Head and Neck radiotherapy dose painting from mpMRI‐based tumor habitat maps

h3 Head and Neck Radiotherapy Dose Painting from mpMRI‐Based Tumor Habitat Maps /h3pstrongScientific Context /strong /ppCancer is the leading cause of mortality and morbidity globally. Radiation therapy is used in more than 50% of patients treated for cancer. Head and neck (HN) tumors have the sixth most frequent cancer prevalence and are often associated with poor outcomes and lifelong sequels. Radiation therapy is generally performed using intensity modulated techniques with photon beams. It is particularly challenging due to anatomical complexity and physiological functions associated with the organs at risk (OAR). At the same time, proton therapy has the potential to further reduce the toxicity of the treatment, but is much less accessible. Treatment planning is routinely performed on CT imaging since accurate dose calculation requires the conversion of Hounsfield unit values (HU) of the CT to relative electron density (ED) or stopping power, for photons and protons beams, respectively. However, CT imaging fails to provide functional information regarding tumor infiltration. MR imaging (MRI) has the potential to improve efficiency of treatment due to its unique ability for better tissue visualization of organs at risk and tumor physiological characterization. Until now, the only way to calculate the dose on MRI is to generate a pseudo‐CT or a synthetic CT via different methods such as bulk density assignment (BDM), atlas‐based and deep learning (DL) methods. In the literature, however, teams have sought to use proton density to calculate dose directly from MRI images. /ppstrongObjectives /strong /ppThis project aims to propose a dose distribution planning solution entirely based on MRI. Particularly, we will exploit the additional potential of mpq‐MRI for dose calculation accounting for the spatial biology of the tumor. Mpq‐MRI is used to build a hydrogen concentration map for dose calculation and a tumor microenvironment map for dose painting. The proposed tumor microenvironment map is built from the multispectral information provided by oxygenation and diffusion mapping. It allows identifying tumor subregions such as necrosis, viable and proliferating tissue, and hypoxia region. Thus, the main objectives of this post‐doc will be: /polliTo extract hypoxic volumes from the microenvironment maps of the Mpq‐MRI on which a dose escalation will be performed; /liliTo perform CT/MRI registration and propagate the contours on the CT in order to perform a new treatment plan with photons using volumetric modulated arc therapy technique (VMAT). These plans will be compared to the clinical plans; /liliTo explore further the possibility of dose escalation by using protons rather than photons to perform the plans; /liliTo evaluate the accuracy of dose calculation on MRI images using the DAM method (or other available DL method at CLB) against dose calculation on the planning CT; /liliTo propose a new robust method for performing treatment planning directly on MRI imaging, based on the proton density. /li /olpstrongProfile and Skills of the Candidate /strong /pulliPhysicist with a PhD thesis in medical physics. /liliKnowledge about medical imaging and machine learning would be a plus. /liliGood practice and knowledge of programming or prototyping software. /liliWillingness to get involved in the medical field and to work in an interdisciplinary environment. /liliGood oral and written level in English. /li /ulpstrongKey Words: /strong Medical physics, Medical imaging, Dose painting, MRI Radiotherapy planning, Quantitative multiparametric MRI, Head and Neck cancer. /p #J-18808-Ljbffr