Virtual gadolinium contrast enhancement MRI using deep learning GAN in rectal cancer: a proof-of-concept two-center study

Motivation: It would be clinically beneficial if GBCAs enhancement could be accurately synthesized without any GBCAs administration though AI. Goal(s): To evaluate the feasibility of deep learning in synthesizing VTE based on noncontrast rectal cancer MRIs obtained without the use of gadolinium. Approach: Deep learning networks were trained and validated on nonenhanced conventional pelvic MRI (T1WI, T2WI, DWI-ADC) using GAN. MRI scans included 697 rectal cancer patients from two hospitals. Results: Quantitative and qualitative evaluation of three-channel VTE was significantly better than that of two-channel and one-channel (P＜0.001). The T staging accuracy of VTE was comparable with that of RTE. Impact: VTE synthesized by deep learning based on noncontrast MRI can overcome the limitations of RTE and aid in the clinical diagnosis and management of rectal cancer as a noninvasive, save, affordable and time-saving method that does not require GBCAs.