Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST)

Jiang Zhang; Tianshu Jiang; Lok-Chun Chan; Sing-Hin Lau; Wei Wang; Xinzhi Teng; Ping-Keung Chan; Jing Cai; Chunyi Wen

doi:10.1016/j.ocarto.2024.100448

Radiomics analysis of patellofemoral joint improves knee replacement risk prediction: Data from the Multicenter Osteoarthritis Study (MOST)

Osteoarthr Cartil Open. 2024 Feb 24;6(2):100448. doi: 10.1016/j.ocarto.2024.100448. eCollection 2024 Jun.

Authors

Jiang Zhang¹, Tianshu Jiang², Lok-Chun Chan², Sing-Hin Lau², Wei Wang², Xinzhi Teng¹, Ping-Keung Chan³, Jing Cai^{1

4}, Chunyi Wen^{2

4}

Affiliations

¹ Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China.
² Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong, China.
³ Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong, China.
⁴ Research Institute for Smart Ageing, The Hong Kong Polytechnic University, Hong Kong, China.

Abstract

Objective: Knee replacement (KR) is the last-resort treatment for knee osteoarthritis. Although radiographic evidence of tibiofemoral joint has been widely adopted for prognostication, patellofemoral joint has gained little attention and may hold additional value for further improvements. We aimed to quantitatively analyse patellofemoral joint through radiomics analysis of lateral view radiographs for improved KR risk prediction.

Design: From the Multicenter Osteoarthritis Study dataset, we retrospectively retrieved the initial-visit lateral left knee radiographs of 2943 patients aged 50 to 79. They were split into training and test cohorts at a 2:1 ratio. A comprehensive set of radiomic features were extracted within the best-performing subregion of patellofemoral joint and combined into a radiomics score (RadScore). A KR risk score, derived from Kellgren-Lawrence grade (KLG) of tibiofemoral joint and RadScore of patellofemoral joint, was developed by multivariate Cox regression and assessed using time-dependent area under receiver operating characteristic curve (AUC).

Results: While patellofemoral osteoarthritis (PFOA) was insignificant during multivariate analysis, RadScore was identified as an independent risk factor (multivariate Cox p-value < 0.001) for KR. The subgroup analysis revealed that RadScore was particularly effective in predicting rapid progressor (KR occurrence before 30 months) among early- (KLG < 2) and mid-stage (KLG = 2) patients. Combining two joints radiographic information, the AUC reached 0.89/0.87 for predicting 60-month KR occurrence.

Conclusions: The RadScore of the patellofemoral joint on lateral radiographs emerges as an independent prognostic factor for improving KR prognosis prediction. The KR risk score could be instrumental in managing progressive knee osteoarthritis interventions.

Keywords: Knee replacement; Lateral knee radiograph; Osteoarthritis; Patellofemoral joint; Radiomics.