Generative adversarial networks: multiparametric, multiregion super-resolution MRI in predicting lymph node metastasis in rectal cancer.

ABSTRACT:

Objective

Development of a preoperative mesorectal lymph node metastasis (LNM) prediction model for rectal cancer (RC) based on intratumoral and multiregional peritumoral radiomics features extracted from super-resolution multiparametric MRI.

Materials and methods

This multicenter study included preoperative MRI data from 243 rectal cancer patients (194 from center A, 49 from center B) with SR reconstruction and scoring. Radiomic features were extracted from tumor, peri-3mm and peri-5mm on SR-DWI and SR-T2WI images. The least absolute shrinkage and selection operator (LASSO) and the maximum relevance minimum redundancy (mRMR) were used for feature selection and dimensionality reduction. DWI_T2WI_INTRA, DWI_T2WI_IntraPeri3mm, DWI_T2WI_InterPeri5mm models were developed employ

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12770130 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature