Abstract
In the abdomen, real-time MRgSBRT has live motion tracking, high soft tissue contrast, and direct visualization of oncologic targets and organs at risk. Setup images are acquired daily, which generates longitudinal, non-invasive radiographic data points. Our study aimed to identify delta radiomic texture features (DRTF) extracted from daily set up images to predict local progression in liver patients treated with MRgSBRT.
This work included a prospective analysis of an IRB-approved database of 20 patients with liver lesions treated by MRgSBRT. The gross tumor volume was identified on daily low field strength (0.35T) setup images. MR images were pre-processed using histogram equalization to re-bin the gray level intensities to 64 levels and 39 second-order DRTFs extracted. DRTFs were calculated as the difference between values on the initial set up scan and after intervals of BED 20 Gy (α/β = 10) were delivered. The Gini Index was used to rank features in order of predictive importance during training of a random forest model while using the delta radiomics texture features to predict local control (LC) at 1 year. Leave-one-out (LOO) analysis was performed using the top two features.
Lesions were treated to a median dose of 50 Gy in 5 fractions, or BED 20 Gy per fraction. The top two features identified after BED 20 Gy (1 fraction) was delivered were gray level co-occurrence matrix features (GLCM) and gray level size zone matrix (GLSZM) based Large Zone Emphasis. The model generated an ROC curve with AUC = 0.9011 [0.752 – 1] using LOO analysis. The same two features were selected as predictive after delivery of BED 40 Gy and LOO analysis resulted in a ROC curve with AUC = 0.6868 [0.3957 – 0.9779].
DRTFs extracted from daily setup MR images after a single fraction of MRgSBRT were predictive of LC in this cohort. Additional studies are needed to evaluate these features as they may identify patients with radioresistant disease and provide an opportunity for physicians to dose escalate.