NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM
Authors: Akbari Hamed , Bakas Spyridon , Sako Chiharu , Kazerooni Anahita Fathi , Villanueva-Meyer Javier , Garcia Jose , Bagley Stephen , Baid Ujjwal , Bilello Michel , Brem Steven , Lustig Robert , Mohan Suyash , Nasrallah MacLean , O'Rourke Donald , Calabrese Evan , Rudie Jeffrey , LaMontagne Pamela , Marcus Daniel , Balana Carmen , Capellades Jaume , Puig Josep , Barnholtz-Sloan Jill , Badve Chaitra , Sloan Andrew , Ak Murat , Colen Rivka , Ahn Sung Soo , Chang Jong Hee , Choi Yoon Seong , Lee Seung-Koo , Dicker Adam , Flanders Adam , Shi Wenyin , Shukla Gaurav , Griffith Brent , Poisson Laila , Rogers Lisa , Booth Thomas , Jain Rajan , Lee Matthew , Mahajan Abhishek , Chakravarti Arnab , Palmer Joshua , Taylor William , Cepeda Santiago , Wiestler Benedikt , Davatzikos Christos 
Published: November 2022

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.