Image-based analyses as a means of modifying Radiation Therapy to customize treatment based on an individual patient's prognosis or early response to treatment is a research area crucial to the success of this program project. Project 3 investigates the hypothesis that, given the underlying heterogeneity of tumors and normal organs and their response to treatment, anatomic, metabolic and physiologic images (as acquired in the clinical projects of this program) are best utilized for individualizing therapy by robustly identifying subvolumes that are more predictive than individual voxels or the entire tumor/normal organ as a w/hole, and providing these subvolumes as a spatial guide for radiation dose redistributions such as focal boosting.
Aim 1 will improve reconstruction of physiological images.
Aim 2 is to investigate methods of Identifying subvolumes predictive of response.
Aim 3 will improve the accuracy of mapping subvolumes to patients to guide modification of radiation therapy dose distributions. Pattern recognition techniques will be developed for subvolume identification, and correlated with outcomes (e.g. sites of local failure). Complementary and redundant information from different imaging methods (e.g. 11C Methionine PET and DCE MRI in gliomas) for local response prediction will be studied, as well as the reproducibility of imaging signals and extracted subvolumes (from test-retest data). Methods to improve physiological imaging and analysis, including direct analysis of pharmacokinetics via pattern recognition, and sparsely sampled parallel MR! reconstructed using compressed sensing to resolve organ movement and improve the temporal resolution and/or volumetric coverage, will be investigated. To guide therapy individualization, subvolumes need to be mapped geometrically to the treated patient, with uncertainties due to the limits of image registration accuracy. Finite element models will be investigated as atlases to regularize the intensity-based deformable alignment methods used to place these subvolumes in the space of treatment planning CT scans acquired for the purposes of planning and/or modifying treatment.
While MRI, PET, and SPECT scans can help predict how an individual patient may respond differently to treatment than others with the same disease, methods to identify poorly responding parts of tumors or normal organs from these images are not yet developed. This project investigates ways to find the regions of a tumor that need further treatment or parts of normal organs that should be spared.
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|Jochems, Arthur; Deist, Timo M; El Naqa, Issam et al. (2017) Developing and Validating a Survival Prediction Model for NSCLC Patients Through Distributed Learning Across 3 Countries. Int J Radiat Oncol Biol Phys 99:344-352|
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|El Naqa, Issam; Kerns, Sarah L; Coates, James et al. (2017) Radiogenomics and radiotherapy response modeling. Phys Med Biol 62:R179-R206|
|Dess, Robert T; Sun, Yilun; Matuszak, Martha M et al. (2017) Cardiac Events After Radiation Therapy: Combined Analysis of Prospective Multicenter Trials for Locally Advanced Non-Small-Cell Lung Cancer. J Clin Oncol 35:1395-1402|
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