In Osteoarthritis and cartilage ; h5-index 62.0
OBJECTIVES : To develop and validate a nomogram to detect improved knee pain in osteoarthritis (OA) by integrating magnetic resonance imaging (MRI) radiomics signature of subchondral bone and clinical characteristics.
METHODS : Participants were selected from the Vitamin D Effects on Osteoarthritis (VIDEO) study. The primary outcome was 20% improvement of knee pain score over 2 years in participants administrated either vitamin D or placebo. Radiomics features of subchondral bone and clinical characteristics from 216 participants were extracted and analyzed. The participants were randomly split into the training and validation cohorts at a ratio of 8:2. Least absolute shrinkage and selection operator (LASSO) regression was used to select features and generate radiomics signatures. The optimal radiomics signature and clinical indicators were fitted into a nomogram using multivariable logistic regression model.
RESULTS : The nomogram showed favorable discrimination performance [AUCtraining, 0.79 (95%CI: 0.72-0.79), AUCvalidation, 0.83 (95%CI: 0.70-0.96)] as well as a good calibration. Additional contributing value of fusion radiomics signature to the nomogram was statistically significant (NRI, 0.23; IDI, 0.14, p < 0.001 in training cohort and NRI, 0.29; IDI, 0.18, p < 0.05 in validating cohort). Decision curve analysis confirmed the clinical usefulness of nomogram.
CONCLUSION : The radiomics-based nomogram comprising the MR radiomics signature and clinical variables achieves a favorable predictive efficacy and accuracy in differentiating improvement in knee pain among OA patients. This proof-of-concept study provides a promising way to predict clinically meaningful outcomes.
Lin Ting, Peng Shengwang, Lu Shilong, Fu Shuai, Zeng Dong, Li Jia, Chen Tianyu, Fan Tianxiang, Lang Chao, Feng Siyuan, Ma Jianhua, Zhao Chen, Antony Benny, Cicuttini Flavia, Quan Xianyue, Zhu Zhaohua, Ding Changhai
2022-Nov-02
machine learning, nomogram, osteoarthritis, radiomics
