Noncontrast MRI-based machine learning and radiomics signature can predict the severity of primary lower limb lymphedema.

ABSTRACT:

Objective

According to International Lymphology Society guidelines, the severity of lymphedema is determined by the difference in volume between the affected limb and the healthy side divided by the volume of the healthy side. However, this method of measuring volume is time consuming, laborious, and has certain errors in clinical applications. Therefore, this study aims to explore whether machine learning radiomics features based on noncontrast magnetic resonance imaging (MRI) can predict the severity of primary lower limb lymphedema.

Methods

A retrospective analysis of 119 patients with primary lower limb lymphedema. The enrolled patients were divided into a nonsevere group (mild and moderate) and a severe group. Using the semiautomatic threshold method in ITK-snap softwar

SUBMITTER: Ren J 

PROVIDER: S-EPMC12014297 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature