We developed a deep learning classifier of rectal cancer response (tumor vs. no-tumor) to
total neoadjuvant treatment (TNT) from endoscopic images acquired before, during, and …

Background Rectal tumors display varying degrees of response to total neoadjuvant therapy
(TNT). We evaluated the performance of a convolutional neural network (CNN) in …

In this study, we evaluate the influence of normalization on the performance of deep learning
networks for tumor segmentation and the prediction of the pathological response of locally …

Background For locally advanced rectal cancer (LARC), accurate response evaluation is
necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and …

Background and Purpose Pretreatment prediction of the response to neoadjuvant
chemoradiotherapy (NCRT) helps to determine the subsequent plans for the patients with …

BACKGROUND: A barrier to the widespread adoption of watch-and-wait management for
locally advanced rectal cancer is the inaccuracy and variability of identifying tumor response …

Background and Aims: Although the wait and watch (W&W) strategy is a treatment choice for
locally advanced rectal cancer (LARC) patients who achieve clinical complete response …

Background: Fully automatic prognostic prediction by novel deep learning networks is
achievable but has not been reported for locally advanced rectal cancer (LARC). Our study …

Identifying complete response (CR) after rectal cancer preoperative treatment is critical to
deciding subsequent management. Imaging techniques, including endorectal ultrasound …

This retrospective study aims to evaluate the generalizability of a promising state-of-the-art
multitask deep learning (DL) model for predicting the response of locally advanced rectal …