Ovarian cancer has one of the highest mortality rates of all gynecological cancers [13]. Further knowledge of risk factors for the growth of ovarian tumors would be beneficial in both the treatment and prevention of this type of cancer. Previous research has shown a positive correlation between diabetes and prostate tumor growth [22], The first aim of this study was to determine the effect of diabetes of ovarian tumor growth. The second aim was to develop a model to predict ovarian tumor growth based on the microenvironment within a patient’s body. The hypothesis was that there would be a positive correlation between diabetes and ovarian tumor volume. Data from fifty patients was collected from charts at Grady Memorial Hospital in Atlanta, Georgia. Oxygen saturation, tumor volume, blood glucose level, and cancer stage were gathered for each patient. The results contradicted the hypothesis; there was a negative correlation found between blood glucose level and tumor volume. More data is needed to determine if increased blood glucose could be an effective treatment of ovarian cancer, particularly since there other health risks associated with elevated blood glucose levels. The proposed mathematical model also needs modification in order to effectively bridge the gap between the clinical and research aspects of the cancer field.
","abstract_html":"<p>Ovarian cancer has one of the highest mortality rates of all gynecological cancers [13]. Further knowledge of risk factors for the growth of ovarian tumors would be beneficial in both the treatment and prevention of this type of cancer. Previous research has shown a positive correlation between diabetes and prostate tumor growth [22], The first aim of this study was to determine the effect of diabetes of ovarian tumor growth. The second aim was to develop a model to predict ovarian tumor growth based on the microenvironment within a patient’s body. The hypothesis was that there would be a positive correlation between diabetes and ovarian tumor volume. Data from fifty patients was collected from charts at Grady Memorial Hospital in Atlanta, Georgia. Oxygen saturation, tumor volume, blood glucose level, and cancer stage were gathered for each patient. The results contradicted the hypothesis; there was a negative correlation found between blood glucose level and tumor volume. More data is needed to determine if increased blood glucose could be an effective treatment of ovarian cancer, particularly since there other health risks associated with elevated blood glucose levels. The proposed mathematical model also needs modification in order to effectively bridge the gap between the clinical and research aspects of the cancer field.</p>","abstract_has_math":false,"creators":["Belay, Claire"],"institution":null,"degree_name":"Biology","degree_level":"Thesis","degree_discipline":"Biology","degree_department":null,"school":null,"contributors":["Dr. Monica Frazier","Dr. Guihong Fan","Dr. Cindy Ticknor"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-01-01T08:00:00Z","date_published":"2018-01-01T08:00:00Z","updated_at":"2026-07-24T01:45:01Z","subjects":["Ovarian","Ovaries","Cancer","Diabetes","Mathematical Model","Tumor","Biology","Cancer Biology"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://csuepress.columbusstate.edu/theses_dissertations/336","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Monica Frazier","Dr. Guihong Fan","Dr. Cindy Ticknor"]},{"key":"dc:creator","label":"Author","values":["Belay, Claire"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-23T08:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Biology"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Ovarian","Ovaries","Cancer","Diabetes","Mathematical Model","Tumor","Biology","Cancer Biology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://csuepress.columbusstate.edu/theses_dissertations/336"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Ovarian cancer has one of the highest mortality rates of all gynecological cancers [13]. Further knowledge of risk factors for the growth of ovarian tumors would be beneficial in both the treatment and prevention of this type of cancer. Previous research has shown a positive correlation between diabetes and prostate tumor growth [22], The first aim of this study was to determine the effect of diabetes of ovarian tumor growth. The second aim was to develop a model to predict ovarian tumor growth based on the microenvironment within a patient’s body. The hypothesis was that there would be a positive correlation between diabetes and ovarian tumor volume. Data from fifty patients was collected from charts at Grady Memorial Hospital in Atlanta, Georgia. Oxygen saturation, tumor volume, blood glucose level, and cancer stage were gathered for each patient. The results contradicted the hypothesis; there was a negative correlation found between blood glucose level and tumor volume. More data is needed to determine if increased blood glucose could be an effective treatment of ovarian cancer, particularly since there other health risks associated with elevated blood glucose levels. The proposed mathematical model also needs modification in order to effectively bridge the gap between the clinical and research aspects of the cancer field.
"]},{"key":"dc:title","label":"Title","values":["Effects of Diabetes on Ovarian Cancer: Data Analysis and Modeling Study"]}]}],"canonical_facts":{"dc:contributor":["Dr. Monica Frazier","Dr. Guihong Fan","Dr. Cindy Ticknor"],"dc:creator":["Belay, Claire"],"dc:date.available":["2020-01-23T08:00:00Z"],"dc:description.abstract":["Ovarian cancer has one of the highest mortality rates of all gynecological cancers [13]. Further knowledge of risk factors for the growth of ovarian tumors would be beneficial in both the treatment and prevention of this type of cancer. Previous research has shown a positive correlation between diabetes and prostate tumor growth [22], The first aim of this study was to determine the effect of diabetes of ovarian tumor growth. The second aim was to develop a model to predict ovarian tumor growth based on the microenvironment within a patient’s body. The hypothesis was that there would be a positive correlation between diabetes and ovarian tumor volume. Data from fifty patients was collected from charts at Grady Memorial Hospital in Atlanta, Georgia. Oxygen saturation, tumor volume, blood glucose level, and cancer stage were gathered for each patient. The results contradicted the hypothesis; there was a negative correlation found between blood glucose level and tumor volume. More data is needed to determine if increased blood glucose could be an effective treatment of ovarian cancer, particularly since there other health risks associated with elevated blood glucose levels. The proposed mathematical model also needs modification in order to effectively bridge the gap between the clinical and research aspects of the cancer field.
"],"dc:identifier":["https://csuepress.columbusstate.edu/theses_dissertations/336"],"dc:language":["English"],"dc:subject":["Ovarian","Ovaries","Cancer","Diabetes","Mathematical Model","Tumor","Biology","Cancer Biology"],"dc:title":["Effects of Diabetes on Ovarian Cancer: Data Analysis and Modeling Study"],"thesis:degree_discipline":["Biology"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Biology"]},"updated_at":"2026-07-24T01:45:01Z"}