","article-title":"Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma","volume":"78","author":"Akbari","year":"2016","journal-title":"Neurosurgery"},{"key":"10.1016\/j.radonc.2025.111180_b0080","doi-asserted-by":"crossref","first-page":"3492","DOI":"10.1158\/1078-0432.CCR-18-0385","article-title":"Data analysis strategies in medical imaging","volume":"24","author":"Parmar","year":"2018","journal-title":"Clin Cancer Res"},{"key":"10.1016\/j.radonc.2025.111180_b0085","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.radonc.2011.12.006","article-title":"Multivariate modeling of complications with data driven variable selection: guarding against overfitting and effects of data set size","volume":"105","author":"van der Schaaf","year":"2012","journal-title":"Radiother Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0090","doi-asserted-by":"crossref","first-page":"6288","DOI":"10.1158\/1078-0432.CCR-17-3420","article-title":"A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models","volume":"24","author":"Zinn","year":"2018","journal-title":"Clin Cancer Res"},{"key":"10.1016\/j.radonc.2025.111180_b0095","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1093\/neuonc\/noac166","article-title":"Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning","volume":"25","author":"van der Voort","year":"2023","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0100","doi-asserted-by":"crossref","first-page":"1739","DOI":"10.1007\/s00259-024-07024-x","article-title":"Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer\/T-cell lymphoma","volume":"52","author":"Jiang","year":"2025","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"10.1016\/j.radonc.2025.111180_b0105","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1002\/med4.62","article-title":"Applications of artificial intelligence and machine learning in clinical medicine: what lies ahead?","volume":"2","author":"Marshall Raj","year":"2024","journal-title":"Med Adv"},{"key":"10.1016\/j.radonc.2025.111180_b0110","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1126\/science.aaf2666","article-title":"An anatomic transcriptional atlas of human glioblastoma","volume":"360","author":"Puchalski","year":"2018","journal-title":"Science"},{"key":"10.1016\/j.radonc.2025.111180_b0115","article-title":"Data from Ivy Glioblastoma Atlas Project (IvyGAP)","author":"Shah","year":"2016","journal-title":"Cancer Imaging Archive"},{"key":"10.1016\/j.radonc.2025.111180_b0120","article-title":"Burdenko\u2019s glioblastoma progression dataset (burdenko-gbm-progression) (version 1)","author":"Zolotova","year":"2023","journal-title":"Cancer Imag Archive"},{"key":"10.1016\/j.radonc.2025.111180_b0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109617","article-title":"The R\u00edo Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)","volume":"50","author":"Cepeda","year":"2023","journal-title":"Data Brief"},{"key":"10.1016\/j.radonc.2025.111180_b0130","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s13311-016-0507-6","article-title":"Modified criteria for radiographic response assessment in glioblastoma clinical trials","volume":"14","author":"Ellingson","year":"2017","journal-title":"Neurotherapeutics"},{"key":"10.1016\/j.radonc.2025.111180_b0135","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat Methods"},{"key":"10.1016\/j.radonc.2025.111180_b0140","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res"},{"key":"10.1016\/j.radonc.2025.111180_b0145","doi-asserted-by":"crossref","first-page":"2266","DOI":"10.1007\/s00330-021-08368-w","article-title":"Multimodality MRI-based radiomics approach to predict the posttreatment response of lung cancer brain metastases to gamma knife radiosurgery","volume":"32","author":"Jiang","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.radonc.2025.111180_b0150","doi-asserted-by":"crossref","DOI":"10.1155\/2016\/9093721","article-title":"Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts","volume":"2016","author":"Wu","year":"2016","journal-title":"Comput Math Methods Med"},{"key":"10.1016\/j.radonc.2025.111180_b0155","doi-asserted-by":"crossref","first-page":"1894","DOI":"10.3390\/cancers15061894","article-title":"Predicting regions of local recurrence in glioblastomas using voxel-based radiomic features of multiparametric postoperative MRI","volume":"15","author":"Cepeda","year":"2023","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.radonc.2025.111180_b0160","doi-asserted-by":"crossref","first-page":"9748","DOI":"10.1038\/s41598-020-66691-6","article-title":"A neural network approach to identify the peritumoral invasive areas in glioblastoma