Abstract #0081

Pre-Surgical Detection of Infiltrating Glioblastoma Using a Histopathology-Validated MR Fingerprinting Prediction Model

Walter Zhao¹, Tiffany R. Hodges², Sree Gongala³, Parisa Arjmand³, Xiaofeng Wang⁴, Shengwen Deng³, Charit Tippareddy³, Nisha Korakavi³, Eunate Alzaga Goni¹, Rhea Adams¹, Robin Ghotra³, Prashant Vempati⁵, Christos Davatzikos⁶, Marta Couce⁷, Jeffrey Sunshine³, Michael D. Staudt², Andrew E. Sloan⁸, Chaitra Badve³, and Dan Ma¹

¹Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, ²Neurosurgery, University Hospitals Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, United States, ³Radiology, University Hospitals Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, United States, ⁴Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, United States, ⁵Radiation Oncology, University Hospitals Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, United States, ⁶Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, ⁷Pathology, University Hospitals Cleveland Medical Center and Case Western Reserve University, Cleveland, OH, United States, ⁸Piedmont Physicians Neurosurgery Atlanta, Piedmont Healthcare, Atlanta, GA, United States

Synopsis

Keywords: Tumors (Pre-Treatment), Tumors

Motivation: Glioblastoma (GBM) infiltration models lack histopathological validation.

Goal(s): GBM infiltration models should be validated with histopathological data of the surrounding peritumoral zone (PZ), which is typically omitted from surgery and difficult to obtain retrospectively.

Approach: We prospectively obtained histopathology data from either targeted biopsies (n = 5) or extended peritumoral resection (n = 18). GBM cases with ground truth PZ sampling were used to evaluate the performance of a self-supervised MR fingerprinting (MRF) and multiparametric MRI classifier.

Results: Our histopathology-validated infiltration model achieved 70.9% sensitivity and 74.5% specificity in detecting infiltrating glioma, with a mean balanced accuracy of 72.7%.

Impact: Glioblastoma (GBM) peritumoral infiltration leads to inevitable recurrence and death. We develop and histopathologically-validate an MRF artificial intelligence (AI) model for pre-surgical prediction of infiltrating GBM to reduce tumor recurrence, improve patient quality of life, and extend survival.

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