MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

ABSTRACT:

Background

To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.

Methods

A retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for textu

SUBMITTER: Mo X 

PROVIDER: S-EPMC9902579 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature