NIMG-22. PREDICTION OF GLIOBLASTOMA CELLULAR INFILTRATION AND RECURRENCE USING MACHINE LEARNING AND MULTI-PARAMETRIC MRI ANALYSIS: RESULTS FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM
Authors: Akbari Hamed , Mohan Suyash , Garcia Jose A , Kazerooni Anahita Fathi , Sako Chiharu , Bakas Spyridon , Shukla Gaurav , Bagley Stephen J , Ahn Sung Soo , Ak Murat , Alexander Gregory S , Ali Ayesha S , Baid Ujjwal , Bavde Chaitra , 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 , Mahajan Abhishek , Milchenko Mikhail , Nazeri Arash , Puig Josep , Sloan Andrew , Taylor William , Vadmal Vachan , Waite Kristin , Nasrallah MacLean , Bilello Michel , Lustig Robert A , 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 and artificial intelligence (AI) methods were previously used to predict peritumoral neoplastic cell infiltration and risk of future recurrence in glioblastoma, in single-institution studies. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured, engineered/selected, and quantified by these methods relate to predictions generalizable in the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium.

METHODS
To support further development, generalization, and clinical translation of our proposed method, we trained the AI model on a retrospective cohort of 29 de novo glioblastoma patients from the Hospital of the University of Pennsylvania (UPenn) (Male/Female:20/9, age:22-78 years) followed by evaluation on a prospective multi-institutional cohort of 84 glioblastoma patients (Male/Female:51/33, age:34-89 years) from Case Western Reserve University/University Hospitals (CWRU/UH, 25), New York University (NYU, 13), Ohio State University (OSU, 13), University Hospital Río Hortega (RH, 2), and UPenn (31). Features extracted from pre-resection MRI (T1, T1-Gd, T2, T2-FLAIR, ADC) were used to build our 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 sensitivity 83%, AUC 0.83 (99% CI, 0.73-0.93), and odds ratio 7.23 (99% CI, 7.09-7.37) in the prospective cohort. Odds ratio (99% CI)/AUC(99% CI) per institute were: CWRU/UH, 7.8(7.6-8.1)/0.82(0.75-0.89); NYU, 3.5(3.3-3.6)/0.84(074-0.93); OSU, 7.9(7.6-8.3)/0.8(0.67-0.94); RH, 22.7(20-25.1)/0.94(0.27-1); UPenn, 7.1(6.8-7.3)/0.83(0.76-0.91).

CONCLUSION
This is the first study that provides relatively extensive multi-institutional validated evidence that AI can provide good predictions of peritumoral neoplastic cell infiltration and future recurrence, by dissecting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and evaluate AI-based biomarkers for individualized prediction and prognostication, by moving from single-institution studies to generalizable, well-validated multi-institutional predictive biomarkers.