Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Industrial and Management Systems Engineering

Major Professor

Grisselle Centeno, Ph.D.

Co-Major Professor

Steven Eschrich, Ph.D.

Committee Member

Steven Eschrich, Ph.D.

Committee Member

Javier Torres-Roca, M.D.

Committee Member

Ali Yalcin, Ph.D.

Committee Member

Jose Zayas-Castro, Ph.D.

Keywords

Fuzzy Logic, Gene Expression, Random Forest, Rectal Cancer, Supervised Learning, Systems Biology

Abstract

This work is motivated by the need of providing patients with a decision support system that facilitates the selection of the most appropriate treatment strategy in cancer treatment. Treatment options are currently subject to predetermined clinical pathways and medical expertise, but generally, do not consider the individual patient characteristics or preferences. Although genomic patient data are available, this information is rarely used in the clinical setting for real-life patient care. In the area of personalized medicine, the advancement in the fundamental understanding of cancer biology and clinical oncology can promote the prevention, detection, and treatment of cancer diseases.

The objectives of this research are twofold. 1) To develop a patient-centered decision support model that can determine the most appropriate cancer treatment strategy based on subjective medical decision criteria, and patient's characteristics concerning the treatment options available and desired clinical outcomes; and 2) to develop a methodology to organize and analyze gene expression data and validate its accuracy as a predictive model for patient's response to radiation therapy (tumor radiosensitivity).

The complexity and dimensionality of the data generated from gene expression microarrays requires advanced computational approaches. The microarray gene expression data processing and prediction model is built in four steps: response variable transformation to emphasize the lower and upper extremes (related to Radiosensitive and Radioresistant cell lines); dimensionality reduction to select candidate gene expression probesets; model development using a Random Forest algorithm; and validation of the model in two clinical cohorts for colorectal and esophagus cancer patients.

Subjective human decision-making plays a significant role in defining the treatment strategy. Thus, the decision model developed in this research uses language and mechanisms suitable for human interpretation and understanding through fuzzy sets and degree of membership. This treatment selection strategy is modeled using a fuzzy logic framework to account for the subjectivity associated to the medical strategy and the patient's characteristics and preferences. The decision model considers criteria associated to survival rate, adverse events and efficacy (measured by radiosensitivity) for treatment recommendation. Finally, a sensitive analysis evaluates the impact of introducing radiosensitivity in the decision-making process.

The intellectual merit of this research stems from the fact that it advances the science of decision-making by integrating concepts from the fields of artificial intelligence, medicine, biology and biostatistics to develop a decision aid approach that considers conflictive objectives and has a high practical value. The model focuses on criteria relevant to cancer treatment selection but it can be modified and extended to other scenarios beyond the healthcare environment.

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