A Multi-Structural Radiomic Digital Phenotyping Framework for Deciphering Preclinical Evolution of Degenerative Meniscal Tears.
Authors
Affiliations (11)
Affiliations (11)
- Hebei Medical University Clinical Medicine Postdoctoral Station (Hebei Medical University Third Hospital), Shijiazhuang, Hebei, China.
- Department of Orthopaedic Surgery, NHC Key Laboratory of Intelligent Orthopaedic Equipment, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.
- Department of Orthopaedics, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
- Department of Arthroplasty and Sport Medicine, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
- Department of Orthopaedics, The Third Affiliated Hospital of Southern Medical University, Guangzhou, China.
- Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China.
- Department of Rheumatology, Kolling Institute, Royal North Shore Hospital and Sydney Musculoskeletal Health, University of Sydney, Australia.
- Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia.
- Clinical Research Centre, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University, Beijing, China.
- Medical Research Center, The Third People's Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University & The Second Affiliated Hospital of Chengdu, Chongqing Medical University, Chengdu, Sichuan, China.
- Joint Surgery Department, Hebei Medical University Third Hospital, Shijiazhuang, Hebei, China.
Abstract
Degenerative meniscus tears (DMTs) are pivotal in knee osteoarthritis progression, yet conventional MRI often fails to capture the subvisual alterations preceding macroscopic failure. We developed a multi-structural digital risk stratification framework (MSC-RM) that integrates standardized radiomic signatures from six knee structures (femur, tibia, cartilages, and menisci) derived from 3D double-echo steady-state MRI with baseline clinical risk profiles. Utilizing a longitudinal cohort (n = 674), our framework decoded complex degeneration patterns, achieving superior 4-year predictive performance (AUC 0.871). Survival analysis revealed that high-risk individuals had a 6.12-fold greater hazard of developing DMTs than the low-risk group. Furthermore, a reader test with five resident surgeons demonstrated that MSC-RM significantly enhanced diagnostic consistency (Kappa increased from 0.38 to 0.55), effectively bridging the expertise gap. By shifting the paradigm from reactive lesion detection to proactive risk management, this framework provides a scalable strategy for precision preventive medicine and establishes digital biomarkers as critical tools for optimizing musculoskeletal health in aging populations.