Date de l'annonce : mercredi 20 janvier 2021

Intitulé du poste : 2021/01/06 – Post-doc in radiomics – Consortium HARMONY

Type de structure : HARMONY consortium : four teams from the West of France, Latim, iBrain, CRCINA, LARIS, working together to address the challenge of improving patients management through predictive models helping the identification of patients at risk before treatment and to validate the methodological developments across six different multicentre datasets and clinical contexts.
Responsible to: Mathieu HATT, Clovis TAUBER, Thomas CARLIER, Pierre CHAUVET

Contexte et mission : The HARMONY consortium is recruiting a post-doc fellow with expertise in radiomics, machine/deep learning and statistics.

Radiomics denotes the high throughput extraction of numerous quantitative metrics (including shape, intensity or textural features) of medical images with the goal of providing a full macroscopic phenotyping of tissues (tumors, organs, etc.) that could reflect at least in part the underlying pathophysiological processes (such as necrosis, proliferation, etc.), down to the genomic level [1]. Radiomics has shown promising results in identifying tumor subtypes, aggressiveness as well as in predicting response to therapy and outcome of patients in several cancers [2], however, most of these results have been obtained small, retrospective and monocentric cohorts. On the one hand, standardization was identified early on as a major limitation preventing radiomics to enter clinical practice, because of the lack of comparability of the results. No meta-analysis could be carried out, because each research group relied on different methodological workflows, software, nomenclature and implementation choices, and did not provide sufficient details for their work to be reproduced [3]. These issues have been addressed by the Imaging Biomarker Standardization Initiative (IBSI) [4]. On the other hand, it has been shown for PET [5]–[7], CT [8], [9] and MRI [10], [11] that most radiomic features exhibit moderate to high sensitivity to variability in scanner models, acquisition protocols and reconstruction settings, which constitutes the biggest challenge for multicentric studies [12].
OBJECTIVES
Our long term goal is to achieve societal impact by improving patients management. This will be achieved thanks to more robust and accurate predictive models that will help identify patients at risk before initiating treatment. In order for these tools to be exploited in the clinical routine a high level of proof is necessary, which in turn requires larger scale, multicentric (ideally prospective) studies regarding the use of radiomics and/or deep learning techniques relying on multimodal medical images, which are currently lacking. Our objectives are thus to develop harmonization techniques in both image and feature domains in order to improve, facilitate or even render feasible otherwise impossible radiomic analyses of large, multicentric, heterogeneous cohorts. In the present project, we aim at validating these methods in several applications across the consortium.

The consortium is recruiting a post-doc fellow that will join the group in which another post doc has already been working since August 2020.

References
[1] E. Segal, C. B. Sirlin, C. Ooi, A. S. Adler, J. Gollub, X. Chen, B. K. Chan, G. R. Matcuk, C. T. Barry, H. Y. Chang, and M. D. Kuo, “Decoding global gene expression programs in liver cancer by noninvasive imaging,” Nat Biotechnol, vol. 25, no. 6, pp. 675–80, Jun. 2007.
[2] M. Hatt, C. C. Le Rest, F. Tixier, B. Badic, U. Schick, and D. Visvikis, “Radiomics: Data Are Also Images,” J. Nucl. Med. Off. Publ. Soc. Nucl. Med., vol. 60, no. Suppl 2, pp. 38S-44S, Sep. 2019.
[3] M. Vallières, A. Zwanenburg, B. Badic, C. Cheze Le Rest, D. Visvikis, and M. Hatt, “Responsible Radiomics Research for Faster Clinical Translation,” J. Nucl. Med. Off. Publ. Soc. Nucl. Med., vol. 59, no. 2, pp. 189–193, 2018.
[4] A. Zwanenburg, M. Vallières, M. A. Abdalah, H. J. W. L. Aerts, V. Andrearczyk, A. Apte, S. Ashrafinia, S. Bakas, R. J. Beukinga, R. Boellaard, M. Bogowicz, L. Boldrini, I. Buvat, G. J. R. Cook, C. Davatzikos, A. Depeursinge, M.-C. Desseroit, N. Dinapoli, C. V. Dinh, S. Echegaray, I. El Naqa, A. Y. Fedorov, R. Gatta, R. J. Gillies, V. Goh, M. Götz, M. Guckenberger, S. M. Ha, M. Hatt, F. Isensee, P. Lambin, S. Leger, R. T. H. Leijenaar, J. Lenkowicz, F. Lippert, A. Losnegård, K. H. Maier-Hein, O. Morin, H. Müller, S. Napel, C. Nioche, F. Orlhac, S. Pati, E. A. G. Pfaehler, A. Rahmim, A. U. K. Rao, J. Scherer, M. M. Siddique, N. M. Sijtsema, J. Socarras Fernandez, E. Spezi, R. J. H. M. Steenbakkers, S. Tanadini-Lang, D. Thorwarth, E. G. C. Troost, T. Upadhaya, V. Valentini, L. V. van Dijk, J. van Griethuysen, F. H. P. van Velden, P. Whybra, C. Richter, and S. Löck, “The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping,” Radiology, p. 191145, Mar. 2020.
[5] P. E. Galavis, C. Hollensen, N. Jallow, B. Paliwal, and R. Jeraj, “Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters,” Acta Oncol, vol. 49, no. 7, pp. 1012–6, Oct. 2010.
[6] J. Yan, J. L. Chu-Shern, H. Y. Loi, L. K. Khor, A. K. Sinha, S. T. Quek, I. W. K. Tham, and D. Townsend, “Impact of Image Reconstruction Settings on Texture Features in 18F-FDG PET,” J Nucl Med, vol. 56, no. 11, pp. 1667–1673, Nov. 2015.
[7] E. Pfaehler, R. J. Beukinga, J. R. de Jong, R. H. J. A. Slart, C. H. Slump, R. A. J. O. Dierckx, and R. Boellaard, “Repeatability of 18 F-FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method,” Med. Phys., vol. 46, no. 2, pp. 665–678, Feb. 2019.
[8] D. Mackin, X. Fave, L. Zhang, D. Fried, J. Yang, B. Taylor, E. Rodriguez-Rivera, C. Dodge, A. K. Jones, and L. Court, “Measuring Computed Tomography Scanner Variability of Radiomics Features,” Invest. Radiol., vol. 50, no. 11, pp. 757–765, Nov. 2015.
[9] R. Berenguer, M. D. R. Pastor-Juan, J. Canales-Vázquez, M. Castro-García, M. V. Villas, F. Mansilla Legorburo, and S. Sabater, “Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters,” Radiology, vol. 288, no. 2, pp. 407–415, 2018.
[10] F. Yang, N. Dogan, R. Stoyanova, and J. C. Ford, “Evaluation of radiomic texture feature error due to MRI acquisition and reconstruction: A simulation study utilizing ground truth,” Phys. Medica PM Int. J. Devoted Appl. Phys. Med. Biol. Off. J. Ital. Assoc. Biomed. Phys. AIFB, vol. 50, pp. 26–36, Jun. 2018.
[11] H. Um, F. Tixier, D. Bermudez, J. O. Deasy, R. J. Young, and H. Veeraraghavan, “Impact of image preprocessing on the scanner dependence of multi-parametric MRI radiomic features and covariate shift in multi-institutional glioblastoma datasets,” Phys. Med. Biol., vol. 64, no. 16, p. 165011, Aug. 2019.
[12] M. Hatt, F. Lucia, U. Schick, and D. Visvikis, “Multicentric validation of radiomics findings: challenges and opportunities,” EBioMedicine, Aug. 2019.

Lieu : The recruited post-doc fellow will be based principally in Tours but will have the opportunity to work also in the 3 other labs during the project.

Rémunération : This position is for a period of 24 months approximately.

Diplômes requis : PhD with expertise in machine/deep learning for image analysis. Previous experience in radiomics and/or medical image analysis and cancer applications is a plus. Candidates that have not yet defended their PhD thesis will be considered as long as they have a defense date already scheduled

Compétences requises :

Contact : Applications containing a CV and a letter of motivation, should be sent to mathieu.hatt@inserm.fr.