📇 Rhumatologue · CLICHY CEDEX · (92) · Hospitalier

MME VALERIE VILGRAIN

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3 raisons identifiées

Plateau technique de référence
Assistance publique – Hôpitaux de Paris (APHP) — équipements et expertise pointus pour les cas complexes
Auteur de référence en rhumatologie
33 articles scientifiques publiés — un praticien à la pointe de la recherche
Délais de RDV courts dans la région
136 rhumatos / 100 000 hab. — département bien doté

Rhumatologue

📍 CLICHY CEDEX (92)HospitalierRPPS 10001021996

📊 Reconnaissance scientifique : 99/100📝 878 articles publiés🇫🇷 Publications académiques françaises (8)

✨ Génération du profil synthétique IA en cours…

Activité de recherche & publications

Indicateurs publics agrégés sur 250 M+ d'œuvres scientifiques (OpenAlex, PubMed). Traduits ici en langage patient.

Influence scientifique

Données ANS publiques (Licence Ouverte 2.0) · Enrichissements MonRhumato 100 % opt-in · Toute personne référencée peut demander la suppression ou la rectification.

Top publications · les plus citées

Prospective Comparison of Attenuation Imaging and Controlled Attenuation Parameter for Liver Steatosis Diagnosis in Patients With Nonalcoholic Fatty Liver Disease and Type 2 Diabetes

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2024

📚 23 citations🎯 RCR 5.17Top 7% NIH

US Markers and Necroinflammation, Steatosis, and Fibrosis in Metabolic Dysfunction-associated Steatotic Liver Disease: The iLEAD Study

Radiology · 2024

📚 22 citations🎯 RCR 6.12Top 6% NIH🔓 Open Access

Lire l'abstract Crossref ↓

US dispersion slope was associated with lobular inflammation at histologic examination and demonstrated fair performance for identifying moderate to severe lobular inflammation in patients with metabolic dysfunction–associated steatotic liver disease.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics

European radiology · 2024

📚 19 citations🎯 RCR 5.67Top 6% NIH🔓 Open Access📄 PDF gratuit ↗

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Abstract Objectives To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). Materials and methods This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). Results In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. Conclusion Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. Clinical relevance statement Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. Key Points • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

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MME VALERIE VILGRAIN

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Activité de recherche & publications

Publications académiques en France

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Top publications · les plus citées

Publications scientifiques (33) — classées par pathologie

Transversal20

IRM ostéo-articulaire3

Épidémiologie & registres2

IA en rhumatologie2

Biomarqueurs / Auto-Ac1

Case report / série1

Pharmacovigilance1

Qualité de vie / PROMs1

Revue / méta-analyse1

Revue générale1

Vraie vie / RWE1

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