The main field of Prof. Arturo Chiti interest and research is oncology, as witnessed by his track of clinical and research activities. He has extensive experience in leading clinical, educational and research projects, and also scientific associations and committees. He has broad knowledge in imaging techniques, both radiological and nuclear medicine. In recent years radiomics, mining of parameters derived from the texture analysis of medical images, and machine learning techniques (e.g. convolutional neural networks) have risen a great attention in medical imaging research field. Prof. Chiti contributed to the field coordinating projects dealing with solid cancers, in particular lung cancer. Currently, he leads a funded research project on lung cancer using a radiogenomics approach (AIRC IG 2016 – 18585) and a project aimed at the baseline risk assessment and staging of cancer patients using deep learning. During his scientific track of activities, he focused his efforts on the standardisation of imaging procedures in order to ensure reliable image derived parameters to be included in trials and clinics as imaging biomarkers. He contributed to numerous guidelines and standardisation initiatives. Radiation treatment plan relies on image acquisition, interpretation and delineation. Prof. Chiti collaborated with the European Radiation Oncology Society (ESTRO) and research groups to promote the value of medical imaging in the radiation treatment in different cancer types. At the beginning of his career Prof. Chiti dedicated most of his scientific activities to molecular imaging of neuroendocrine neoplasms, firstly using 111-In-pentreotide scintigraphy and then using 68-Ga labeled peptides and 18-F-DOPA PET/CT.