Biomechanical mapping of tumor growth: A novel method to quantify glioma infiltration and mass effect

Abstract Background Glioblastoma (GBM) growth can alter surrounding brain tissue through location‐dependent physiological changes. Two main growth phenotypes—(I) infiltrative, characterized by diffuse invasion with minimal mass effect, and (II) proliferative, characterized by pronounced tissue compression—are recognized, but their quantitative characterization and prognostic impact remain poorly explored. Purpose To develop and validate a novel MRI‐based biomarker, the Dynamic Infiltration Rate (DIR), that quantitatively assesses the balance between tumor volume expansion and peritumoral compression, and to evaluate its prognostic ability for stratifying patients based on overall survival (OS). Methods The DIR was defined as the ratio between tumor‐volume enlargement and mass‐effect–induced peritumoral compression. Technical validation was conducted using synthetic datasets with known ground truth spanning realistic infiltrative–proliferative spectra. Clinically, patients were dichotomized into high‐ and low‐infiltration groups using a threshold optimized by maximizing the log‐rank statistic for OS. Prognostic evaluation included multivariate Cox regression adjusted for age, sex, and MGMT methylation status. Results The synthetic dataset validation demonstrated high concordance with ground truth (). Clinical evaluation indicated significantly improved OS in the low DIR group (median = 35.2 weeks) compared to the high DIR group (median = 16.0 weeks; ). DIR effectively stratified patients based on survival (log‐rank , HR = 2.49) and remained an independent prognostic factor on multivariate analysis (HR = 1.45, 95% CI 1.07–1.85; ). Conclusions The DIR is a novel and robust quantitative MRI biomarker capable of distinguishing between proliferative and infiltrative GBM phenotypes, independently predicting OS. Early phenotype identification could facilitate personalized treatment strategies and individualized follow‐up scheduling.