Postdoctoral fellow : Head and Neck radiotherapy dose painting from mpMRI-based tumor habitat maps (M/F)

16 Jan 2024
Job Information

Organisation/Company

CNRS

Department

Centre de Recherche En Acquisition et Traitement de l'Image pour la Santé

Research Field

Pharmacological sciences
Engineering
Technology

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

5 Feb 2024 - 23:59 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

35

Offer Starting Date

1 Mar 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

Cancer 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 [1]. 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 [2]. In the literature, however, teams have sought to use proton density to calculate dose directly from MRI images [3].
In the Radiology Department of center Leon Berard, we have developed at 3.0T (Siemens Vida MR system) a MRI acquisition protocol including a specific coils setup and patient table to host the patient with the immobilizer mask. This protocol is running and aims to enroll 200 patients. It includes several MRI pulse sequences such as morphological acquisition for the OAR and target volume contouring, and other sequences for multiparametric quantitative MRI (mpq-MRI) purpose, including oxygenation mapping.

This 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 [4]. 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 sub-regions such as necrosis, viable and proliferating tissue, and hypoxia region. Thus, the main objectives of this post-doc will be:
- To extract from the microenvironment maps of the Mpq-MRI hypoxic volumes on which a dose escalation will be performed [4-6];
- To 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.
- To explore further the possibility of dose escalation by using protons rather than photons to perform the plans [7].
- To 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;
- To propose a new robust method for performing treatment planning directly on MRI imaging, based on the proton density.

Locations
In Lyon at the comprehensive cancer center Léon Berard in both diagnostic imaging and radiotherapy departments as well as on campus LyonTech-La Doua at CREATIS.

Partnership
This project, funded by the National Research Agency for 4 years from January 2023, is carried out in partnership with CREATIS, the radiology and radiotherapy departments of the Center Leon Berard and the company Therapanacea. An additional support will be bring by the LabEx PRIMES (https://primes.universite-lyon.fr/ )

Requirements

Research Field

Pharmacological sciences

Education Level

PhD or equivalent

Research Field

Engineering

Education Level

PhD or equivalent

Research Field

Technology

Education Level

PhD or equivalent

Languages

FRENCH

Level

Basic

Research Field

Pharmacological sciences

Years of Research Experience

None

Research Field

Engineering

Years of Research Experience

None

Research Field

Technology

Years of Research Experience

None

Additional Information
Eligibility criteria

• Physicist with a PhD thesis in medical physics.
• Knowledge about medical imaging and machine learning would be a plus.
• Good practice and knowledge of programming or prototyping softwares
• Willingness to get involved in the medical field and to work in an interdisciplinary environment
• Autonomy, dynamism
• Good oral and written level in English

Additional comments

Key words: Medical physics, Medical imaging, Dose painting, MRI Radiotherapy planning, Quantitative multi-parametric MRI, Head and Neck cancer

References:
[1] Hansen CR, Friborg J, Jensen K et al. NTCP model validation method for DAHANCA patient selection of protons versus photons in head and neck cancer radiotherapy. Acta Oncol. 2019 Oct;58(10):1410-1415. doi: 10.1080/0284186X.2019.1654129. Epub 2019 Aug 21.
[2] Boulanger M, Nunes JC, Chourak H et al. Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review. Phys Med. 2021 Sep;89:265-281. doi: 10.1016/j.ejmp.2021.07.027. Epub 2021 Aug 30.
[3] Demol B, Viard R, Reynaert N. Monte Carlo calculation based on hydrogen composition of the tissue for MV photon radiotherapy. J Appl Clin Med Phys. 2015 Sep 8;16(5):117–130. doi: 10.1120/jacmp.v16i5.5586.
[4] Gouel P, Callonnec F, Obongo-Anga FR et al. Quantitative MRI to Characterize Hypoxic Tumors in Comparison to FMISO PET/CT for Radiotherapy in Oropharynx Cancers. Cancers (Basel). 2023 Mar 22;15(6):1918. doi: 10.3390/cancers15061918.
[5] Skjøtskift T, Evensen ME, Furre T et al. Dose painting for re-irradiation of head and neck cancer. Acta Oncol. 2018 Dec;57(12):1693-1699. doi: 10.1080/0284186X.2018.1512753. Epub 2018 Oct 3.
[6] Grönlund E, Almhagen E, Johansson S et al. Robust maximization of tumor control probability for radicality constrained radiotherapy dose painting by numbers of head and neck cancer. Phys Imaging Radiat Oncol. 2019 Dec 9;12:56-62. doi: 10.1016/j.phro.2019.11.004. eCollection 2019 Oct.
[7] Håkansson K, Smulders B, Specht L et al. Radiation dose-painting with protons vs. photons for head-and-neck cancer. Acta Oncol. 2020 May;59(5):525-533. doi: 10.1080/0284186X.2020.1714720. Epub 2020 Jan 21.

Website for additional job details

https://emploi.cnrs.fr/Offres/CDD/UMR5220-MARMOR-005/Default.aspx

Work Location(s)

Number of offers available

1

Company/Institute

Centre de Recherche En Acquisition et Traitement de l'Image pour la Santé

Country

France

City

LYON

Geofield

Where to apply

Website

https://emploi.cnrs.fr/Candidat/Offre/UMR5220-MARMOR-005/Candidater.aspx

Contact

City

LYON

Website

http://www.creatis.insa-lyon.fr/site/

STATUS: EXPIRED