Deep learning algorithms for predicting pathological complete response in MRI of rectal cancer patients undergoing neoadjuvant chemoradiotherapy: a systematic review.

ABSTRACT:

Purpose

This systematic review examines the utility of deep learning algorithms in predicting pathological complete response (pCR) in rectal cancer patients undergoing neoadjuvant chemoradiotherapy (nCRT). The primary goal is to evaluate the performance of MRI-based artificial intelligence (AI) models and explore factors affecting their diagnostic accuracy.

Methods

The review followed PRISMA guidelines and is registered with PROSPERO (CRD42024628017). Literature searches were conducted in PubMed, Embase, and Cochrane Library using keywords such as "artificial intelligence," "rectal cancer," "MRI," and "pathological complete response." Articles involving deep learning models applied to MRI for predicting pCR were included, excluding non-MRI data and studies without AI applica

SUBMITTER: Jong BK 

PROVIDER: S-EPMC11753312 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature