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Annonce

27 mars 2020

Harmony: 2 post-doc fellows with expertise in radiomics, machine/deep learning and statistics


Catégorie : Post-doctorant


Develop image 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.

 

 

The project

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 (figure 1).

 

The consortium

M. Hatt (INSERM, Latim Brest) C. Tauber (U. Tours, INSERM), T. Carlier (CRCINA, CHU Nantes, INSERM), D. Mateus (Centrale Nantes, LS2N), P. Chauvet (U Angers LARIS)

 

Four teams from the West of France will be working together to address that challenge and to validate the methodological developments across six different multicentric datasets and clinical contexts:

The four teams in the consortium have extensive and complementary expertise in radiomics, medical imaging and image processing/analysis, cancer applications, machine/deep learning and statistics.

 

Access to a high performance computing platform (PLACIS, http://placis.univ-brest.fr/english) which is a hybrid cluster with 800 CPU cores and 50 GPUs dedicated to calculations, with a 150 TB storage facility, will be granted to all participants in the project.

 

The four teams in the consortium have extensive and complementary expertise in radiomics, medical imaging and image processing/analysis, cancer applications, machine/deep learning and statistics.

Access to a high performance computing platform (PLACIS, http://placis.univ-brest.fr/english) which is a hybrid cluster with 800 CPU cores and 50 GPUs dedicated to calculations, with a 150 TB storage facility, will be granted to all participants in the project.

 

Recruitment

The consortium is recruiting 2 post-doc fellows. The candidate must have a PhD with expertise in machine/deep learning, statistics and/or image analysis. Previous experience in radiomics and/or medical image analysis and cancer applications is a plus. Each position will be filled for approximately 2 years. Each of the recruited post-doc fellows will work in two different laboratories of the network on different datasets and clinical applications. The aim however is to have them collaborating hand-in-hand on the methodological developments in both image and feature domains. Thus they will be expected to spend a significant amount of time in all four laboratories over the whole duration of the project.

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

Candidates that have not yet defended their PhD thesis will be considered as long as they have a defense date already scheduled. Please not that given the current COVID-19 situation, the start date of the contracts cannot be specified yet.

 

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 Post docs positions offer – HARMONY consortium – 2020 – M. Hatt, C. Tauber, T. Carlier, P. Chauvet

[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.

 

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