{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T08:34:21Z","timestamp":1790411661312,"version":"4.1.0"},"reference-count":65,"publisher":"The Royal Society","issue":"103","license":[{"start":{"date-parts":[[2015,2,1]],"date-time":"2015-02-01T00:00:00Z","timestamp":1422748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/royalsociety.org\/journals\/ethics-policies\/data-sharing-mining\/"}],"content-domain":{"domain":["royalsocietypublishing.org"],"crossmark-restriction":true},"short-container-title":["J. R. Soc. Interface."],"published-print":{"date-parts":[[2015,2]]},"abstract":"<jats:p>\n            Glioblastoma multiforme (GBM) is a highly invasive primary brain tumour that has poor prognosis despite aggressive treatment. A hallmark of these tumours is diffuse invasion into the surrounding brain, necessitating a multi-modal treatment approach, including surgery, radiation and chemotherapy. We have previously demonstrated the ability of our model to predict radiographic response immediately following radiation therapy in individual GBM patients using a simplified geometry of the brain and theoretical radiation dose. Using only two pre-treatment magnetic resonance imaging scans, we calculate net rates of proliferation and invasion as well as radiation sensitivity for a patient's disease. Here, we present the application of our clinically targeted modelling approach to a single glioblastoma patient as a demonstration of our method. We apply our model in the full three-dimensional architecture of the brain to quantify the effects of regional resistance to radiation owing to hypoxia\n            <jats:italic>in vivo<\/jats:italic>\n            determined by [\n            <jats:sup>18<\/jats:sup>\n            F]-fluoromisonidazole positron emission tomography (FMISO-PET) and the patient-specific three-dimensional radiation treatment plan. Incorporation of hypoxia into our model with FMISO-PET increases the model\u2013data agreement by an order of magnitude. This improvement was robust to our definition of hypoxia or the degree of radiation resistance quantified with the FMISO-PET image and our computational model, respectively. This work demonstrates a useful application of patient-specific modelling in personalized medicine and how mathematical modelling has the potential to unify multi-modality imaging and radiation treatment planning.\n          <\/jats:p>","DOI":"10.1098\/rsif.2014.1174","type":"journal-article","created":{"date-parts":[[2014,12,24]],"date-time":"2014-12-24T14:19:45Z","timestamp":1419430785000},"page":"20141174","update-policy":"https:\/\/doi.org\/10.1098\/crossmark-policy","source":"Crossref","is-referenced-by-count":82,"title":["A patient-specific computational model of hypoxia-modulated radiation resistance in glioblastoma using\n            <sup>18<\/sup>\n            F-FMISO-PET"],"prefix":"10.1098","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1557-159X","authenticated-orcid":false,"given":"Russell C.","family":"Rockne","sequence":"first","affiliation":[{"name":"Department of Neurological Surgery, Northwestern University and Feinberg School of Medicine, 676 N 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