Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jun 3, 2025
Date Accepted: Dec 17, 2025
Development and Validation of Nomograms based on X-ray radiomics for Predicting the Pain Progression of Knee Osteoarthritis: Data from FNIH
ABSTRACT
Background:
Knee osteoarthritis (KOA) is one of the most prevalent chronic musculoskeletal disorders among the elderly population. Screening populations at risk for rapid progression of osteoarthritis and implementing appropriate early intervention strategies is advantageous for the treatment and prognosis of affected patients.
Objective:
This study aims to construct and validate a nomogram model based on X-ray radiomics to effectively identify individuals experiencing progression of KOA pain.
Methods:
The FNIH Biomarkers study included a total of 600 participants, who were classified as pain progressors (n=297), non-pain progressors (n=303) according to the change of the WOMAC pain score ( ≥9 on a 0-100 scale) during the follow-up period of 24-48 months. Exclude X-rays that lack defined spacing in the DICOM image. Fully automatic selection of subchondral bone regions on the inner and outer edges of the tibia and femur as regions of interest (ROI), and extraction of radiomics feature for different combinations of ROIs. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to select features and generate a radiomics score. The radiomics score, along with clinical indicators, was incorporated into nomograms utilizing a multivariable logistic regression model. The subgroup analysis focused solely on the progression of pain and cases with no progression at all. The receiver operating characteristic (ROC) curve, along with calibration and decision curves, was employed to assess the discriminative performance.
Results:
A total of 450 participants were included in the study. Nomogram1 and nomogram2 demonstrated good discrimination for predicting KOA pain progression, achieving AUC values of 0.766 and 0.753, respectively. The mean absolute errors (MAE) of the calibration curves were 0.012 and 0.008, indicating strong calibration accuracy. Decision curve analysis confirmed the clinical effectiveness of both nomograms. Subgroup analysis revealed that nomogram3 and nomogram4 had AUCs of 0.795 and 0.740, respectively, showing good predictive performance.
Conclusions:
The nomograms based on X-ray radiomics have demonstrated excellent predictive capability and accuracy in forecasting the progression of KOA pain.
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