Precision imaging in gynecologic cancer
Gynecologic cancers have characteristic structural and functional imaging features reflected in clinical phenotypes, and these imaging biomarkers highlight pathogenic mechanisms potentially targetable by novel treatments. The challenge is now to integrate these imaging biomarkers into clinically relevant treatment algorithms by identifying molecular targets for treatment based on imaging biomarker profiles. Our multidisciplinary research team with competency in computer science, visualization and machine learning algorithms, diagnostic radiology, clinical gynecology, preclinical models and molecular biology/genetics will join efforts to pursue these challenges as one of the funded projects within Centre for Medical Imaging and Visualization in Bergen.
Molecular and imaging biomarkers in gynecologic cancer will be studied in patients and in preclinical gynecologic cancer models (Figure). Potential imaging biomarkers will be identified using machine learning algorithms applied to multiparametric and functional magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) from patients and in mouse models during therapy. Furthermore, the molecular and genetic alterations in the tumors as well as clinical phenotype and survival will be studied in relation to the corresponding imaging biomarker profile using integrative analyses. This research initiative provides a unique platform for identifying promising molecular targets for treatment and their corresponding imaging biomarker profiles. Studying imaging biomarkers in mice during targeted therapy will also facilitate the integration of imaging biomarker guided treatment algorithms and imaging guided monitoring of treatment response in gynecologic cancer. This project has the potential to improve patient care by enabling individualized and targeted treatment in gynecologic cancer patients.