Principal investigators: Dr. Greg Stanisz and Dr. Colleen Bailey
Recently, the range of anti-cancer drugs and possible combinations with radiation and surgery has substantially increased the number of available treatments. Many are expensive and may be toxic, while their effectiveness remains uncertain. Techniques for identifying effective cancer treatments include genotyping, but the complexities of gene interactions and environmental influences make this difficult. Tumour volume measurements, obtained from computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound (US) have also been used, but they do not reflect heterogeneous responses and often occur weeks or months after commencing treatment.
Our aim is to find the combination of MRI parameters with the highest predictive power for early differentiation of apoptosis and necrosis from tumour progression in patients receiving therapy for brain and breast cancer. We will use three MRI techniques that measure tumour metabolism and microstructure:
chemical exchange saturation transfer (CEST)
magnetization transfer (MT)
Recently, we showed, in animal models, that CEST allows for non-invasive evaluation of tumour regions with high metabolic activity and can detect apoptosis. Encouraged by these results, we applied CEST in patients with brain metastases (BM) undergoing stereotactic radiosurgery (SRS). Our results indicated that we can separate responders from non-responders. We are extending this study to explore the prognostic potential of quantitative MRI in evaluating and predicting the clinical response of patients with breast cancer and brain metastasis and also in assessing their radiation-induced late effects.
The major goal of this project is to identify the optimal CEST, MT, and diffusion sequence parameters for predicting tumour response. We will achieve this goal via three aims:
Development of diffusion microstructure imaging
Optimization of the MRI protocol for patients with brain tumours and locally advanced breast cancer (LABC)
Assessment of tumour response to therapy and differentiation of responders from non-responders in patients undergoing chemotherapy or radiation treatment.
We hypothesize that quantitative MRI imaging will be able to predict the tumour response before treatment is administered. Moreover, we seek to confirm our preliminary studies that certain CEST features are capable of differentiating tumour necrosis from tumour progression soon after treatment. The results of this project will provide non-invasive biomarkers of tumour response to chemo- or radiation therapy and could be used to guide patient treatment.