EURAMED has just launched its first Working Group dedicated to “Advancing precision, effectiveness and safety in oncologic care by imaging and Artificial Intelligence”. The WG will be lead by Prof. John Damilakis, Past President of EURAMED.
We kindly invite a) experts in medical radiation protection and/or applications of X-ray imaging in medicine with proven track record in AI, b) AI specialists and c) oncologists to become members in this WG.
If you are interested in joining the WG, please send a short CV with the track record of 5 recent activities related to the topic of the WG to the EURAMED Office (firstname.lastname@example.org) before 30 July 2021.
We look forward to hearing from you!
Details on the WG are provided in the following:
Scientific background and main aims: Several methods for the evaluation of cancer therapy response have been developed using CT, MR, PET/CT or PET/MR imaging. They are based on 2D measurements and have inherent limitations. For example, the slices used to assess therapy response are only surrogates of overall tumour volume. Tumour segmentation using machine learning methods is an important research area. These methods can support the evaluation of cancer therapy response by providing information based on 3D rather than 2D imaging.
The increased frequency of imaging required before, during and after teletherapy could result in an increase of radiation dose to organ and tissues of the patient due to imaging beyond the levels expected from radiotherapy itself. Therefore, optimised protocols are needed for X-ray imaging procedures performed on cancer patients. Machine learning models can be developed to optimise these protocols. Innovations in this field could provide progress not only in the field of oncology but in the field of dose optimization, in general.
Activities for the coming year: Creation of the team, prioritisation of topics of interest, literature search, creation of work plan.
Working procedures: Bi-monthly online meetings, electronic communication (emails)