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Learning Radiogenomic and Longitudinal Patterns to Characterize Disease Trajectory


Disease trajectories describe the health of patients as they are diagnosed, treated, and observed. Along this path, physicians are repeatedly expected to integrate numerous pieces of evidence and explain the possible outcomes of the disease, such as survival. This expectation becomes increasingly challenging with the proliferation of patient data collected. The goal of this research is to evaluate the use of machine learning and related techniques to integrate complementary patient data to better characterize disease trajectories. This dissertation provides approaches (1) to map cellular-level information to diagnostic images, (2) to identify temporal patterns that can estimate the probability of decline at each patient encounter, and (3) to interpret the trained models to explain the underlying patterns of disease and subset ones that affect survival. The approaches are demonstrated on patients with glioblastoma, a brain tumor with poor prognosis.

This research shows how radiogenomic maps can be created using deep neural networks to improve the representation of gene expression. Methods developed to interpret neural networks were systematically evaluated and found transcriptomic patterns of imaging traits that were both new and consistent with prior works. Furthermore, longitudinal changes were mined from patients' radiology and oncology visits. The temporal patterns were used to predict residual survival at each visit and were consistent with physicians' understanding of disease progression. Together, these approaches demonstrate how patterns integrated from molecular, medical imaging, and other clinical data can provide prognostic insights throughout a patient's disease trajectory.

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