A unified framework of tumor segmentation and response assessment in patients with malignant gliomas using structural and APT-weighted MRI

Motivation: Amide protein transfer-weighted (APTw) MRI has been validated to accurately detect recurrent malignant gliomas across different studies. However, APTw image interpretation is time consuming and requires professional knowledge. Goal(s): Our goal was to develop a reliable, automated imaging diagnostic tool to assess malignant glioma response to therapies are urgently needed. Approach: We developed and verified a unified CNN-based deep-learning framework for both tumor segmentation and tumor progression assessment by adding APTw MRI data to structural MR images as model input. Results: The use of APTw images can improve not only diagnostic accuracy but also segmentation performance to structural MRIs. Impact: The proposed deep-learning method could be a highly efficient solution that could help clinical experts to make precise diagnoses for patients with post-treatment gliomas.