Deep learning-based imaging reconstruction for MRI after neoadjuvant chemoradiotherapy for rectal cancer: effects on image quality and assessment of treatment response

Abstract

Purpose To investigate the efects of deep learning-based imaging reconstruction (DLR) on the image quality of MRI of rectal cancer after chemoradiotherapy (CRT), and its accuracy in diagnosing pathological complete responses (pCR).

Methods We included 39 patients (men: women, 21:18; mean age ± standard deviation, 59.1 ± 9.7 years) with mid-to-lower rectal cancer who underwent a long-course of CRT and high-resolution rectal MRIs between January 2020 and April 2021. Axial T2WI was reconstructed using the conventional method (MRIconv) and DLR with two diferent noise reduction factors (MRIDLR30 and MRIDLR50). The signal-to-noise ratio (SNR) of the tumor was measured. Two experienced radiologists inde- pendently made a blind assessment of the complete response on MRI. The sensitivity and specifcity for pCR were analyzed using a multivariable logistic regression analysis with generalized estimating equations.

Results Thirty-four patients did not have a pCR whereas fve (12.8%) had pCR. Compared with the SNR of MRIconv (mean ± SD, 7.94 ± 1.92), MRIDLR30 and MRIDLR50 showed higher SNR (9.44 ± 2.31 and 11.83 ± 3.07, respectively) (p < 0.001). Compared to MRIconv, MRIDLR30 and MRIDLR50 showed signifcantly higher specifcity values (p < 0.036) while the sensitivity values were not signifcantly diferent (p > 0.301). The sensitivity and specifcity for pCR were 48.9% and 80.8% for MRIconv; 48.9% and 88.2% for MRIDLR30; and 38.8% and 86.7% for MRIDLR50, respectively.

Conclusion DLR produced MR images with higher resolution and SNR. The specifcity of MRI for identifcation of pCR was signifcantly higher with DLR than with conventional MRI.