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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
✨ Génération du profil synthétique IA en cours…
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.
99
99 articles ont été cités au moins 99fois par d'autres chercheurs — preuve que ses travaux sont repris par la communauté médicale.
h-index
Total citations reçues
47 435
Nombre de fois où d'autres équipes ont mentionné ses publications dans leurs propres travaux.
Publications totales
878
Articles, revues et chapitres référencés dans les bases académiques internationales.
Articles influents
437
Publications ayant marqué leur domaine — chacune citée au moins 10 fois par d'autres chercheurs.
i10-index
Thématiques principales
Affiliations FR : Centre National de la Recherche Scientifique · Inserm · Université Paris Cité
Source : OpenAlex (CC0, OurResearch). Indicateurs académiques agrégés sur 250 M+ d'œuvres.
Articles déposés en accès libre sur l'archive ouverte des universités françaises (HAL) — gage d'activité de recherche en France.
A Deep Learning Algorithm for Liver Metastasis Detection at Contrast-enhanced Abdominal CT in Patients with Colorectal Cancer: A Comparative Study with Radiologists
2026ArticleRadiology: Imaging Cancer
CT-based deep learning prediction of complete response in intermediate-stage hepatocellular carcinoma treated with drug-eluting beads transarterial chemoembolization
2025ArticleBJR|Artificial Intelligence
PSMA immunohistochemistry as a diagnostic biomarker of hepatocellular carcinoma
2025ArticleJHEP Reports Innovation in Hepatology
Improving risk stratification and detection of early HCC using ultrasound-based deep learning models
2025ArticleJHEP Reports Innovation in Hepatology
Imaging features of recently identified low-grade vascular neoplasia of the liver: hepatic small vessel neoplasm and anastomosing hemangioma
2025ArticleEuropean Radiology
ESR Essentials: assessing the radiological response of liver metastases to systemic therapy—practice recommendations by the European Society of Gastrointestinal and Abdominal Radiology
2025ArticleEuropean Radiology
7-T MRI-based surrogate for histopathology examination of liver fibrosis
2025ArticleEuropean Radiology Experimental
Incidence and risk factors of contrast-associated acute kidney injury in patients hospitalised after contrast-enhanced computed tomography in the Emergency Department
2025ArticleInternational Journal of Emergency Medicine
Source : HAL — archive ouverte CCSD/CNRS (couvre articles, chapitres EMC, communications congrès, thèses).
GHU APHP NUP SITE BEAUJON
100 BD DU GENERAL LECLERC, 92118 CLICHY CEDEX
Secteur de conventionnement non disponible (médecin hospitalier ou non présent dans l'Annuaire santé CNAM des libéraux conventionnés).
Lien Doctolib = recherche Google site:doctolib.fr (le 1er résultat est presque toujours le profil correct s'il existe).
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2024
Radiology · 2024
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.
European radiology · 2024
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.
Source PubMed · Recherche par auteur (homonymes possibles, vérifier l'affiliation).
Annals of surgical oncology · 2026 · Journal Article
Gregory J, Tessier F, Baudouin M, Codjia T, et al.
Abdominal radiology (New York) · 2026 · Journal Article
Dioguardi Burgio M, Ronot M, Chassaing C, Copin P, et al.
European journal of nuclear medicine and molecular imaging · 2026 · Journal Article
Delaunay K, Dieudonné A, Dioguardi M, Bouattour M, et al.
Annals of surgical oncology · 2026 · Journal Article
Tessier F, Baudouin M, Codjia T, Sauvanet A, et al.
Annals of surgical oncology · 2026 · Journal Article
Tessier F, Baudouin M, Codjia T, Sauvanet A, et al.
European radiology · 2026 · Published Erratum
Bouattour M, Vilgrain V, Lesurtel M
European radiology · 2025 · Journal Article
Hamadouche M, Vilgrain V, Hobeika C, Castera L, et al.
European radiology · 2025 · Editorial
Bouattour M, Vilgrain V, Lesurtel M
Diagnostic and interventional imaging · 2025 · Letter
Matteini F, Vilgrain V, Ronot M
European journal of gastroenterology & hepatology · 2025 · Journal Article
Vullierme MP, Desterke C, Hillaire S, Parfait B, et al.
