Integration of longitudinal load-bearing tissue MRI radiomics and neural network to predict knee osteoarthritis incidence.

ABSTRACT:

Background

Load-bearing structural degradation is crucial in knee osteoarthritis (KOA) progression, yet limited prediction models use load-bearing tissue radiomics for radiographic (structural) KOA incident.

Purpose

We aim to develop and test a Load-Bearing Tissue plus Clinical variable Radiomic Model (LBTC-RM) to predict radiographic KOA incidents.

Study design

Risk prediction study.

Methods

The 700 knees without radiographic KOA at baseline were included from Osteoarthritis Initiative cohort. We selected 2164 knee MRIs during 4-year follow-up. LBTC-RM, which integrated MRI features of meniscus, femur, tibia, femorotibial cartilage, and clinical variables, was developed in total development cohort (n = 1082, 542 cases vs. 540 controls) using neural network alg

SUBMITTER: Chen T 

PROVIDER: S-EPMC11937290 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature