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PURPOSE T2-FLAIR mismatch (T2FM) is a highly specific imaging biomarker for isocitrate dehydrogenase (IDH) mutation in low-grade gliomas. Previous T2FM studies are inconsistent for glioblastoma (GBM)/grade-4 glioma, partly due to low IDH-mutation prevalence in high-grade gliomas. We leveraged a large multi-institutional GBM/grade-4 glioma cohort to analyze the association of partial T2FM and IDH-mutation (T2-hyperintense, FLAIR-hypointense, nonenhancing, nonedema). METHODS We analyzed preoperative MRI of 1500 pathologically confirmed GBM/grade-4 gliomas with known IDH-mutation status from the ReSPOND consortium, consisting of the following institutions (sample size): Ivy GBM Atlas Project (33), Catalan Institute of Oncology (132), Case Western Reserve University/University Hospitals (132), New York University (55), Ohio State University (25), University of Pennsylvania (641), University Hospital Río Hortega (16), Yonsei University Health System (118), The Cancer Imaging Archive (93), Thomas Jefferson University (48), Tata Memorial Hospital (22), University of Pittsburgh Medical Center (156), and Washington University School of Medicine in St. Louis (57). Sequences were co-registered to a common anatomic atlas. Continuous variables were compared by t-test and categorical variables by Χ 2-test. RESULTS 71 (4.7%) were IDH-mutants, significantly younger (43±1 v. 62±12 years, p=5x10-37), and more likely to exhibit partial T2FM (20% v. 0.4%, p=1x10-43), frontal lobe predominance (68% v. 29%, p=7x10-12), nonenhancing components (T2/FLAIR-intermediate signal, nonedema; 45% v. 9%, p=1x10-22), and cystic components (smooth margins, no/minimal enhancement, homogeneous FLAIR suppression; 17% v. 3%, p=7x10-11) than IDH-wildtypes. 20 cases had partial T2FM (14 IDH-mutant, 6 IDH-wildtype). Sensitivity of partial T2FM for IDH-mutation was 19.7%, specificity 99.6%, positive predictive value 70%, and negative predictive value 96.1%. Subset analysis of 983 IDH-wildtypes with known MGMT methylation status (406 MGMT-hypermethylated) showed frontal lobe predominance was more common in MGMT-hypermethylated than MGMT-unmethylated (39.4% v. 24.3%, p=.02); other imaging characteristics did not significantly differ. CONCLUSIONS Partial T2FM is a highly specific imaging biomarker for IDH-mutation in GBM/grade-4 glioma.

PURPOSE Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence. METHODS We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence. RESULTS Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84). CONCLUSION This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.

PURPOSE Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizable prognostic stratification. We seek an AI-based OS predictor using routine clinically acquired MRI sequences, quantitatively evaluated across institutions of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium. METHODS We identified a retrospective cohort of 2,293 diffuse glioma (IDH-wildtype/-NOS/-NEC) patients from 22 geographically distinct institutions across 3 continents, with preoperative structural MRI scans. The entire tumor burden was automatically segmented into 3 sub-compartments, i.e., enhancing, necrotic, peritumoral T2-FLAIR abnormality. We developed our AI predictor by multivariate integration of i)patient age, ii)tumor sub-compartment volume normalized to brain volume, iii)spatial distribution characteristics (tumor location, distance to the ventricles, and laterality), and iv)morphologic descriptors (major axes’ length, axes’ ratio, extent, and number of tumors). The AI predictor returns a continuous value between 0-1, defining short-, intermediate-, and long-survivors based on thresholds on the 25th and 75th percentiles. Leave-One-Site-Out-Cross-Validation was used to assess the generalizability of our stratification. Kaplan-Meier survival curves were computed for OS analysis and evaluated by a Cox proportional hazards model for statistical significance and hazard ratios. RESULTS Survival analysis yielded a hazard ratio of 2.07 (95%CI, 2.06-2.08, p-value= 4.8e-102) for patient stratification into short-, intermediate-, and long-survivors. Pearson correlation between the predicted and actual OS yielded an R= 0.49. CONCLUSION Multivariate integration of visually quantified tumor characteristics, agnostic to acquisition protocol/equipment, yields an accurate OS surrogate index. Validation of our AI model in the largest centralized glioblastoma imaging dataset, from the ReSPOND consortium, supports its generalizability across diverse patient populations and acquisition settings, potentially contributing to equitable improvements of personalized patient care.