📇 Rhumatologue · CAEN · (14) · Salarié

M SERGE KLEIN

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Auteur de référence en rhumatologie
32 articles scientifiques publiés — un praticien à la pointe de la recherche
Délais de RDV courts dans la région
150.8 rhumatos / 100 000 hab. — département bien doté

Rhumatologue

📍 CAEN (14)SalariéRPPS 10002102225

✨ Génération du profil synthétique IA en cours…

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Top publications · les plus citées

The epidemiology of osteoporosis--Bone Evaluation Study (BEST): an analysis of routine health insurance data

Deutsches Arzteblatt international · 2013

📚 133 citations🎯 RCR 6.06Top 6% NIH

Is T1ρ Mapping an Alternative to Delayed Gadolinium-enhanced MR Imaging of Cartilage in the Assessment of Sulphated Glycosaminoglycan Content in Human Osteoarthritic Knees? An in Vivo Validation Study

Radiology · 2016

📚 66 citations🎯 RCR 3.80Top 12% NIH🩺 Clinique🔓 Open Access

A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone

European radiology · 2021

📚 47 citations🎯 RCR 4.47Top 9% NIH🔓 Open Access📄 PDF gratuit ↗

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Abstract Objectives Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis. Methods The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. Tibial bone was segmented using a method that combines multi-atlas and appearance models. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). Results Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). The model that combined image features from all VOIs and covariates yielded an ROC AUC of 0.80 (CI: 0.73–0.87). Conclusion Our results suggest that radiomic features are useful imaging biomarkers of subchondral bone for the diagnosis of osteoarthritis. An advantage of assessing bone on MRI instead of on radiographs is that other tissues can be assessed simultaneously. Key Points • Subchondral bone plays a role in the osteoarthritis disease processes. • MRI radiomics is a potential method for quantifying changes in subchondral bone. • Semi-automatically extracted radiomic features of tibia differ between subjects without and with osteoarthritis.

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M SERGE KLEIN

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Lieu de consultation

Tarifs & secteur de conventionnement

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Top publications · les plus citées

Publications scientifiques (32) — classées par pathologie

Transversal21

Biomarqueurs / Auto-Ac3

Revue générale3

Corticothérapie1

IA en rhumatologie1

IRM ostéo-articulaire1

Lupus1

Vraie vie / RWE1

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