patients by using MR radiomics","volume":"10","author":"Yan","year":"2020","journal-title":"Sci Rep"},{"key":"10.1016\/j.radonc.2025.111180_b0165","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0141506","article-title":"Multi-parametric MRI and texture analysis to visualize spatial histologic heterogeneity and tumor extent in glioblastoma","volume":"10","author":"Hu","year":"2015","journal-title":"PLoS One"},{"key":"10.1016\/j.radonc.2025.111180_b0170","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1667\/RR14662.1","article-title":"Identifying voxels at risk for progression in glioblastoma based on dosimetry, physiologic and metabolic MRI","volume":"188","author":"Anwar","year":"2017","journal-title":"Radiat Res"},{"key":"10.1016\/j.radonc.2025.111180_b0175","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1016\/j.ijrobp.2021.12.153","article-title":"Voxelwise prediction of recurrent high-grade glioma via proximity estimation-coupled multidimensional support vector machine","volume":"112","author":"Lao","year":"2022","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2025.111180_b0180","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1148\/radiol.2018180200","article-title":"Radiomic MRI phenotyping of glioblastoma: improving survival prediction","volume":"289","author":"Bae","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.radonc.2025.111180_b0185","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1093\/neuonc\/nox188","article-title":"Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma","volume":"20","author":"Kickingereder","year":"2018","journal-title":"Neuro Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0190","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1148\/radiol.2021203281","article-title":"Biologic pathways underlying prognostic radiomics phenotypes from paired MRI and RNA sequencing in glioblastoma","volume":"301","author":"Sun","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.radonc.2025.111180_b0195","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.659969","article-title":"Comparison of different machine models based on contrast-enhanced computed tomography radiomic features to differentiate high from low grade clear cell renal cell carcinomas","volume":"11","author":"Pei","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.radonc.2025.111180_b0200","doi-asserted-by":"crossref","first-page":"4188","DOI":"10.1007\/s00330-016-4637-3","article-title":"Radiomic features from the peritumoral brain parenchyma on treatment-na\u00efve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings","volume":"27","author":"Prasanna","year":"2017","journal-title":"Eur Radiol"},{"key":"10.1016\/j.radonc.2025.111180_b0205","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1002\/med4.75","article-title":"Automated machine learning with interpretation: a systematic review of methodologies and applications in healthcare","volume":"2","author":"Yuan","year":"2024","journal-title":"Med Adv"},{"key":"10.1016\/j.radonc.2025.111180_b0210","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.ijrobp.2021.05.001","article-title":"Dose escalated radiation therapy for glioblastoma multiforme: an international systematic review and meta-analysis of 22 prospective trials","volume":"111","author":"Singh","year":"2021","journal-title":"Int J Radiat Oncol Biol Phys"},{"key":"10.1016\/j.radonc.2025.111180_b0215","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1109\/TMI.2016.2626443","article-title":"Personalized radiotherapy planning based on a computational tumor growth model","volume":"36","author":"Le","year":"2017","journal-title":"IEEE Trans Med Imaging"}],"container-title":["Radiotherapy and Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814025051849?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167814025051849?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T04:12:29Z","timestamp":1780546349000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167814025051849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":43,"alternative-id":["S0167814025051849"],"URL":"https:\/\/doi.org\/10.1016\/j.radonc.2025.111180","relation":{},"ISSN":["0167-8140"],"issn-type":[{"value":"0167-8140","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An intelligent postoperative management system for glioblastoma integrating automated segmentation, risk stratification, and recurrence spatial mapping","name":"articletitle","label":"Article Title"},{"value":"Radiotherapy and Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.radonc.2025.111180","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111180"}}