Endoscopy-Based Deep Convolutional Neural Network Predicts Response to Neoadjuvant Treatment for Locally Advanced Rectal Cancer.

ABSTRACT: 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 (cCR) after neoadjuvant therapy (NT), the issue on consistency between cCR and pathological CR (pCR) remains unsettled. Herein, we aimed to develop a deep convolutional neural network (DCNN) model using endoscopic images of LARC patients after NT to distinguish tumor regression grade (TRG) 0 from non-TRG0, thus providing strength in identifying surgery candidates. Methods: A total of 1000 LARC patients (6,939 endoscopic images) who underwent radical surgery after NT from April 2013 to April 2021 at the Sixth Affiliated Hospital, Sun Yat-sen University were retrospectively included in our study. Patients

SUBMITTER: Chen X 

PROVIDER: S-EPMC9091815 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature