Many publications presenting findings resulting from the Euradiomics project were published in peer reviewed scientific journals:  

Julien Guiot et al. Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19. Diagnostics (2021)

Zerka et al. Blockchain for Privacy Preserving and Trustworthy Distributed Machine Learning in Multicentric Medical Imaging (C-DistriM). IEEE (2020)  

Zerka et al., Systemic review of privacy preserving distributed machine learning from federated databases in health care. JCO Clinical Cancer Informatics (2020).  

Bogowicz et al., Privacy-preserving distributed learning of radiomics to predict overall survival and HPV status in head and neck cancer. Scientific reports (2020) 

de Jong et al., Can radiomics help to predict skeletal muscle response to chemotherapy in stage IV non-small cell lung cancer?. European Journal of Cancer (2020) 

Ankolekar at al., Development and validation of a patient decision aid for prostate Cancer therapy: from paternalistic towards participative shared decision making. BMC Medical Informatics and Decision Making (2019)

Peerlings et al., Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial. Scientific reports (2019) 

Petersen et al., Improving Decision Making in Larynx Cancer by Developing a Decision Aid: A Mixed Methods Approach. The Laryngoscope (2019) 

van Timmeren et al. Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics. PLoS ONE (2019)  

Leijenaar et al., Development and validation of a radiomic signature to predict HPV (p16) status from standard CT imaging: a multicenter study. British Journal of Radiology (2018) 

Jochems et al., Combining deep learning and radiomics to predict HPV status in oropharyngeal squamous cell carcinoma. ESTRO 37 conference abstract (PO-0932) (2018)