Deep learning reconstructed fast non-Gaussian DWI for predicting microsatellite instability in esophagogastric junction adenocarcinoma

Motivation: Microsatellite instability (MSI) in esophagogastric junction adenocarcinoma (EGA) can serve as a predictor of sensitivity to immunotherapy and affect the prognosis. Predicting MSI preoperatively can enable personalized and precise treatment for EGA patients. Goal(s): This study investigates the use of fast non-Gaussian diffusion-weighted imaging with deep learning-based reconstruction (DLRecon) to assess MSI in EGA. Approach: We compared image quality between conventional scanning (CS) and DLRecon, calculated diffusion parameters, and assessed their ability to distinguish MSI status. Results: DLRecon exhibited superior image quality and reduced scan time. Diffusion parameters effectively differentiated MSI status in EGA. Impact: DLRecon non-Gaussian DWI significantly improved image quality and reduced acquisition time. Multiple diffusion parameters may serve as imaging markers, and their combination provides high diagnostic accuracy for discriminating MSI status in EGA.