Doctor of Philosophy, Case Western Reserve University, 2020, Biomedical Engineering

Personalized interventions in ongologic applications could enable significant benefits to cancer patients by tailoring treatment based on disease phenotype and associated treatment response. Not only will such strategies yield better outcomes, but also will incur lower morbidity rates and better quality-of-life by avoiding unnecessary operations. In the context of locally advanced rectal cancer, current standard-of-care recommends surgical removal of the entire rectum and surrounding tissue after neoadjuvant chemoradiation. However, a significant proportion of rectal cancer patients exhibit minimal to no remaining disease on the excised specimen, and could have been candidates for non-surgical, active surveillance. Magnetic resonance imaging (MRI) is routinely utilized for staging and re-staging the tumor before and after treatment, but is subjective and known to have poor correlation with pathologic staging. Recently, the computerized-extraction of more advanced features from radiographic images, or radiomics, which attempts to quantifying tissue attributes on imaging, has enabled improved disease characterization compared to visual inspection alone. While initial studies have shown promise for radiomic analyses in the context of predicting treatment response for rectal cancers, there is still a need to understand what they are capturing and ensure their utility between different scanners and different hospitals. Traditionally, statistical descriptors are used to describe the heterogeneity captured by radiomic operators, but may be sensitive to noise and not adequately characterize treatment-related changes in the rectal environment. It is therefore important to design radiomic features which capture more information that is pathologically or physiologically intuitive. Finally, in order to ensure their utility in a clinical setting, it is important to identify a stable set of radiomic features, or features which are reproducible across different sites and consistent in n (open full item for complete abstract)

Committee: Anant Madabhushi PhD (Committee Chair); Satish Viswanath PhD (Advisor); David Wilson PhD (Committee Member); Sharon Stein MD (Committee Member); Andrei Purysko MD (Committee Member) Subjects: Biomedical Engineering; Computer Science; Oncology