Cancer tissue classification from DCE-MRI data using pattern recognition techniques
Day - Time:
06 June 2019, h.19:00
Area della Ricerca CNR di Pisa - Room: C-29
- Maria Venianaki (FORTH-ICS)
Cancer research has significantly advanced in recent yearsmainly through developments in medical genomics andbioinformatics. It is expected that such approaches will resultin more durable tumor control and fewer side effects comparedwith conventional treatments such as radiotherapy orchemotherapy. From the imaging standpoint, non-invasive imagingbiomarkers (IBs) that assess angiogenic response and tumorenvironment at an early stage of therapy are of utmostimportance, since they could provide useful insights into therapyplanning. However, the extraction of IBs is still an openproblem, since there are no standardized imaging protocols yet orestablished methods for the robust extraction of IBs. DCE-MRI isamongst the most promising non-invasive functional imagingmodalities with compartmental pharmacokinetic (PK) modeling beingthe most common technique used for DCE-MRI data analysis.However, PK models suffer from a number of limitations such asmodeling complexity, which often leads to variability in thecomputed biomarkers. To address these problems, alternativeDCE-MRI biomarker extraction strategies coupled with a profoundunderstanding of the physiological meaning of IBs is a sine quanon condition. To this end, a more recent model-free approach hasbeen suggested in literature for DCE-MRI data analysis, whichrelies on the shape classification of the time-signal uptakecurves of image pixels in a selected tumor region of interest.This talk is centered on this classification approach and theclinical question whether model-free DCE-MRI data analysis hasthe potential to provide robust, clinically significantbiomarkers using pattern recognition and image analysistechniques.