Study flowchart depicting the procedure.

<div><p>Objective</p><p>Accurate prediction of glioblastoma (GBM) progression is essential for improving therapeutic interventions and outcomes. This study aimed to develop and validate an integrated clinical-radiomics model to predict overall survival (OS) and evaluate the r...

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Bibliographic Details
Main Author:	Han Wang (254423) (author)
Published:	2025
Subjects:	Medicine Genetics Neuroscience Biotechnology Cancer Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified significant prognostic factors least absolute shrinkage improving therapeutic interventions gross total resection predict overall survival meier survival analysis clinical &# 8210 kaplan &# 8210 cox proportional hazards multiregional radiomics model preclinical gbm patients predicting risk stratification wildtype gbm patients glioblastoma </ p wildtype gbm radiomics model survival prediction p </ compare survival gbm patients radiomics models radiomic model integrated clinical clinical models xlink "> tumor microenvironment thus could three sub study aimed standalone radiomic selection operator risk score risk groups risk group retrospectively analyzed regions individually rank test radiomic signature radiomic features isocitrate dehydrogenase important tool 6 months 3 years 3 months 1 year 05 ).
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Study flowchart depicting the procedure.

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