Neuroendocrine neoplasms (NENs) are a heterogeneous group of malignancies which arise from neuroendocrine cells and are divided into well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). Currently, 18F-FDG-PET is the preferred choice for NECs, whereas 68Ga-DOTA-peptides PET is useful for the characterization of NETs.
The potential of radiomics has already been demonstrated in 18F-FDG-PET, but this is currently lacking for 68Ga-DOTA-peptides PET images. This research line is focused on the development of an accurate and robust segmentation algorithm for NETs and the exploration of potentials and pitfalls associated with a radiomics model on 68Ga-DOTA-TOC PET/CT images.
Philippe Thuillier,Virginia Liberini,Osvaldo Rampado, Elena Gallio,Bruno De Santi, Francesco Ceci, Jasna Metovic, Mauro Papotti, Marco Volante, Filippo Molinari, Désirée Deandreis
Virginia Liberini, Bruno De Santi, Osvaldo Rampado, Elena Gallio, Beatrice Dionisi, Francesco Ceci, Giulia Polverari, Philippe Thuillier, Filippo Molinari & Désirée Deandreis
European Journal of Nuclear Medicine and Molecular Imaging Physics