Context and objectives: Personalized medicine is one of the major goals of oncology.
With the help of technological breakthroughs, the fine characterization of tumors has led to the identification of diagnostic biomarkers, which are prognostic of survival or predictive of the response to therapeutic agents. PET/CT imaging is an integral part of this approach by enabling the distribution and accessibility of biomarkers expressed by tumor or microenvironment in vivo in a non-invasive way. To date, numerous studies explored the potential value of textural features in PET imaging with encouraging results in a number of cancers [1, 2]. However, only a limited number adopted rigorous methodological choices with in particular large cohorts of patients and robust statistical analysis [3, 4]. Keeping in mind these limitations, the evidence supporting the additional value of advanced image features from FDG-PET continues to expand year after year. Several of the most recent studies have used techniques such as external cohort validation [5, 6], and machine(deep)-learning technique [7, 8] and concluded in the usefulness of textural analysis regarding patient management.
Our team developed an approach based on Random Survival Forest  in the context of patient suffering from multiple myeloma using PET imaging at baseline. The aim of this post-doctoral position will be to enhance this methodology taking into account unbalanced data, right censoring, boosting… and to assess the benefits of fractal analysis as an alternative textural features. A second aim will be to improve the interpretability of RSFs by studying the importance of the most predictive features for very high dimensional and correlated data [10, 11]. The candidate will apply the development within the context of several large multicentric, prospective studies including IMAJEM fo MM patients , LYMA for mantle cell lymphoma  and GAINED for DLBCL (https://clinicaltrials.gov/ct2/ show/NCT01659099).
This position is funded by a large project called SIRIC (ILIAD Imaging and Longitudinal Investigations to Ameliorate Decision making in multiple myeloma and breast cancer) involving several teams (from biology to applied mathematics) in Nantes. This project is conducted in strong partnership with the Numerical Science Laboratory of Nantes and will be associated with the work done by a current PhD student.
Requirements: • Education: The candidate must hold a PhD in Physics, Computer Science or Applied Mathematics • Programming Skills: Python, R
- University Hospital of Nantes, Nuclear Medicine Department / French Institute of Health and Medical Research (INSERM, CRCINA, Nuclear Oncology Team, UMR1232), Nantes, France
- Ecole Centrale Nantes, LS2N, CNRS UMR 6004, Nantes, France Supervision:
- Dr Thomas Carlier (email@example.com)
Pr Diana Mateus (firstname.lastname@example.org)
Duration: 2 years