📇 Rhumatologue · BRON CEDEX · (69) · Salarié

MME PIA AKL

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Plateau technique de référence
Hospices Civils de Lyon (HCL) — équipements et expertise pointus pour les cas complexes
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7 articles scientifiques publiés — formation continue solide
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144.6 rhumatos / 100 000 hab. — département bien doté

Rhumatologue

📍 BRON CEDEX (69)SalariéRPPS 10100933794

Publications actives

11 publications sur 5 ans

📊 Reconnaissance scientifique : 7/100📝 17 articles publiés🇫🇷 Publications académiques françaises (8)

✨ Génération du profil synthétique IA en cours…

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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

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients

European journal of nuclear medicine and molecular imaging · 2022

📚 33 citations🎯 RCR 3.52Top 13% NIH🔓 Open Access📄 PDF gratuit ↗

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Abstract Purpose The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastases significantly affecting survival rates. Nodes are currently identified by a physician, but this process is time-consuming and prone to errors. In this paper, we investigate the use of artificial intelligence–based methods to increase the accuracy and consistency of this process. Methods Whole-body 18F-labelled fluoro-2-deoxyglucose ([18F]FDG) positron emission tomography/computed tomography ([18F]FDG-PET/CT) scans (Philips Gemini TF) from 134 patients were retrospectively analysed. The thorax was automatically located, and then slices were fed into a U-Net to identify candidate regions. These regions were split into overlapping 3D cubes, which were individually predicted as positive or negative using a 3D CNN. From these predictions, pathological mediastinal nodes could be identified. A second cohort of 71 patients was then acquired from a different, newer scanner (GE Discovery MI), and the performance of the model on this dataset was tested with and without transfer learning. Results On the test set from the first scanner, our model achieved a sensitivity of 0.87 (95% confidence intervals [0.74, 0.94]) with 0.41 [0.22, 0.71] false positives/patient. This was comparable to the performance of an expert. Without transfer learning, on the test set from the second scanner, the corresponding results were 0.53 [0.35, 0.70] and 0.24 [0.10, 0.49], respectively. With transfer learning, these metrics were 0.88 [0.73, 0.97] and 0.69 [0.43, 1.04], respectively. Conclusion Model performance was comparable to that of an expert on data from the same scanner. With transfer learning, the model can be applied to data from a different scanner. To our knowledge it is the first study of its kind to go directly from whole-body [18F]FDG-PET/CT scans to pathological mediastinal lymph node localisation.

A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

Magma (New York, N.Y.) · 2021

📚 28 citations🎯 RCR 2.52Top 20% NIH

Visual ensemble selection of deep convolutional neural networks for 3D segmentation of breast tumors on dynamic contrast enhanced MRI

European radiology · 2023

📚 13 citations🎯 RCR 1.84🔓 Open Access📄 PDF gratuit ↗

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Abstract Objectives To develop a visual ensemble selection of deep convolutional neural networks (CNN) for 3D segmentation of breast tumors using T1-weighted dynamic contrast-enhanced (T1-DCE) MRI. Methods Multi-center 3D T1-DCE MRI (n = 141) were acquired for a cohort of patients diagnosed with locally advanced or aggressive breast cancer. Tumor lesions of 111 scans were equally divided between two radiologists and segmented for training. The additional 30 scans were segmented independently by both radiologists for testing. Three 3D U-Net models were trained using either post-contrast images or a combination of post-contrast and subtraction images fused at either the image or the feature level. Segmentation accuracy was evaluated quantitatively using the Dice similarity coefficient (DSC) and the Hausdorff distance (HD95) and scored qualitatively by a radiologist as excellent, useful, helpful, or unacceptable. Based on this score, a visual ensemble approach selecting the best segmentation among these three models was proposed. Results The mean and standard deviation of DSC and HD95 between the two radiologists were equal to 77.8 ± 10.0% and 5.2 ± 5.9 mm. Using the visual ensemble selection, a DSC and HD95 equal to 78.1 ± 16.2% and 14.1 ± 40.8 mm was reached. The qualitative assessment was excellent (resp. excellent or useful) in 50% (resp. 77%). Conclusion Using subtraction images in addition to post-contrast images provided complementary information for 3D segmentation of breast lesions by CNN. A visual ensemble selection allowing the radiologist to select the most optimal segmentation obtained by the three 3D U-Net models achieved comparable results to inter-radiologist agreement, yielding 77% segmented volumes considered excellent or useful. Key Points • Deep convolutional neural networks were developed using T1-weighted post-contrast and subtraction MRI to perform automated 3D segmentation of breast tumors. • A visual ensemble selection allowing the radiologist to choose the best segmentation among the three 3D U-Net models outperformed each of the three models. • The visual ensemble selection provided clinically useful segmentations in 77% of cases, potentially allowing for a valuable reduction of the manual 3D segmentation workload for the radiologist and greatly facilitating quantitative studies on non-invasive biomarker in breast MRI.

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MME PIA AKL

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Publications académiques en France

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Publications scientifiques (7) — classées par pathologie

Transversal3

IA en rhumatologie2

Épidémiologie & registres1

Pédiatrie1

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