Exploring feature-based approaches in PET images for predicting cancer treatment outcomes
Accumulating evidence suggests that characteristics of pre-treatment FDGPET could be used as prognostic factors to predict outcomes in different cancer sites. Current risk analyses are limited to visual assessment or direct uptake value measurements. We are investigating intensityvolume histogram metrics and shape and texture features extracted from PET images to predict patient's response to treatment. These approaches were demonstrated using datasets from cervix and head and neck cancers, where AUC of 0.76 and 1.0 were achieved, respectively. The preliminary results suggest that the proposed approaches could potentially provide better tools and discriminant power for utilizing functional imaging in clinical prognosis.
treatment outcomes uptake values intensityvolume histograms positron emission tomography image morphology co-occurrence matrix tumor shape tumor heterogeneity