Development of imaging-based response predictors for personalized radiotherapy in head and neck cancer

Tumor response to chemoradiotherapy is heterogeneous in patients with head and neck cancer. At the same time, head and neck radiotherapy can lead to significant toxicity in treated patients. Personalization of treatment could improve response to treatment while minimizing side effects. The largest bottleneck to employ personalization approaches are the lack of methods for response and toxicity prediction. In this thesis we therefore provide improved approaches for response prediction. In part one, we present improved MRI techniques to measure response before and early during treatment. In part two we present dose response models for osteoradionecrosis of the mandible incorporating key spatial information and the equivalent uniform dose as a generalizable dose variable across different fractionation schemes. The presented MRI techniques and dose response models can now be validated in larger groups of patients, after which they could contribute to personalized treatment planning and decision making processes

Development of imaging-based response predictors for personalized radiotherapy in head and neck cancer

Tumor response to chemoradiotherapy is heterogeneous in patients with head and neck cancer. At the same time, head and neck radiotherapy can lead to significant toxicity in treated patients. Personalization of treatment could improve response to treatment while minimizing side effects. The largest bottleneck to employ personalization approaches are the lack of methods for response and toxicity prediction. In this thesis we therefore provide improved approaches for response prediction. In part one, we present improved MRI techniques to measure response before and early during treatment. In part two we present dose response models for osteoradionecrosis of the mandible incorporating key spatial information and the equivalent uniform dose as a generalizable dose variable across different fractionation schemes. The presented MRI techniques and dose response models can now be validated in larger groups of patients, after which they could contribute to personalized treatment planning and decision making processes

Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

International audienceWhile accurate tumor delineation in FDG-PET is a vital task, noisy and blurring imaging system makes it a challenging work. In this paper, we propose to address this issue using the theory of belief functions, a powerful tool for modeling and reasoning with uncertain and/or imprecise information. An automatic segmentation method based on clustering is developed in 3-D, where, different from available methods, PET voxels are described not only by intensities but also com-plementally by features extracted from patches. Considering there are a large amount of features without consensus regarding the most informative ones, and some of them are even unreliable due to image quality, a specific procedure is adopted to adapt distance metric for properly representing clustering distortions and neighborhood similarities. A specific spatial regularization is also included in the clustering algorithm to effectively quantify local homogeneity. The proposed method has been evaluated by real-patient images, showing good performance

Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

International audienc

Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

International audienc