Conventional and Cross-Channel MR Radiomic Features do Not Predict Histone H3 Status in DIPG: Genomic and Clinical Evaluation of a Multi-Institutional Cohort

Abstract

<h3>Purpose/Objective(s)</h3> Genomic profiling of diffuse pontine glioma (DIPG) has suggested distinct and clinically relevant molecular subgroups based on the presence and type of histone H3K27M mutation. Recent studies have demonstrated improved safety for biopsy, but sampling carries risks. We evaluated the impact of radiomic features on the molecular classification of 154 histologically confirmed and centrally reviewed DIPG. <h3>Materials/Methods</h3> Automatically segmented and manually reviewed tumor volumes were used to compute single & cross channel radiomic features from T1, T2, FLAIR, and T1+gadolinium MR sequences obtained from histone H3 WT (N=30) and H3K27M-mutant (N=124) DIPG. Radiomic feature stability was assessed when multiple pretreatment MRs were available using the concordance correlation coefficient. Random Forest, Logistic Regression, XGBoost, and Deep Learning classifiers were used to build predictive models of H3K27M status in a training dataset, after exclusion of correlated features. Model accuracy was assessed in the remaining validation dataset. <h3>Results</h3> Two hundred and twelve of 456 imaging features (46.4%) demonstrated stability (CCC, p<0.05) between pretreatment scans. Accuracy varied by method, but none surpassed the No Information Rate (82%). Model accuracy was not improved by efforts to remedy class imbalance (resampling and class weight adjustment). Several cross channel radiomic features were consistently selected by the models, indicating some predictive utility, although cross institutional scanning techniques were confounding. <h3>Conclusion</h3> While early efforts with single institutional data were encouraging, differences in MR technique and class imbalance seemed to limit the identification of stable radiomic features predictive of H3 status in this multi-institutional cohort. Biopsy remains key for determining trial eligibility and directing precision medicine.