DESCRIPTION :
Brain tumors and central nervous system tumors have an incidence of 24.71 cases per 100,000 people. Meningiomas are the most frequent (39.7%) and gliomas account for approximately 80% of malignant tumors. The latter are associated with a poor prognosis, with a median survival of 15 months for glioblastomas, the most common type of glioma [2]. Due to the high risk and difficulty of invasive procedures, the diagnosis and monitoring of cancer in neuro-oncology rely heavily on medical imaging, such as magnetic resonance imaging (MRI) and positron emission tomography (PET) radiolabeled amino acids like 18F-FDOPA or 18F-FET which are recommended by international groups.
Biopsy planning is comprised in these recommendations, as it better delineates the tumor extent compared to conventional MRI. In particular, amino acid PET helps locate the most aggressive part of the lesion and guide the biopsy target, which is crucial for patient prognosis and subsequent treatment. This part is currently determined by the tumor regions with the highest PET uptake [4] while several publications have shown that the kinetics of tumor metabolism during acquisition can provide additional and relevant information at the time of initial diagnosis to identify the most aggressive tumors especially when extracted at the voxel level and has never been applied to biopsy planning. As early as 2011, a study by Kunz et al. showed that dynamic hotspots could be useful in identifying malignant parts of tumors.
Moreover, 18F-FDOPA PET is also recommended by the RANO group in addition to MRI for recurrent gliomas in the differential diagnosis between radionecrosis, a treatment-induced change, and true progression. This is the primary indication for amino acid PET in gliomas, as it has better diagnostic performance than conventional MRI. Numerous studies in the literature have demonstrated the excellent performance of amino acid PET based on simple image analysis or more advanced analysis with massive extraction of tumor characteristics coupled with an artificial intelligence model for classification. During the last decade, deep neural network models have been developed to automate certain healthcare-related tasks and assist physicians in their clinical practice. When applied to medical imaging data, such models are often designed for specific tasks, need large amounts of annotated data, and demonstrate little generalization capability. Recently, foundation models have gained
prominence for their ability to learn robust and generic latent representations of data in a self-supervised way. However, such models are still very rare for 3D imaging, common in the medical field. In particular, no such model integrates PET imaging. But these models could improve performance on specific subtasks through fine-tuning on a small number of annotated data, which is particularly interesting in the context of rare tumors such as gliomas. Furthermore, despite an exponential number of publications attempting to develop a machine learning model to answer a specific question, only a very small number make it into clinical routine, often due to a lack of evaluation in this context and model acceptability. The representations learned by a foundation model being more robust and general, the potential for explainability is greater, and they are therefore more likely to be accepted as tools for clinical decision support.
Research environment
This project brings together researchers from the IADI lab and from the Inria Center of the University of Lorraine and the LORIA lab.
The IADI (Adaptive Diagnostic and Interventional Imaging) laboratory is a joint research unit of the University of Lorraine/INSERM, specializing in medical imaging and developing innovative technologies to improve the quality of medical images and the resulting diagnoses. The nuclear medicine division of the laboratory specializes in processing 18F-FDOPA PET images, developing methods necessary for dynamic analysis at the voxel level and studying the heterogeneity of dynamic behaviors within a tumor.
The LORIA laboratory is a joint research unit of the University of Lorraine, Inria and CNRS, in computer sciecne. The TANGRAM team specializes in computer vision and medical imaging, aiming at designing and developing methods towards image understanding and scene reconstruction, through physical and deep neural models.
A major theme of this PhD work is artificial intelligence applied to medical images. In this regard, the IADI and LORIA laboratories have demonstrated their competencies through numerous projects, with IADI possessing deep knowledge of PET and MRI images for use with deep learning, and LORIA offering a more fundamental understanding of the functioning of these models and the methods to apply, with national recognition in this field. Thereafter the PhD student will be co-supervised by researchers in these two labs.
The thesis supervisor, Pr Antoine VERGER, from IADI lab is also a medical practitioner at Nancy University Hospital. He maintains constant contact with clinical teams involved in the management of glioma patients (nuclear medicine physicians, neurosurgeons, neuro-oncologists, pathologists), enabling the PhD student to consult them with questions, discuss their expectations, or gain a better understanding of the problem.
