Predicting treatment response to neoadjuvant chemotherapy in locally advanced rectal cancer: A combined deep learning and machine learning approach utilizing longitudinal multi-sequence MRI.

ABSTRACT:

Purpose

To develop and validate deep leaning-based machine learning models using longitudinal multi-sequence MRI for predicting treatment response of patients with locally advanced rectal cancer (LARC) to neoadjuvant chemotherapy (NCT).

Methods

This retrospective study included 169 LARC patients who received 2-4 cycles of CAPOX chemotherapy before surgery. Patients were randomly divided into a training cohort (n = 118) and a test cohort (n = 51). High-resolution paired MRI sequences (CE-T1WI, T2WI, DWI) were acquired before and after NCT. These sequences were then independently input into a DenseNet121 network to generate predictive probability scores, which served as deep leaning (DL) signatures. Prediction models were built using an SVM classifier. These models were built

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12933455 | biostudies-literature | 2026 Jun

REPOSITORIES: biostudies-literature