PHD, Kent State University, 2024, College of Public Health

This study focused on evaluating the patient-reported psychological outcomes (PRPOs), including anxiety, depression, cancer-related post-traumatic stress disorder symptoms (CR-PTSD), fear of cancer recurrence (FCR), and post-traumatic growth (PTG), among surgically treated colorectal cancer (CRC) patients. Additionally, this study examined the association between coping strategies and these PRPOs. The research involved 23 CRC patients undergoing curative surgery. With the exception of FCR, which was measured only post-surgery, the study conducted assessments of all PRPOs and coping strategies at two crucial points: before and after the surgical intervention. Results demonstrated a significant reduction in anxiety levels post-surgery, while depression scores remained unchanged. PTG, particularly in the dimensions of Relating to Others and Appreciation of Life, showed significant increases, indicating potential positive psychological adaptation following surgery. In contrast, CR-PTSD symptoms were minor and exhibited negligible changes that were not statistically significant. For coping strategies, there was a significant improvement in problem-focused coping post-surgery, whereas emotion-focused and avoidant coping strategies remained unchanged. Despite improvements in certain psychological outcomes and coping strategies, the study identified a high frequency of FCR among participants post-surgery, with 70% reporting elevated levels (≥12). Regression analysis showed that problem-focused coping strategies were significantly associated with reduced anxiety levels and positively correlated with PTG factors over time. These findings highlight the importance of adaptive coping mechanisms in affecting psychological outcomes after CRC surgery. The persistent high levels of FCR post-surgery underline the need for targeted psychosocial interventions to address this prevalent concern among CRC survivors. In conclusion, this research underscores the complexity of psychological (open full item for complete abstract)

Committee: Melissa Zullo (Committee Chair); Joel Hughes (Committee Member); Lynette Phillips (Committee Member); Vinay Cheruvu (Committee Member) Subjects: Epidemiology; Health Care; Health Care Management; Oncology; Psychology; Public Health; Statistics; Surgery

Master of Engineering, Case Western Reserve University, 2024, Biomedical Engineering

Radiomic analysis of individual regions or acquisitions has shown significant potential for predicting treatment response to neoadjuvant therapy in rectal cancers via routine MRI. We present a novel multi-plane, multi-region radiomics framework for exploiting intuitive clinical and biological aspects of rectal tumor response on MRI. Using a multi-institutional cohort of 151 baseline T2-weighted axial and coronal rectal MRIs, 2D texture features were extracted from multiple regions of interest (tumor, tumor-proximal fat) across both axial and coronal planes, with machine learning analysis to identify descriptors predictive of complete response to neoadjuvant therapy. Our multi-plane, multi-region radiomics model was found to significantly outperform single-plane or single-region feature sets with a discovery area under the ROC curve (AUC) of 0.765±0.054, and hold-out validation AUCs of 0.700 and 0.759. This suggests multi- region, multi-plane radiomics could enable detailed phenotyping of treatment response on MRI and thus personalization of therapeutic and surgical interventions in rectal cancers.

Committee: Satish Viswanath (Committee Chair); Amit Gupta (Committee Member); Juhwan Lee (Committee Member) Subjects: Artificial Intelligence; Bioinformatics; Biomedical Engineering; Biomedical Research; Medical Imaging

Master of Sciences, Case Western Reserve University, 2022, Biomedical Engineering

