Journal Article

Multimodal MRI radiomics based on habitat subregions of the tumor microenvironment for predicting risk stratification in glioblastoma

Wang, Han

2025

Overview

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 risk of disease progression in patients with isocitrate dehydrogenase-wildtype GBM (IDH-wildtype GBM). The data of 423 IDH-wildtype GBM patients were retrospectively analyzed. Radiomic features were extracted from preoperatively acquired MR images. Least absolute shrinkage and selection operator-Cox proportional hazards (LASSO-Cox) regression was used to identify radiomic features significantly associated with OS and calculate a risk score and construct a radiomic signature for each patient. Kaplan‒Meier survival analysis and the log-rank test were used to compare survival between the high-risk and low-risk groups. A clinical‒radiomic model and a nomogram were developed on the basis of the results of multivariable Cox proportional hazards regression and were evaluated with the concordance index (C-index). Radiomics models were developed on the basis of feature extracted from the three sub-regions individually, and a multiregional radiomics model was established by aggregating 16 features selected from these subregions. Kaplan-Meier survival analysis indicated that the high-risk group exhibited significantly worse outcomes than the low-risk group did (p < 0.05). The C-index of the multiregional radiomics model was the highest. Univariable Cox regression analysis revealed that the risk score, age, and extent of gross total resection (GTR) were significant prognostic factors for OS in GBM patients. According to the C-index, the combined clinical‒radiomic model outperformed the standalone radiomic and clinical models. The multifactor nomogram showed high accuracy in predicting the OS rates of preclinical GBM patients at 3 months, 6 months, 1 year, and 3 years in both the training and test cohorts. The integrated model combining clinicopathological data with a radiomic signature achieves good risk stratification and survival prediction in GBM and thus could be an important tool in clinical practice.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Adult / Aged / Algorithms / Automation / Brain Neoplasms - diagnostic imaging / Brain Neoplasms - mortality / Brain Neoplasms - pathology / Data mining / Datasets / Deep learning / Development and progression / Disease Progression / Feature extraction / Female / Glioblastoma / Glioblastoma - diagnostic imaging / Glioblastoma - genetics / Glioblastoma - mortality / Glioblastoma - pathology / Glioblastoma multiforme / Glioma / Hazard identification / Health risks / Humans / Image acquisition / Isocitrate dehydrogenase / Kaplan-Meier Estimate / Machine learning / Magnetic resonance imaging / Magnetic Resonance Imaging - methods / Male / Medical imaging equipment / Medical prognosis / Methods / Middle Aged / Multimodal Imaging - methods / Nomograms / Patients / Predictions / Prognosis / Proportional Hazards Models / Radiomics / Rank tests / Regression analysis / Retrospective Studies / Risk Assessment / Risk groups / Software / Statistical analysis / Survival / Survival analysis / Therapeutic applications / Tumor Microenvironment / Tumors