Overview GRayLines R&D Program
Mission and vision
The mission of the department of Radiation Oncology is ‘High precision, Innovation and Healthy Ageing for Cancer Survivors’.
To maximize healthy ageing of cancer survivors we strive to cure cancer patients, while minimizing side effects and improving the quality of life. To achieve this, we optimize radiation treatments by achieving high dose delivery to the target, thereby maximizing local tumor control, while minimizing the integral dose to normal tissue (high precision). To further optimize treatment and promote healthy ageing of cancer survivors, we focus on innovation to support the development, validation and safe implementation of novel radiation technologies (including proton therapy), through traditional and alternative evidence-based methods.
Following this mission, our vision and research focus is to predict, prevent and treat toxicity.
The Model-Based Approach
Our research strategy is based on the so-called model-based approach (MBA), invented by the department of Radiation Oncology at the UMCG. The MBA is an evidence-based method based on multifactorial normal tissue complication probability (NTCP) models. It can be used for the optimisation and validation of novel radiation technologies and approaches, mainly aimed at reducing radiation-induced toxicity.
The overall concept of the model-based approach relies on the principle that clinical superiority of treatment is mainly determined by optimal dose delivery: a higher conformity of the therapeutic dose to the target, with less dose to surrounding organs and healthy tissue.
Whether a reduction in dose will lead to a clinically relevant improved outcome will depend on multiple factors, including both dosimetric features and non-dosimetric features such as age or concomitant chemotherapy. The MBA includes these factors in multivariable models to relate dose to the normal tissue complication probability (NTCP).
The challenge now is to validate the NTCP-models in clinical practice. For this purpose, a Rapid Learning Health Care (RPHC) system has been designed.
Rapid Learning Health Care
The Rapid Learning Health Care system (RLHC) is basically a Plan-Do-Control-Act cycle embedding the MBA, supporting the continuous evaluation, optimisation and implementation of the NTCP-models. The starting point of the MBA and RLHC system is a comprehensive prospective data registry, in which the department collects all patient- and treatment related data.
This is the basis to develop the NCTP-model (MBA-step 1), perform dose optimisation in treatments plans for either photon or proton treatment (MBA-step 2) and translate the dose reduction to a predicted clinical benefit to support decision making (MBA-step 3). By comparing the observed toxicity with the predicted optimized treatment plan, the validity of the model can be assessed. This may lead to adjustments/improvements in the model.
Additional research is required to not only relate dose distribution to normal tissue complication risk, but also add additional information from for example imaging biomarkers, to be able to generate full prediction profiles, including complication risk, quality of life and life expectancy. It is our ambition to develop novel methods for building extended models that enable the full assessment of different radiotherapeutic technologies.
Our three central research themes are ‘Normal Tissue Damage’, ‘Applied Imaging’ and ‘Proton Therapy’. While these research themes are on different levels, they are overarching to all research programs and strongly integrated. Each research theme consists of multiple research lines and projects.
We have clustered the different tumor sites into an integrative research program (brain, head and neck / thorax / abdomen, pelvis and others). This allows us to study coherence and interrelations of different toxicities within the cluster area. Within each of these research programs a RLHC-system has been set-up, including a uniform prospective data registry, which will support the development and validation of NTCP-models.