Cancer tissue classification from DCE-MRI data using pattern recognition techniques

Cancer research has significantly advanced in recent years mainly through developments in medical genomics and bioinformatics. It is expected that such approaches will result in more durable tumor control and fewer side effects compared with conventional treatments such as radiotherapy or chemotherapy. From the imaging standpoint, non-invasive imaging biomarkers (IBs) that assess angiogenic response and tumor environment at an early stage of therapy are of utmost importance since they could provide useful insights into therapy planning. However, the extraction of IBs is still an open problem since there are no standardized imaging protocols yet or established methods for the robust extraction of IBs. DCE-MRI is amongst the most promising non-invasive functional imaging modalities while compartmental pharmacokinetic (PK) modeling is the most common technique used for DCE-MRI data analysis. However, PK models suffer from a number of limitations such as modeling complexity, which often leads to variability in the computed biomarkers. To address these problems, alternative DCE-MRI biomarker extraction strategies coupled with a profound understanding of the physiological meaning of IBs is a sine qua non condition. To this end, a more recent model-free approach has been suggested in literature for the analysis of DCE-MRI data, which relies on the shape classification of the time-signal uptake curves of image pixels in a selected tumor region of interest. This thesis is centered on this new approach and the clinical question whether model-free DCE-MRI data analysis has the potential to provide robust, clinically significant biomarkers using pattern recognition and image analysis techniques