Methodology

POPEYE aims to address state-of-the-art challenges by:

1. using pre-treatment datasets for improving patients’ selectivity based on radiomics extraction
2. developing image processing algorithms using Machine Learning (ML) techniques for improving accuracy in diagnostic data
3. optimizing the treatment plans of each individual patient, exploiting Monte Carlo (MC) simulations for accurate dosimetry assessment
4. developing a novel portable gamma camera, allowing bedside whole-body patient imaging
5. evaluating the developed open-software tools in clinical environment
6. applying socio-economic research to optimize the impact of the project results in European health care system.

Clinical data derived from diagnostic (⁶⁸Ga) and therapy (¹⁷⁷Lu) procedures are used to optimize the quantification on SPECT/PET acquisitions and to accurately extract tumor radiomics. MC simulations incorporating ML techniques serve as gold standard and allows to estimate dosimetry on personalized treatment protocols. A well-established management & dissemination plan in combination with an extensive research on economic, ethical, legal and social aspects will allow to achieve POPEYE’s final goal; the clinical evaluation and exploitation of the proposed software & hardware tools, as a support guidance to the clinicians, to assess personalized MNETS diagnosis and therapy protocols.

5 Working Packages are connected towards this effort.