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: Hamed Akbari, Suyash Mohan, Jose A Garcia, Anahita Fathi Kazerooni, Chiharu Sako, Spyridon Bakas, Gaurav Shukla, Stephen J Bagley, Sung Soo Ahn, Murat Ak, Gregory S Alexander, Ayesha S Ali, Ujjwal Baid, Chaitra Bavde, Steven Brem, Jaume Capellades, Jong Hee Chang, Yoon Seong Choi, Adam P Dicker, Hassan Fathallah-Shaykh, Adam E Flanders, Brent D Griffith, Pamela LaMontagne, Matthew Lee, Seung-Koo Lee, Spencer Liem, Joseph Lombardo, Abhishek Mahajan, Mikhail Milchenko, Arash Nazeri, Josep Puig, Andrew Sloan, William Taylor, Vachan Vadmal, Kristin Waite, MacLean Nasrallah, Michel Bilello, Robert A Lustig, Carmen Balana, Thomas C Booth, Santiago Cepeda, Laila Poisson, Rivka R Colen, Daniel S Marcus, Joshua Palmer, Rajan Jain, Wenyin Shi, Donald M O’Rourke, Jill Barnholtz-Sloan, Christos Davatzikos
Journal: Neuro-Oncology (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.