An MRI-based radiomics framework for early identification and progression stratification in knee osteoarthritis: data from the osteoarthritis initiative.

ABSTRACT:

Objectives

To develop a cascaded machine learning model based on MRI radiomics features from cartilage and subchondral bone to predict the incidence and progression of knee osteoarthritis (KOA), thereby addressing the need for early intervention in pre-radiographic stages.

Materials and methods

The study analyzed 456 participants without radiographic OA (Kellgren-Lawrence [KL] 0-1) at baseline were selected from the Osteoarthritis Initiative (OAI) and randomly divided into training and testing cohorts (7:3). Participants underwent 3D DESS MRI of the right knee and were stratified into incident KOA and non-KOA groups based on 4-year radiographic outcomes, using 1:2 propensity score matching (PSM) to adjust for baseline confounders. Early and late progressors were further clas

SUBMITTER: Fu J 

PROVIDER: S-EPMC12577384 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature