A robust semi-automatic delineation workflow using denoised diffusion weighted magnetic resonance imaging for response assessment of patients with esophageal cancer treated with neoadjuvant chemoradiotherapy

Abstract

Background and PurposeDiffusion weighted magnetic resonance imaging (DW-MRI) can be prognostic for response to neoadjuvant chemotherapy (nCRT) in patients with esophageal cancer. However, manual tumor delineation is labor intensive and subjective. Furthermore, noise in DW-MRI images will propagate into the corresponding apparent diffusion coefficient (ADC) signal. In this study a workflow is investigated that combines a denoising algorithm with semi-automatic segmentation for quantifying ADC changes. Materials and MethodsTwenty patients with esophageal cancer who underwent nCRT before esophagectomy were included. One baseline and five weekly DW-MRI scans were acquired for every patient during nCRT. A self-supervised learning denoising algorithm, Patch2Self, was used to denoise the DWI-MRI images. A semi-automatic delineation workflow (SADW) was next developed and compared with a manually adjusted workflow (MAW). The agreement between workflows was determined using the Dice coefficients and Brand Altman plots. The prognostic value of ADCmean increases (%/week) for pathologic complete response (pCR) was assessed using c-statistics. ResultsThe median Dice coefficient between the SADW and MAW was 0.64 (interquartile range 0.20). For the MAW, the c-statistic for predicting pCR was 0.80 (95% confidence interval (CI):0.56–1.00). The SADW showed a c-statistic of 0.84 (95%CI:0.63–1.00) after denoising. No statistically significant differences in c-statistics were observed between the workflows or after applying denoising. ConclusionsThe SADW resulted in non-inferior prognostic value for pCR compared to the more laborious MAW, allowing broad scale applications. The effect of denoising on the prognostic value for pCR needs to be investigated in larger cohorts.