Predicting Rectal Cancer Response to Total Neoadjuvant Treatment Using an Artificial Intelligence Model Based on Magnetic Resonance Imaging and Clinical Data.

ABSTRACT:

Purpose

To develop a model for predicting response to total neoadjuvant treatment (TNT) for patients with locally advanced rectal cancer (LARC) based on baseline magnetic resonance imaging (MRI) and clinical data using artificial intelligence methods.

Methods

Baseline MRI and clinical data were curated from patients with LARC and analyzed using logistic regression (LR) and deep learning (DL) methods to predict TNT response retrospectively. We defined two groups of response to TNT as pathological complete response (pCR) versus non-pCR (Group 1), and high sensitivity [tumor regression grade (TRG) 0 and TRG 1] versus moderate sensitivity (TRG 2 or patients with TRG 3 and a reduction in tumor volume of at least 20% compared to baseline) versus low sensitivity (TRG 3 and a reduct

SUBMITTER: Ouyang G 

PROVIDER: S-EPMC10338728 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature