A Multi‐Structural Radiomic Digital Phenotyping Framework for Deciphering Preclinical Evolution of Degenerative Meniscal Tears

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.

Authors

Institutions

Publication Details

Journal
Advanced Science
Published
2026-10-30
DOI
https://doi.org/10.1002/advs.78506
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Multi‐Structural Radiomic Digital Phenotyping Framework for Deciphering Preclinical Evolution of Degenerative Meniscal Tears

Tianyu Chen, Changhai Ding, Yanbin Zhu, David John Hunter et al.
6 links
Advanced Science
Radiomics and Machine Learning in Medical Imaging
article

A Multi‐Structural Radiomic Digital Phenotyping Framework for Deciphering Preclinical Evolution of Degenerative Meniscal Tears

Tianyu Chen, Changhai Ding, Yanbin Zhu, David John Hunter, Qifeng Tao, Shengfa Li, Wei Chen, Ting Wang, Xin Miao, Shanshan Yang, Hao Liu, Wenbo Zhao, Hang Zhang, Juan Wang
article en

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.

Advanced Science
Hebei Medical University (CN), The University of Sydney (AU), University of Tasmania (AU), Royal North Shore Hospital (AU), Third Hospital of Hebei Medical University (CN), Third Affiliated Hospital of Southern Medical University (CN), Third People's Hospital of Chengdu (CN), Institute for Musculoskeletal Health (AU), Zhujiang Hospital (CN), Beijing Tsinghua Chang Gung Hospital (CN), Menzies Institute for Medical Research (AU), Southwest Jiaotong University (CN), Southern Medical University (CN), Chongqing Medical University (CN), Tsinghua University (CN)
Pfizer, GlaxoSmithKline, Novartis Pharmaceuticals Corporation, National Natural Science Foundation of China, National Institutes of Health, Young Scientists Fund, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Good health and well-being
Openalex Percentile: Top 13%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon