The Potential Use of Radiomics with Pre-Radiation Therapy MR Imaging in Predicting Risk of Pseudoprogression in Glioblastoma Patients

Authors

Michael J. Baine, University of Nebraska Medical CenterFollow
Justin Burr, University of Nebraska Medical CenterFollow
Qian Du, University of Nebraska - LincolnFollow
Chi Zhang, University of Nebraska-LincolnFollow
Xiaoying Liang, Proton Institute, University of FloridaFollow
Luke Krajewski, University of Nebraska Medical CenterFollow
Laura Zima, University of Nebraska Medical CenterFollow
Gerard Rux, University of Nebraska Medical CenterFollow
Chi Zhang, University of Nebraska Medical CenterFollow
Dandan Zheng, University of Nebraska Medical CenterFollow

Document Type

Article

Date of this Version

2021

Citation

J. Imaging 2021, 7, 17. https://doi.org/10.3390/jimaging7020017

Comments

2021 by the authors.

Abstract

Glioblastoma (GBM) is the most common adult glioma. Differentiating post-treatment effects such as pseudoprogression from true progression is paramount for treatment. Radiomics has been shown to predict overall survival and MGMT (methylguanine-DNA methyltransferase) pro- moter status in those with GBM. A potential application of radiomics is predicting pseudoprogression on pre-radiotherapy (RT) scans for patients with GBM. A retrospective review was performed with radiomic data analyzed using pre-RT MRI scans. Pseudoprogression was defined as post-treatment findings on imaging that resolved with steroids or spontaneously on subsequent imaging. Of the 72 patients identified for the study, 35 were able to be assessed for pseudoprogression, and 8 (22.9%) had pseudoprogression. A total of 841 radiomic features were examined along with clinical features. Receiver operating characteristic (ROC) analyses were performed to determine the AUC (area under ROC curve) of models of clinical features, radiomic features, and combining clinical and radiomic features. Two radiomic features were identified to be the optimal model combination. The ROC analysis found that the predictive ability of this combination was higher than using clini- cal features alone (mean AUC: 0.82 vs. 0.62). Additionally, combining the radiomic features with clinical factors did not improve predictive ability. Our results indicate that radiomics is potentially capable of predicting future development of pseudoprogression in patients with GBM using pre- RT MRIs.