The thesis co-supervisor, Erwan Kerrien, from LORIA lab, is an Inria researcher who specializes in computer vision and image processing, in particular for neuro-imaging. He will support the PhD student regarding computer science issues, discussing innovations in the computer vision field and connecting with the computerized medical imaging research community.
Mission confiée
Objectives
The research project aims to develop machine learning models based on learned features describing the complex relationships between voxels in 18F-FDOPA PET scans in gliomas.
1. Development of a self-supervised deep learning model to learn the representation of both healthy and pathological brains from multimodal PET and MRI images (WP1).
2. Identification of an aggressive subregion within a glioma using the previously constructed latent representation and MRI and PET images to assist in biopsy planning (WP2).
3. Development of a classification model for differential diagnosis between radionecrosis and true progression, with evaluation in a clinical routine context (WP3)., To develop our classification model, the model from WP1 can serve as an initialization with the advantage of converging to the solution with less data. The results obtained with this model will be compared to completely supervised CNN classification models trained from scratch and a model based on a radiomics pipeline with traditional machine learning algorithms. The model will be evaluated in terms of diagnostic performance, including precision, sensitivity, and specificity. The model must demonstrate good generalization performance on an external validation dataset. However, this external validation is only a first step; the crucial aspect is proving the added value of the model in a clinical routine context, which will be assessed by doctors from different hospitals. Explainability methods will be integrated into the model to increase its acceptability. Expected impact This project will have a scientific impact towards a more detailed understanding of
the presentation of gliomas and their evolution, as well as a practical impact on the management of patients with gliomas in clinical routine. Each WP will lead to the publication of a paper in an international journal with peer review
Principales activités
The candidate will pursue research activities in deep learning on PET and MRI 3D images. Besides deep neural network models and methods, more classical image processing activities such as image registration, image segmentation or more generally image manipulation will be required to preprocess and curate data. Experimentation, evaluation and validation will also have to leverage numerical data analysis tools to convey key insight and communicate main results to both clinicians and computer scientists involved in the project. Access to computing resources will be granted. Data will be provided by physicians from the nuclear medicine division who are European experts in their domain.
The candidate is expected to participate in meetings, report on his/her research, interact with other members of both Tangram and IADI teams, and in particular work in close collaboration with the involved clinicians and physicians to validate new ideas and ensure their clinical relevance.
Code d'emploi : Chirurgien (h/f)
Domaine professionnel actuel : Médecins Spécialistes et Chirurgiens
Niveau de formation : Bac+8
Temps partiel / Temps plein : Plein temps
Type de contrat : Contrat à durée indéterminée (CDI)
Compétences : Intelligence Artificielle, Analyse des Données, Réseaux de Neurones Artificiels, Vision par Ordinateur, C ++ (Langage de Programmation), Cluster Analysis, Analyse d'Image, Analyse Dynamique des Programmes, Image Registration, Python (Langage de Programmation), Machine Learning, Object Detection, Logiciel de Gestion des Patients, Tensorflow, 3-d Imaging, Pytorch, Deep Learning, Scikit-learn, Technologies Informatiques, Programming Languages, Anglais, Français, Sens de la Communication, Minutie ou Attention aux Détails, Axé sur le Succès, Esprit d'Équipe, Motivation Personnelle, Curiosité, Innovation, Recherche, Examen par les Pairs, Mathématiques Appliquées, Systèmes Automatisés, Imagerie Médicale, Biopsies, Travaux Cliniques, Aide à la Décision Clinique, Pratiques Cliniques, Recherche Clinique, Diagnostic Différentiel, Expérimentation, Manipulation Photographique, Traitement d'Image, Lesion, Imagerie par Résonance Magnétique (IRM), Médecine Nucléaire, Oncologie, Néoplasie, Systèmes Nerveux, Tomographie par Émission de Positrons, Procédures Invasives, Imagerie, Science des Données, Littérature
Courriel :
erwan.kerrien@inria.fr
Téléphone :
0139635511
Type d'annonceur : Employeur direct
