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) promoter 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 clinical 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.
Highlights
Imaging is a primary diagnostic tool upon which medical decisions are made, and magnetic resonance imaging (MRI) is commonly used to monitor post-treatment effects for central nervous system (CNS) tumors
A total of 72 patients were identified for this study, of whom 35 patients could be assessed for pseudoprogression and were included in the analysis
The radiomic model resulted in a mean AUC of 0.82, which was higher than the clinical model (0.62)
Summary
Imaging is a primary diagnostic tool upon which medical decisions are made, and magnetic resonance imaging (MRI) is commonly used to monitor post-treatment effects for central nervous system (CNS) tumors. There is more information within images than is initially seen, . The process of radiomics involves image acquisition, segmentation, and labeling of the tumor/normal tissues, extraction of quantitative features (shape, intensity, texture), followed by statistical modeling and machine learning [2]. Cellular and molecular changes impacting imaging characteristics may allow for the gathering of complex tumor information through less invasive methods. These features are readily available as patients have many images taken throughout the course of their treatment
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