Evaluating tumor regression and nodal metastasis in rectal cancers via MRI after standard-of-care chemoradiation therapy (CRT) remains highly challenging in clinical practice. Recent studies have shown that physiologic environments surrounding tumor regions may provide complementary information that is predictive of response to CRT and patient survival. In order to capture physiologic changes due to CRT within the rectal wall (primary location of tumor) and perirectal fat (region surrounding tumor), we evaluated the performance of radiomics characterization of these regions within the rectal environment on post-chemoradiation T2-weighted (T2w) MRI in predicting tumor regression and nodal response after CRT. A total of 83 rectal cancer patients for whom MRIs as well as pathologic tumor staging were available post-CRT were included in this study, from two different institutions. Region-wise radiomic features were extracted from expert annotated perirectal fat and rectal wall regions on MRI and a 2-stage feature selection was employed to identify the most relevant features from each of these locations. Combining radiomic features from across the fat and wall yielded significantly improved classifier performance for identifying tumor stage regression (AUC=0.86) as well as nodal metastasis (AUC = 0.7) when compared to the performance of these regions individually; a trend that generalized across discovery and hold-out validation. Top-ranked radiomic features were also found to be associated with pathologic complete response, surrogates of long-term survival, as well as revealed significant differences between age- and sex-specific subgroups.

Committee: Satish Viswanath (Committee Chair) Subjects: Biomedical Engineering

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

Master of Sciences (Engineering), Case Western Reserve University, 2016, Biomedical Engineering

Medical imaging has become widespread for evaluating treatment response in vivo by measuring changes in tumor size or contrast uptake. However, the imaging data alone is limited as it does not capture all information needed for a complete characterization of tumoral response to treatment. Recently, the use of image analysis has enabled strategies for more intelligently extracting information from imaging data. However, these strategies have been scarcely used for treatment response evaluation purposes. In this work, we investigate the development of two classes of image analysis called (1) radiomics for early treatment response characterization and (2) radiologypathology fusion. Radiomics will enable the detection of more subtle changes in the tumor environment in vivo early into the treatment regime, and the fusion of radiology and pathology data will allow annotations of treatment effects seen on the pathology specimen to be spatially mapped onto the corresponding imaging.

Committee: Anant Madabhushi Dr. (Advisor); Pallavi Tiwari Dr. (Committee Member); Xin Yu Dr. (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Medical Imaging

PhD, University of Cincinnati, 2014, Pharmacy: Pharmaceutical Sciences/Biopharmaceutics

Background: Colorectal cancer (CRC) is a type of cancer which develops from uncontrolled cell growth in the colon or rectum. It is the third most commonly diagnosed cancer in males and the second in females. In epidemiologic research for CRC, advanced cancer stage is an important factor for determining disease development and treatment patterns. However, this variable is not available because medical claims databases is retrospective and only original built for financial analysis only. Algorithms to predict advanced CRC stage were developed based on the existing medical information in claims database. Method: Study cohorts were identified from the Surveillance Epidemiology and End Results (SEER)-Medicare database. Two algorithms were constructed based on covariates obtained from the database for different study periods, including demographic, treatment pattern variables. The training set was used to derive predictive equations by using logistic regression model, then applied to validation set for evaluating the predictive characteristics (sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV)). The developed algorithm were applied to MarketScan® Commercial Claims and Encounters Database and tested the predictive values. Results: The algorithm of predicting advanced CRC stage in 1999 to 2003 achieved sensitivity 50.3% and specificity 95.0%, PPV 66.78% and NPV 90.58% while the equation distinguishing CRC stage IV in 2004 to 2007 achieved sensitivity 56.8%, specificity 95.3%, PPV 71.86% and NPV 91.19%. All algorithms made better predictive values than the single ICD-9 metastatic diagnosis as the predictor. Then the algorithm for 1999 to 2003 was applied to MarketScan database. 9484 patients were predicted as non-advanced CRC group while 1097 patients were assigned to advanced CRC group. Conclusion Claims-based algorithms were developed to predict advanced cancer stage. These algorithms were shown to be successful in the recent stu (open full item for complete abstract)

Committee: Jianfei Guo Ph.D. (Committee Chair); Jane Pruemer Pharm.D. (Committee Member); Christina Kelton Ph.D. (Committee Member); Wei Pan Ph.D. (Committee Member); Patricia Wigle Pharm.D. (Committee Member) Subjects: Classical Studies; Pharmaceuticals