In medical imaging, radiomic features make it possible to characterize heterogeneity of a region of interest at the anatomical level. This way of quantifying the heterogeneity of a region of interest can be useful, for example, in order to identify the more aggressive tumors in oncology. To do this, we hypothesize here that variation in magnetic resonance imaging (MRI) acquisition sequences and its resulting different levels of contrast would make it possible to optimize the subsequent radiomic analysis.
In this project, a pipeline for the analysis of real medical images will first be set up in order to quantify the robustness of radiomic characteristics according to variations in acquisition protocols. Then, an MRI acquisition simulation pipeline will be developed in order to evaluate the potential for optimizing radiomic features in medicine.
 

The PARTAGE project (Patients and Researchers Team-up and Generate Evidence) is a research project that is examining mechanisms to allow patients to securely share their clinical data with researchers using the Opal patient portal. As part of the overall PARTAGE project, this specific sub-project is about obtaining stakeholder feedback on the concept of data-sharing. To do so, we are using a process of stakeholder co-design in which patients, clinicians and researchers are embedded in the research team and we are obtaining additional feedback from each stakeholder group through focus groups and surveys.

In diagnostic radiology, the use of ionizing radiation is justified by benefits surpassing risks. From an epidemiological perspective, this balance is difficult to assess because accurate dose values for individuals are not available. This project consists in developing tools to automatically report dose-to-organs from Computed Tomography (CT) images. First, a machine-learning based, multiclass segmentation tool will be developed to automatically contour organs in CT imaging studies. Then, a fast GPU-based Monte Carlo code will be used to compute dose maps from technical scanning parameters stores in DICOM headers of medical images. A large database of dose-to-organ values will be constituted as well as interactive dashboards to explore dose usage as a function of site explored, device used, etc.

On the long term, this database will be linked with epidemiological cancer data to assess potential causal relations.

High-dose-rate (HDR) brachytherapy is a standard treatment modality to treat cancer (e.g., prostate and cervical cancer) using the ionizing radiation of a small encapsulated radioactive source. The curative aim in the clinic is to create treatment plans that maximize the dose to the tumor while minimizing the dose to normal tissues. When it comes to the treatment plan generation, manual fine tuning of an objective function is necessary to achieve optimal trade-offs between these two conflicting objectives. Therefore, the plan generation is a time-consuming iterative task for practitioners; the plan quality can be dependent on the user skills.

The purpose of the project is to implement efficient optimization algorithms on GPU that can generate thousands of alternative plans with optimal trade-offs (Pareto-optimal plans) within seconds. Using real-time plan navigation tools, the user can quickly explore the trade-offs through the set of Pareto-optimal plans and select the best plan for the patient at hand. The impact of these novel optimization approaches is quantified and compared to the standard clinical approach. 

The project consists in determining and exploring the possibilities offered by dynamic dashboards in a medical context as well as the associated data management structures. The project therefore considers several aspects of data management. In this sense, the considerations related to DICOM data transfers as well as different approaches to their management and conservation are considered. In addition, the dashboards will be designed to ensure an effective, clear and concise presentation with recognized visualization tools. Different additions will be made to the different portions of the project during its implementation depending on the direction taken by the research and the needs of health professionals. Particular emphasis is placed on compliance with FAIR principles by the resulting system.