What if OA cartilage degradation is driven by the infrapatellar fat pad? Insights from lipodystrophy models DOI

Léa Loisay,

Xavier Houard

Osteoarthritis and Cartilage, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Implication of bone marrow adipose tissue in bone homeostasis during osteoarthritis DOI Creative Commons

Natalia Zapata‐Linares,

Indira Toillon,

Kristell Wanherdrick

et al.

Osteoarthritis and Cartilage, Journal Year: 2025, Volume and Issue: unknown

Published: March 1, 2025

Language: Английский

Citations

1
Osteoarthritis year in review 2024: biology DOI Creative Commons
Zsuzsa Jenei‐Lanzl, Frank Zaucke

Osteoarthritis and Cartilage, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

5
Rheumatic Diseases Following Metabolic and Bariatric Surgery: A Systematic Review and Meta-Analysis DOI

K de Souza,

Maria Luiza Rodrigues Defante,

Matheus Franco

et al.

Obesity Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 2, 2025

Language: Английский

Citations

0
A machine learning-based radiomics approach for differentiating patellofemoral osteoarthritis from non-patellofemoral osteoarthritis using Q-Dixon MRI DOI Creative Commons
Liangjing Lyu, Jing Ren, Wenjie Lu

et al.

Frontiers in Sports and Active Living, Journal Year: 2025, Volume and Issue: 7

Published: Jan. 17, 2025

This prospective diagnostic study aimed to assess the utility of machine learning-based quadriceps fat pad (QFP) radiomics in distinguishing patellofemoral osteoarthritis (PFOA) from non-PFOA using Q-Dixon MRI patients presenting with anterior knee pain. accuracy retrospectively analyzed data 215 (mean age: 54.2 ± 11.3 years; 113 women). Three predictive models were evaluated: a proton density-weighted image model, fraction and merged model. Feature selection was conducted analysis variance, logistic regression applied for classification. Data collected training, internal, external test cohorts. Radiomics features extracted sequences distinguish PFOA non-PFOA. The performance three compared area under curve (AUC) values Delong test. In training set (109 patients) internal (73 patients), model exhibited optimal performance, AUCs 0.836 [95% confidence interval (CI): 0.762-0.910] 0.826 (95% CI: 0.722-0.929), respectively. (33 achieved an AUC 0.885 0.768-1.000), sensitivity specificity 0.833 0.933, respectively (p < 0.001). Fat stronger value than shape-related features. Machine QFP accurately distinguishes non-PFOA, providing non-invasive approach

Language: Английский

Citations

0
What if OA cartilage degradation is driven by the infrapatellar fat pad? Insights from lipodystrophy models DOI

Léa Loisay,

Xavier Houard

Osteoarthritis and Cartilage, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

Citations

0