Advanced radiotherapy planning based on probabilistic concepts for photon and proton therapy

The purpose of this project is to develop and test advanced radiotherapy planning techniques based on probabilistic concepts. The project aims to explicitly account for delineation uncertainties, uncertainties related to intra-/inter-fraction motion and deformation of the targets and organs at risk. For proton therapy, range uncertainties and relative biological effectiveness uncertainties will be addressed.

Five publications and multiple presentations will result from this project. It is expected that the resulting evidence collected after finishing this project will start the paradigm shift from margin-based planning to advanced planning based on probabilistic concepts.

Find out more on the University of Manchester’s website. Deadline: Friday 17 November 2017.


Using advanced MR imaging to select the best treatment for men with high risk prostate cancer

This project will collect a uniquely detailed dataset with mpMRI at each radiotherapy session, utilising the MRI-linac, for 50 patients with high-risk prostate cancer. The MRI-linac allows the mpMRI to be acquired during standard treatment enabling this data collection. mpMRI will be designed to provide comprehensive information of a patient’s disease. Due to ADT and radiotherapy homogenising the signal in the prostate we will use a radiomics feature analysis approach. This approach has shown the feasibility of radiomics and mpMRI to distinguish cancerous and healthy tissue in the prostate. This high temporal resolution dataset will be used to investigate changes in radiomic features during treatment.

Changes in image features, with patient demographics, will be used to train a supervised machine learning algorithm. This approach will derive a radiomics signature, as a function of features seen pre-ADT or pre-radiotherapy. Therefore, allowing our approach to be applicable for all men with high-risk prostate cancer as part of their standard diagnostic pathway.

Find out more on the University of Manchester’s website. Deadline: Friday 17 November 2017.