Cancer treatment courses which rely on imaging and spatially-dependent therapy involve making multiple treatment decisions (e.g., radiotherapy alone, radiotherapy plus chemotherapy, induction chemotherapy) over time. These decisions depend on complex factors, including the tumor location with respect to sensitive organs and its response to treatment, laboratory data, toxicity, anticipated side effects and survival probability. In this project, we design and develop novel statistical methodology for dynamic and personalized treatment decisions with specific application to head and neck cancer radiotherapy planning. The empirically-derived treatment rules developed in this project have the potential to improve the standard of care (i.e., treatment plans chosen by the tumor treatment board) and the quality of life of surviving patients. The methods developed in the proposal may be used to derive optimal treatment strategies across not only a variety of spatially-dependent cancer diagnoses, but also other chronic conditions including mental health disorders, substance abuse diseases, or diabetes, that require making multiple decisions that must weigh the tradeoffs between efficacy and toxicity. This Big Data collaboration effectively bridges the quantitative sciences with biomedicine, and provides quantitative techniques for leveraging the largest existing repository of head and neck cancer data in the country.
The statistical methodology developed in this project will improve the standard of care and the quality of life of surviving head and neck cancer patients. These quantitative techniques leverage the largest existing repository of head and neck cancer data in the country, advancing our understanding of human health and disease.
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|Guzun, Gheorghi; Canahuate, Guadalupe (2018) Distributed query-aware quantization for high-dimensional similarity searches. Adv Database Technol 2018:373-384|
|M. D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group (2018) Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep 8:1524|
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|Multidisciplinary Larynx Cancer Working Group; Mulcahy, Collin F; Mohamed, Abdallah S R et al. (2018) Age-adjusted comorbidity and survival in locally advanced laryngeal cancer. Head Neck 40:2060-2069|
|Elhalawani, Hesham; Lin, Timothy A; Volpe, Stefania et al. (2018) Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 8:294|
|Ma, Chihua; Pellolio, Filippo; Llano, Daniel A et al. (2017) RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphs. J Imaging Sci Technol 61:|
|MICCAI/M.D. Anderson Cancer Center Head and Neck Quantitative Imaging Working Group (2017) Matched computed tomography segmentation and demographic data for oropharyngeal cancer radiomics challenges. Sci Data 4:170077|
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