We propose to develop and validate a quantitative Positron Emission Tomography / Computed Tomography (PET/CT) image analysis framework to improve the evaluation of esophageal tumor response to chemoradiotherapy (CRT) in patients with locally advanced esophageal cancer.
In Aim 1, we will extract comprehensive spatial and temporal features of a tumor from PET/CT images and evaluate their ability in predicting tumor response to CRT. These features will quantify the spatial characteristics of a tumor along with their changes due to CRT, adding a great amount of information to the current non-volumetric PET/CT response measures. Also, we will use image registration techniques to align pre-CRT images with post-CRT images, making it possible to quantify the spatial changes at the original tumor site.
In Aim 2, we will construct and test reliable predictive models of tumo response to CRT using machine learning techniques with spatial- temporal PET/CT features, clinical parameters and demographics as input. The models will identify an optimal subset of predictive features and utilize PET and CT information in chorus.
In Aim 3, we will develop a novel multi-modality adaptive region-growing algorithm for tumor delineation in PET/CT. We will compare the accuracy and precision of the resulting predictive models against those in which tumor is delineated using conventional methods (manual contouring or thresholding). This comparison will help us understand to what degree the prediction of tumor response depends on the tumor delineation methods. Finally, we will use pathologic response and survival as the end points and ground truth to cross-validate each predictive model. If all aims are achieved, the proposed PET/CT image analysis framework may provide a highly accurate diagnostic tool. This, will complement other diagnostic tests in assisting physicians in making a treatment decision to more appropriately select patients for surgery, thus avoiding the mortality and morbidity of surgery in responders for whom surgery can be safely deferred; while improving local control and survival in non- responders for whom surgery should be considered. Therefore, this work has the potential to improve outcomes by safely deferring surgery which will improve our locally advanced esophageal cancer patient's quality of life while simultaneously reducing costs.
We propose to develop and validate a quantitative Positron Emission Tomography / Computed Tomography (PET/CT) image analysis framework to improve the evaluation of esophageal tumor response to chemoradiotherapy. If all aims are achieved, the proposed PET/CT image analysis framework may provide a highly accurate diagnostic tool. This, will complement other diagnostic tests in assisting physicians in making a treatment decision to more appropriately select patients for surgery, thus avoiding the mortality and morbidity of surgery in responders for whom surgery can be safely deferred; while improving local control and survival in non-responders for whom surgery should be considered. Therefore, this work has the potential to improve outcomes by safely deferring surgery which will improve our locally advanced esophageal cancer patient's quality of life while simultaneously reducing costs.
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