• Assessment of hypoxia and oxidative-related changes in a lung-derived brain metastasis model by [64Cu][Cu(ATSM)] PET and proteomic studies. Fantin J., Toutain J., Peres E., Bernay B., Mehani S., Helaine C., Bourgeois M., Brunaud C., Chazalviel L., Pontin J., Corroye Dumont A., Valable S., Chérel M., Bernaudin M. EJNMMI Res 2023; 13(1). https://doi.org/10.1186/s13550-023-01052-8

  • Graph-based multimodal multi-lesion DLBCL treatment response prediction from PET images. Thiery O, Rizkallah M, Bailly C, Bodet-Milin C, Itti E, Casasnovas RO, and al. arXiv; 2023. http://arxiv.org/abs/2310.16863

  • Performance of baseline FDG-PET/CT radiomics for prediction of bone marrow minimal residual disease status in the LyMa-101 trial. Bodet-Milin C, Morvant C, Carlier T, Frecon G, Tournilhac O, Safar V, and al. Sci Rep. 24 oct 2023;13(1):18177. https://doi.org/10.1038/s41598-023-45215-y

  • Hybrid simultaneous whole-body 2-[18F]FDG-PET/MRI imaging in newly diagnosed multiple myeloma: first diagnostic performance and clinical added value results. Jamet B, Carlier T, Bailly C, Bodet-Milin C, Monnet A, Frampas E, and al. Eur Radiol. sept 2023;33(9):6438 47. https://doi.org/10.1007/s00330-023-09593-1

  • Design of a generic method for single dual-tracer PET imaging acquisition in clinical routine. Taheri N, Le Crom B, Bouillot C, Chérel M, Costes N, Gouard S, and al. Phys Med Biol. 10 apr 2023;68(8). https://doi.org/10.1088/1361-6560/acc723

  • A Multicenter Study on Observed Discrepancies Between Vendor-Stated and PET-Measured 90Y Activities for Both Glass and Resin Microsphere Devices, Gnesin S, Mikell JK, Conti M, Prior JO, Carlier T, Lima TVM, and al. . J Nucl Med. may 2023;64(5):825‑8. https://doi.org/10.2967/jnumed.122.264458

  • Preclinical Evaluation of a 64Cu-Based Theranostic Approach in a Murine Model of Multiple Myeloma, Métivier C, Le Saëc P, Gaschet J, Chauvet C, Marionneau-Lambot S, Hofgaard PO, et al., Pharmaceutics, 25 juin 2023;15(7):1817. https://doi.org/10.3390/pharmaceutics15071817

  • 18F-FDG-Based Radiomics and Machine Learning. Godefroy T, Frécon G, Asquier-Khati A, Mateus D, Lecomte R, Rizkallah M, et al., JACC: Cardiovascular Imaging. avr 2023;S1936878X23000931. https://doi.org/10.1016/j.jcmg.2023.01.020

  • 18ffdg-based radiomics and machine learning: a useful help for aortic prosthetic valve infective endocarditis diagnosis?, Thomas Godefroy, Gauthier Frécon, Antoine Asquier-Khati, Diana Mateus, Raphael Lecomte, Mira Rizkallah, Nicolas Piriou, Thomas Le Tourneau, David Boutoille, Thomas Eugene and Thomas Carlier, European Heart Journal, 43(Supplement 2):ehac544–318, 2022
    http://dx.doi.org/10.1093/eurheartj/ehac544.318
  • F-18-fdg pet/ct at baseline in dlbcl patients enrolled in the gained protocol: A machine learning study to assess the usefulness of combining clinical, conventional and radiomics features to predict the 2-years pfs and survival
    Thomas Carlier, Gauthier Frecon, Diana Mateus, Mira Rizkallah, Francoise Kraeber-Bodéré, S Kanoun, P Blanc-Durand, E Itti, S Le Gouill, R Casasnovas, and others
    European Journal Of Nuclear Medicine And Molecular Imaging, Annual Congress of the European Association of Nuclear Medicine, Barcelona (Spain), October 15-19, 2022