The AI.DReAM project, funded by BPI France, brings together a consortium of 9 partners (GE Healthcare, 4 SMEs and start-ups, 3 clinical partners, and LITO as the only academic laboratory). The project aims to accelerate the development and market access of Artificial Intelligence applications in medical imaging. The role of our laboratory is to carry out the necessary methodological developments to ensure the quality control, the robustness of the radiomic models (classical or deep) and their ability to produce reliable results on a wide variety of images. To evaluate our approaches, we will work with the clinical partners of the consortium, which are the AP-HP, Gustave Roussy, and the Hôpital Saint Joseph in Paris.
People involved in the lab : Nicolas Captier, Fanny Orlhac, Irène Buvat (PI for the lab).
Radiomic and artificial intelligence models will be easier to apply in a clinical context if they are explainable and provide an estimate of the confidence associated with the result they produce. We are therefore working on the development of interpretable radiomic models, which can be designed from a limited amount of data (from less than a hundred patients), with the aim of highlighting biological mechanisms from the models and/or verifying the results produced by the models. In particular, we are developing these methods in the context of patients treated by radiotherapy to determine whether the identification of subregions responsible for resistance to treatment or recurrence would allow the dose delivery plan to be modified to combat these poor outcomes.
People involved in the laboratory: Fanny Orlhac, Irène Buvat, Frédérique Frouin, Christophe Nioche, Thibault Escobar (PhD student), Hamid Mammar, Laurence Champion, Romain-David Seban, Claire Provost
This work is supported by Dosisoft.
The primary objective of the BIOMEDE-IA project (2020-2023) is to predict by machine learning methods the main genomic mutations of patients with diffuse intrinsic pontine gliomas from their clinical and imaging (multi-parametric MRI) features, to manage the cases for which biopsy is not available.
The TIPIT project (2020-2023), who involves U900 Inserm - Institut Curie - PSL (Head : Emmanuel Barillot), the Department of thoracic oncology of Institut Curie (Head : Nicolas Girard) and our lab is funded by the ARC fondation, as part of the SIGNIT call, for 3 years.
The aim of the PANACEE project (2020-2023) is to develop methods and a tool that will make it possible, for a patient with non small cell lung cancer described by his or her clinical, biological, histological or medical images characteristics (radiomics), to identify a small group of patients with very similar characteristics, in a reference database consisting of patients already treated for the same pathology.
Positron Emission Tomography is well-established in the diagnosis and treatment monitoring of Hodgkin‘s lymphoma (HL). HL is a type of tumour that can be characterized by its sugar consumption. Thus, PET enables early monitoring of treatment response, that is shows if the lymphoma shrinks or grows, reflecting whether the therapy is working or not working . So far, only very basic information of the acquired PET images is used. Since the biology of HL is linked to its metabolism, we aim to analyze PET images in more detail by using artificial intelligence algorithms.
The PRECISION PREDICT project (2020-2022) is led by Institut Curie (Thoracic Oncology Department, Data Department, and LITO) and funded as part of the Health Data Hub call related to the "Improvement of medical diagnosis through the use of Artificial Intelligence".
In close collaboration with radiation oncologists, we are exploring the contribution of radiomics in the context of the planning and follow-up of patients treated by radiotherapy and proton therapy. In particular, we are trying to predict the occurrence of recurrence, and their location, so as to be able to modify the treatment plans to avoid them or reduce their probability of occurrence.
People involved: Arnaud Beddok (MD, PhD student in Sciences), Valentin Calugaru, Gilles Créhange, Irène Buvat
We are interested in whole-body radiomics for stratification of patients with different types of lymphoma using Fluorodeoxyglucose PET/CT scans. In particular, we have contributed to the validation of the prognostic value of a simple radiomic biomarker, the total metabolically active tumour volume (TMTV). In addition, we introduced a completely original biomarker that reflects the dissemination of the disease, and showed not only its prognostic value, but also its complementarity with TMTV. We are validating methods for the automatic calculation of these prognostic biomarkers and are seeking to complete them in order to improve the quality of the stratification.
People involved in the laboratory: Anne-Ségolène Cottereau (MD, PhD student in science), Louis Rebaud (PhD student), Kibrom Girum (post-doctoral student), Christophe Nioche, Irène Buvat
The work is partly supported by Siemens Healthineers and by the EU as part of the HOLY2020 project.
Contribution of MRI radiomic analysis for the prediction of pathological Complete Response in breast cancer treated by neoadjuvant chemotherapy
The goal of the NeoTex project is to improve the early prediction of pathological complete response to neoadjuvant chemotherapy in patients with locally advanced or aggressive breast cancer and to propose new biomarkers of response. Features extracted from MRI scans performed at diagnosis and at mid-term are taken into account for the definition of predictive models, combining qualitative (BI-RADS criteria), quantitative (radiomic features), clinical and histopathological data.
People involved in the laboratory: Caroline Malhaire, Marie-Judith Saint-Martin (PhD student), Fanny Orlhac, Hervé Brisse, Frédérique Frouin
VOCALE is a project (2018-2022) dedicated to motion analysis of vocal folds using dynamic translaryngeal ultrasound. It is driven by the laboratory in collaboration with surgical departments and the "Laboratoire d’Imagerie Biomédicale".
We are part of the H2020-MSCA-ITN-2017 HYBRID (Healthcare Yearns for Bright Researchers for Imaging Data, 2017-2021) project in collaboration with 10 partners from Germany, Austria, United Kingdom, the Netherlands, Danemark, and Belgium.
Photo Dynamic Therapy (PDT) is a localized treatment using a laser beam focused on the tumor. The PDT is based on the activation by specific wavelength light of a photosensitizer (PS) localized preferentially in tumor cells.
LIFEx is a user-friendly image processing software, making it possible for colleagues without any programming skills to perform radiomic studies using any type of medical images, including PET, MR, CT, SPECT or US images.