Work

A feasibility study on predicting disease progression in high-grade gliomas using magnetic resonance imaging habitat radiomics based on response assessment in neuro-oncology (RANO) criteria

Year: 2026

Type: article

Abstract: Objective Investigating progression risk insights of high-grade gliomas through habitat radiomics analysis. Methods A cohort of 89 patients with high-grade gliomas was enrolled, with 63 patients in the train cohort and 26 patients in the test cohort. The methodology involved delineating the region of interest (ROI) within the tumor area on magnetic resonance imaging images, followed by multisequence registration. The ROI was further divided into subregions using K -means clustering. Radiomics features were extracted from the subregions, and feature selection was performed using least absolute shrinkage and selection operator regression. Four separate models were created: radiomics model, clinical model, habitat model, and combined model that merged the habitat signature with clinical factors. The accuracy of the models was evaluated using concordance index (C-index) and receiver operating characteristic analysis. Results The ROI was divided into three subregions, from which 36 features were extracted and selected. The habitat model, radiomics model, clinical model, and combined model were constructed by combining the extracted features with clinical data. The habitat model showed excellent predictive performance with the C-index values of 0.879 in the train cohort and 0.781 in the test cohort. Using this model, patients were classified into high-risk and low-risk groups, resulting in significantly different median progression-free survival (mPFS) times of 7 and 31 months, respectively ( P < 0.001). Stratifying the patient cohort based on isocitrate dehydrogenase status also revealed distinct mPFS outcomes for high-risk and low-risk patients. Conclusion The habitat model demonstrated outstanding predictive performance for forecasting the progression risk of patients with high-grade gliomas.

Source: Digital Health

DOI: 10.1177/20552076251412634