AJR. American journal of roentgenology · 2025 · Journal Article
Zhang LX, Dioguardi Burgio M, Vilgrain V, Fang C, et al.
European radiology experimental · 2025 · Journal Article
Dana J, Fattori A, Po C, Beaufrère A, et al.
Abdominal radiology (New York) · 2025 · Journal Article
Matteini F, Cannella R, Dioguardi Burgio M, Torrisi C, et al.
NEJM evidence · 2024 · Journal Article
Gu W, de Lédinghen V, Aubé C, Krag A, et al.
Radiology · 2024 · Journal Article
Sugimoto K, Moriyasu F, Dioguardi Burgio M, Vilgrain V, et al.
European radiology · 2024 · Editorial
Bouattour M, Vilgrain V, Sepulveda A
European radiology · 2024 · Journal Article
Gross M, Huber S, Arora S, Ze'evi T, et al.
Insights into imaging · 2024 · Journal Article
Matteini F, Cannella R, Garzelli L, Dioguardi Burgio M, et al.
Nature reviews. Gastroenterology & hepatology · 2024 · Journal Article
Pastor CM, Vilgrain V
Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2024 · Journal Article
Dioguardi Burgio M, Castera L, Oufighou M, Rautou PE, et al.
Journal of magnetic resonance imaging : JMRI · 2025 · Journal Article
Koch V, Gotta J, Chernyak V, Cengiz D, et al.
European radiology · 2024 · Published Erratum
Taouli B, Ba-Ssalamah A, Chapiro J, Chhatwal J, et al.
European radiology · 2024 · Journal Article
Ducatel A, Trillaud H, Reizine E, Vilgrain V, et al.
International journal of emergency medicine · 2025 · Journal Article
Lecomte E, Vaittinada Ayar P, Vilgrain V, Vaittinada Ayar P
Hepatology (Baltimore, Md.) · 2024 · Journal Article
Sidali S, Borie R, Sicre de Fontbrune F, El Husseini K, et al.
BJR artificial intelligence · 2025 · Journal Article
Dana J, Vardazaryan A, Gallix B, Ronot M, et al.
JHEP reports : innovation in hepatology · 2025 · Journal Article
Dana J, Meyer A, Paisant A, Rode A, et al.
JHEP reports : innovation in hepatology · 2025 · Journal Article
Véron K, Becht E, Laurent-Bellue A, Nicolle R, et al.
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2025 · Journal Article
Beaufrère A, Laurent-Bellue A, Lemoine A, Bourillon A, et al.
Cardiovascular and interventional radiology · 2024 · Journal Article
di Giuseppe R, Hansel B, Puyraimond Zemmour J, Vilgrain V, et al.
Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024 · Journal Article
Agirrezabal I, Pollock RF, Carion PL, Shergill S, et al.
Hepatology (Baltimore, Md.) · 2025 · Journal Article
Hobeika C, Ronot M, Guiu B, Ferraioli G, et al.
AJR. American journal of roentgenology · 2026 · Journal Article
Li L, Dioguardi Burgio M, Fetzer DT, Ferraioli G, et al.
Cardiovascular and interventional radiology · 2024 · Journal Article
di Giuseppe R, Hansel B, Puyraimond Zemmour J, Vilgrain V, et al.
PCT-HCC-Seg: Polyphasic CT hepatocellular carcinoma segmentation model
Model description PCT-HCC-Seg is a well performing model for polyphasic hepatocellular carcinoma segmentation. It is based on nnUNet, a widely popular segmentation network training package, and segments cases across bot
PCT-HCC-Seg: Polyphasic CT hepatocellular carcinoma segmentation model
Model description PCT-HCC-Seg is a well performing model for polyphasic hepatocellular carcinoma segmentation. It is based on nnUNet, a widely popular segmentation network training package, and segments cases across bot
Source : DataCite — DOIs pour datasets, logiciels, protocoles, registres patient. Hors articles (déjà couverts).