Assessment of glioma treatment is based on pathological evaluation via biopsies or radiological criteria using follow-up MRI, which is either invasive or time consuming. Amide protein transfer weighted (APTw) MRI has been validated to accurately detect recurrent malignant gliomas by multiple studies. The cutting edge methodology of radiomics provides quantitative measurements for imaging diagnosis. Here, we develop an automated framework that integrates APTw MRI radiomic features with a machine learning model to evaluate treatment response for gliomas. Our results suggest that the use of APTw features enabled the radiomic model to reach a more accurate assessment of the treatment effect.