NIMG-39. RADIOMIC ANALYSIS FOR NON-INVASIVE IN VIVO PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS: A MULTI-INSTITUTIONAL EVALUATION FOR GENERALIZABILITY IN THE RESPOND CONSORTIUM
Authors: Akbari* Hamed , Bakas* Spyridon , Sako* Chiharu , Kazerooni Anahita Fathi , Garcia Jose A , Bagley Stephen J , Mohan Suyash , Ahn Sung Soo , Ak Murat , Alexander Gregory S , Ali Ayesha S , Baid Ujjwal , Bavde Chaitra , Bilello Michel , Brem Steven , Capellades Jaume , Chang Jong Hee , Choi Yoon Seong , Dicker Adam P , Fathallah-Shaykh Hassan , Flanders Adam E , Griffith Brent D , LaMontagne Pamela , Lee Matthew , Lee Seung-Koo , Liem Spencer , Lombardo Joseph , Lustig Robert A , Mahajan Abhishek , Milchenko Mikhail , Nasrallah MacLean , Nazeri Arash , Puig Josep , Shukla Gaurav , Sloan Andrew , Taylor William , Vadmal Vachan , Waite Kristin , Balana Carmen , Booth Thomas C , Cepeda Santiago , Poisson Laila , Colen Rivka R , Marcus Daniel S , Palmer Joshua , Jain Rajan , Shi Wenyin , O’Rourke Donald M , Barnholtz-Sloan Jill , Davatzikos Christos 
Published: November 2021

Abstract

PURPOSE
Multi-parametric MRI based radiomic signatures have highlighted the promise of artificial intelligence (AI) in neuro-oncology. However, inter-institution heterogeneity hinders generalization to data from unseen clinical institutions. To this end, we formulated the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium for glioblastoma. Here, we seek non-invasive generalizable radiomic signatures from routine clinically-acquired MRI for prognostic stratification of glioblastoma patients.

METHODS
We identified a retrospective cohort of 606 patients with near/gross total tumor resection ( &gt;90%), from 13 geographically-diverse institutions. All pre-operative structural MRI scans (T1,T1-Gd,T2,T2-FLAIR) were aligned to a common anatomical atlas. An automatic algorithm segmented the whole tumors (WTs) into 3 sub-compartments, i.e., enhancing (ET), necrotic core (NC), and peritumoral T2-FLAIR signal abnormality (ED). The combination of ET+NC defines the tumor core (TC). Quantitative radiomic features were extracted to generate our AI model to stratify patients into short- (&lt; 14mts) and long-survivors ( &gt;14mts). The model trained on 276 patients from a single institution was independently validated on 330 unseen patients from 12 left-out institutions, using the area-under-the-receiver-operating-characteristic-curve (AUC).

RESULTS
Each feature individually offered certain (limited but reproducible) value for identifying short-survivors: 1) TC closer to lateral ventricles (AUC=0.62); 2) larger ET/brain (AUC=0.61); 3) larger TC/brain (AUC=0.59); 4) larger WT/brain (AUC=0.55); 5) larger ET/WT (AUC=0.59); 6) smaller ED/WT (AUC=0.57); 7) larger ventricle deformations (AUC=0.6). Integrating all features and age, through a multivariate AI model, resulted in higher accuracy (AUC=0.7; 95% C.I.,0.64-0.77).

CONCLUSION
Prognostic stratification using basic radiomic features is highly reproducible across diverse institutions and patient populations. Multivariate integration yields relatively more accurate and generalizable radiomic signatures, across institutions. Our results offer promise for generalizable non-invasive in vivo signatures of survival prediction in patients with glioblastoma. Extracted features from clinically-acquired imaging, renders these signatures easier for clinical translation. Large-scale evaluation could contribute to improving patient management and treatment planning.
*Indicates equal authorship.