Objectives To evaluate the performance of machine learning models in predicting
pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in …

Purpose The purpose of this study was to evaluate the diagnostic accuracy of artificial
intelligence (AI) models with magnetic resonance imaging (MRI) in predicting pathological …

Objective Artificial intelligence (AI) holds enormous potential for noninvasively identifying
patients with rectal cancer who could achieve pathological complete response (pCR) …

Purpose This study aimed to assess tumor regression grade (TRG) in patients with rectal
cancer after neoadjuvant chemoradiotherapy (NCRT) through a machine learning–based …

Objective Radiomics based on magnetic resonance imaging (MRI) shows potential for
prediction of therapeutic effect to neoadjuvant chemoradiotherapy (nCRT) in locally …

Background To analyse the performance of multicentre pre-treatment MRI-based radiomics
(MBR) signatures combined with clinical baseline characteristics and neoadjuvant treatment …

Objective To investigate whether pretreatment MRI-based radiomics of locally advanced
rectal cancer (LARC) and/or the surrounding mesorectal compartment (MC) can predict …

BACKGROUND: Accurate prediction of response to neoadjuvant chemoradiotherapy is
critical for subsequent treatment decisions for patients with locally advanced rectal cancer …

Objectives The aim of this study was two-fold:(1) to develop and externally validate a
multiparameter MR-based machine learning model to predict the pathological complete …

Objective The aim of this study was to develop and validate a decision support model using
data mining algorithms, based on morphologic features derived from MRI images, to …