# Radiomics Analysis of Non-Enhancing Lesions After Bevacizumab Administration in Recurrent Glioblastoma

## TL;DR

This study uses radiomic features to identify non-contrast-enhancing tumors in glioblastoma patients after Bevacizumab treatment.

## Contribution

The study introduces a predictive model using radiomic features from transformed tumor regions after Bevacizumab treatment.

## Key findings

- A radiomic feature from T2WI showed high accuracy in predicting non-contrast-enhancing tumors in the BEV cohort.
- The model had moderate accuracy when tested on newly diagnosed GBM patients using Met-PET data.
- Image reconstruction using radiomic features effectively visualized non-contrast-enhancing tumors in newly diagnosed GBM.

## Abstract

This study explored radiomic features that help identify non-contrast-enhancing tumors (nCET) by analyzing regions where contrast-enhancing tumors (CET) transformed into nCET after Bevacizumab (BEV) treatment. The BEV cohort included 24 recurrent GBM (rGBM) patients treated with BEV, showing reduced contrast-enhancement on gadolinium-enhanced T1-weighted imaging (T1Gd) imaging. The 11C-methionine positron emission tomography (Met-PET) cohort consisted of 24 newly diagnosed GBM (nGBM) patients with available Met-PET data. VOIs were created from T2WI, FLAIR, T1Gd, and Met-PET to analyze nCET and T2/FLAIR lesions. After significant radiomic features were identified, a prediction model for nCET was developed in the BEV cohort and subsequently evaluated in the Met-PET cohort. A total of 37 and 46 significant radiomic features were found in the BEV and Met-PET cohorts, respectively. The key feature, T2WI_whole_GLCMcorrelation_1, was selected for predictive modeling. The model demonstrated high accuracy (AUC = 0.93, p < 0.0001) in the BEV cohort, with sensitivity and specificity of 0.91, while the Met-PET cohort showed moderate accuracy (AUC = 0.74, p = 0.0053). Image reconstruction using these features also effectively visualized nCET in nGBM. These findings suggest that radiomic features in CET regions transforming to nCET after BEV treatment harbors valuable information for identifying nCET in GBM.