A5049072650- Liver Disease Diagnosis and Treatment
- Radiomics and Machine Learning in Medical Imaging
- Hepatocellular Carcinoma Treatment and Prognosis
- Cystic Fibrosis Research Advances
- Renal cell carcinoma treatment
- Genetic and Kidney Cyst Diseases
- Endometrial and Cervical Cancer Treatments
- Pediatric Hepatobiliary Diseases and Treatments
- Medical Imaging Techniques and Applications
- Medical Imaging and Analysis
- Liver Disease and Transplantation
- MRI in cancer diagnosis
- Gastrointestinal disorders and treatments
- 3D Shape Modeling and Analysis
- Prostate Cancer Treatment and Research
- IgG4-Related and Inflammatory Diseases
- Congenital Ear and Nasal Anomalies
- Gastrointestinal Bleeding Diagnosis and Treatment
- Colorectal Cancer Screening and Detection
- Cancer Genomics and Diagnostics
- Hepatitis B Virus Studies
- Child Nutrition and Feeding Issues
- Viral gastroenteritis research and epidemiology
- Tracheal and airway disorders
- Medical Image Segmentation Techniques
McGill University Health Centre
2022-2025
Université de Strasbourg
2020-2024
McGill University
2022-2024
Inserm
2020-2024
Institut de Recherche contre les Cancers de l’Appareil Digestif
2022-2024
Institut de Chirurgie Guidée par l'Image
2020-2024
Institute for Translational Medicine and Liver Disease
2021-2023
Université Paris Cité
2020-2023
Hôpital Necker-Enfants Malades
2021-2023
Hôpital Civil, Strasbourg
2021-2023
Rationale and Objectives: Fat quantification accuracy using a commercial single-voxel high speed T 2 -corrected multi-echo (HISTO) technique its robustness to R * variations at 3.0 T, such as those introduced by iron in liver, has not been fully established. This study evaluated HISTO sought reproduce results 1.5 T. Methods: Phantoms were prepared with range of fat content *. Data acquired Dixon technique. was function The patient included 239 consecutive patients. or techniques. techniques...
Abstract CHARGE syndrome, due to CHD7 pathogenic variations, is an autosomal dominant disorder characterized by a large spectrum of severity. Despite the great number variations reported, no clear genotype‐to‐phenotype correlation has been reported. Unsupervised machine learning and clustering was undertaken using retrospective cohort 42 patients, after deep radiologic clinical phenotyping, establish genotype–phenotype for ‐related syndrome. It resulted in three clusters showing phenotypes...
<h3>Introduction/Background</h3> Evaluation of prognostic factors is crucial in patients with endometrial cancer to ensure optimal treatment planning and accurate prognosis assessment. This study introduces an end-to-end MRI-based deep learning (DL) pipeline from tumor uterus segmentation myometrial invasion (DMI) cervical stroma (CSI) prediction assist radiologists pre-operative workup. <h3>Methodology</h3> 178 pre-treatment pelvic sagittal T2-weighted images were obtained (DMI: 90/178 –...
Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline tumor uterus segmentation from magnetic resonance imaging (MRI) images to predict myometrial invasion cervical stroma thus assist clinicians pre-operative workups. Two experts consensually reviewed the MRIs assessed stromal as per International Federation Gynecology Obstetrics staging classification, compare...