T2 Texture Analysis of Knee Cartilage in Healthy Individuals: Impact of Regular Running and Demographics

Motivation: To explore early detection of articular cartilage degeneration using quantitative MRI (qMRI), physical activity and demographic factors. Goal(s): To investigate the relationship between demographic characteristics, regular running, and GLCM texture features in T2-maps of the tibiofemoral cartilage in a large cohort of healthy adults. Approach: A cohort of 1,214 healthy participants underwent T2-mapping MRI, with texture features analyzed using multivariate regression models that accounted for sex, age, BMI, and running distance. Results: Significant texture variations were associated with male sex, older age, overweight and obesity, and high weekly running distances, suggesting early degeneration similar to OA patients and the need for targeted interventions. Impact: These results may advance machine learning models using non-invasive T2 texture analysis to identify osteoarthritis risk, promoting research into preventive strategies and enhancing monitoring and early interventions for joint health through improved understanding of cartilage changes.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/0965
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

T2 Texture Analysis of Knee Cartilage in Healthy Individuals: Impact of Regular Running and Demographics

Dominik Vilímek, Daniel Jandačka, Vahid Ghodrati, Miika T. Nieminen et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Radiomics and Machine Learning in Medical Imaging
article

T2 Texture Analysis of Knee Cartilage in Healthy Individuals: Impact of Regular Running and Demographics

Dominik Vilímek, Daniel Jandačka, Vahid Ghodrati, Miika T. Nieminen, Jaroslav Uchytil, Victor Casula
article en

Abstract

Motivation: To explore early detection of articular cartilage degeneration using quantitative MRI (qMRI), physical activity and demographic factors. Goal(s): To investigate the relationship between demographic characteristics, regular running, and GLCM texture features in T2-maps of the tibiofemoral cartilage in a large cohort of healthy adults. Approach: A cohort of 1,214 healthy participants underwent T2-mapping MRI, with texture features analyzed using multivariate regression models that accounted for sex, age, BMI, and running distance. Results: Significant texture variations were associated with male sex, older age, overweight and obesity, and high weekly running distances, suggesting early degeneration similar to OA patients and the need for targeted interventions. Impact: These results may advance machine learning models using non-invasive T2 texture analysis to identify osteoarthritis risk, promoting research into preventive strategies and enhancing monitoring and early interventions for joint health through improved understanding of cartilage changes.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Radiomics and Machine Learning in Medical Imaging
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T2 Texture Analysis of Knee Cartilage in Healthy Individuals: Impact of Regular Running and Demographics — Dominik Vilímek, Daniel Jandačka, et al. · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (2025) | TGRS Research Map | TGRS