Radiomic Analysis to Predict Histopathologically Confirmed Pseudoprogression in Glioblastoma Patients
Anna Sophia McKenney, Emily S. Weg, Tejus Bale, Aaron T. Wild, Hyemin Um, Michael J. Fox, Andrew Lin, Jonathan T. Yang, Peter Yao, Maxwell Birger, Florent Tixier, Matthew Sellitti, Nelson S. Moss, Robert J. Young, Harini Veeraraghavan
Abstract
Purpose: Pseudoprogression mimicking recurrent glioblastoma remains a diagnostic challenge that may adversely confound or delay appropriate treatment or clinical trial enrollment. We sought to build a radiomic classifier to predict pseudoprogression in patients with primary isocitrate dehydrogenase wild type glioblastoma. Methods and Materials: We retrospectively examined a training cohort of 74 patients with isocitrate dehydrogenase wild type glioblastomas with brain magnetic resonance imaging including dynamic contrast enhanced T1 perfusion before resection of an enhancing lesion indeterminate for recurrent tumor or pseudoprogression. A recursive feature elimination random forest classifier was built using nested cross-validation without and with O 6 -methylguanine-DNA methyltransferase status to predict pseudoprogression. Results: A classifier constructed with cross-validation on the training cohort achieved an area under the receiver operating curve of 81% for predicting pseudoprogression. This was further improved to 89% with the addition of O 6 -methylguanine-DNA methyltransferase status into the classifier.