{"id":{"repo_id":"utswmed","oai_identifier":"oai:utswmed-ir.tdl.org:2152.5/10057"},"canonical_url":"https://search.dev.ndltd.org/etd/utswmed/oai:utswmed-ir.tdl.org:2152.5/10057","repository":{"repo_id":"utswmed","name":"University of Texas Southwestern Medical Center","base_url":"https://utswmed-ir.tdl.org/server/oai/request"},"display":{"title":"Automated Treatment Planning in High Dose-Rate Brachytherapy for Cervical Cancer","abstract":"Standard care of cervical cancer is chemoradiation therapy followed by a boost to the cervix tumor site with High-dose-rate brachytherapy (HDRBT). HDRBT is a procedure that involves the insertion of radioactive sources into the tumorous area to ablate the cancer. Treatment planning for the procedure is typically performed on the day of treatment. The complex treatment planning process has led to issues such as prolonged treatment planning time and suboptimal plan quality. This dissertation reports systematic studies to improve treatment planning for HDRBT of cervical cancer. After a brief overview in Chapter 1, Chapter 2 will analyze the problems of sub-optimal plan quality and time management in the treatment planning workflow at our institution. In Chapter 3, developments on computational modules for automatic organ segmentation will be presented. Current clinical practice primarily relies on manual organ segmentation, but we have developed deep-learning models to segment the bladder, rectum, and sigmoid colon automatically, organs of particular importance for cervical cancer HDRBT. In Chapter 4, I present integration of the organ segmentation modules and other modules into the AutoBrachy system for clinical use to automate the planning process. Chapter 5 presents a deep-learning based method that can predict the physician's preference for a patient-specific treatment plan as defined by EQD2 to the bladder, rectum, sigmoid, and CTV D90. This method will serve as a guide in the future to automatically create patient-specific physician preferred treatment plans. Chapter 6 reports the study analyzing benefits from the use of joint intracavitary and interstitial HDRBT. Finally, Chapter 7 concludes the dissertation with discussions and future work.","abstract_html":"Standard care of cervical cancer is chemoradiation therapy followed by a boost to the cervix tumor site with High-dose-rate brachytherapy (HDRBT). HDRBT is a procedure that involves the insertion of radioactive sources into the tumorous area to ablate the cancer. Treatment planning for the procedure is typically performed on the day of treatment. The complex treatment planning process has led to issues such as prolonged treatment planning time and suboptimal plan quality. This dissertation reports systematic studies to improve treatment planning for HDRBT of cervical cancer. After a brief overview in Chapter 1, Chapter 2 will analyze the problems of sub-optimal plan quality and time management in the treatment planning workflow at our institution. In Chapter 3, developments on computational modules for automatic organ segmentation will be presented. Current clinical practice primarily relies on manual organ segmentation, but we have developed deep-learning models to segment the bladder, rectum, and sigmoid colon automatically, organs of particular importance for cervical cancer HDRBT. In Chapter 4, I present integration of the organ segmentation modules and other modules into the AutoBrachy system for clinical use to automate the planning process. Chapter 5 presents a deep-learning based method that can predict the physician's preference for a patient-specific treatment plan as defined by EQD2 to the bladder, rectum, sigmoid, and CTV D90. This method will serve as a guide in the future to automatically create patient-specific physician preferred treatment plans. Chapter 6 reports the study analyzing benefits from the use of joint intracavitary and interstitial HDRBT. Finally, Chapter 7 concludes the dissertation with discussions and future work.","abstract_has_math":false,"creators":["Gonzalez, Yesenia Amanda"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Wang, Jing","Medin, Paul","Hrycushko, Brian","Albuquerque, Kevin"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-06-07T18:15:26Z","date_published":"2023-06-07T18:15:26Z","updated_at":"2026-07-24T05:52:24Z","subjects":["Brachytherapy","Deep Learning","Uterine Cervical Neoplasms"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["1381370389"],"render_values":[{"text":"1381370389","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/2152.5/10057","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Jing","Medin, Paul","Hrycushko, Brian","Albuquerque, Kevin"]},{"key":"dc:creator","label":"Author","values":["Gonzalez, Yesenia Amanda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-06-07T18:15:26Z","2021-05","2021-05-01T05:00:00.000Z","May 2021","2023-06-07T18:15:27Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis","text"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Brachytherapy","Deep Learning","Uterine Cervical Neoplasms"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2152.5/10057","1381370389"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Standard care of cervical cancer is chemoradiation therapy followed by a boost to the cervix tumor site with High-dose-rate brachytherapy (HDRBT). HDRBT is a procedure that involves the insertion of radioactive sources into the tumorous area to ablate the cancer. Treatment planning for the procedure is typically performed on the day of treatment. The complex treatment planning process has led to issues such as prolonged treatment planning time and suboptimal plan quality. This dissertation reports systematic studies to improve treatment planning for HDRBT of cervical cancer. After a brief overview in Chapter 1, Chapter 2 will analyze the problems of sub-optimal plan quality and time management in the treatment planning workflow at our institution. In Chapter 3, developments on computational modules for automatic organ segmentation will be presented. Current clinical practice primarily relies on manual organ segmentation, but we have developed deep-learning models to segment the bladder, rectum, and sigmoid colon automatically, organs of particular importance for cervical cancer HDRBT. In Chapter 4, I present integration of the organ segmentation modules and other modules into the AutoBrachy system for clinical use to automate the planning process. Chapter 5 presents a deep-learning based method that can predict the physician's preference for a patient-specific treatment plan as defined by EQD2 to the bladder, rectum, sigmoid, and CTV D90. This method will serve as a guide in the future to automatically create patient-specific physician preferred treatment plans. Chapter 6 reports the study analyzing benefits from the use of joint intracavitary and interstitial HDRBT. Finally, Chapter 7 concludes the dissertation with discussions and future work."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automated Treatment Planning in High Dose-Rate Brachytherapy for Cervical Cancer"]}]}],"canonical_facts":{"dc:contributor":["Wang, Jing","Medin, Paul","Hrycushko, Brian","Albuquerque, Kevin"],"dc:creator":["Gonzalez, Yesenia Amanda"],"dc:date":["2023-06-07T18:15:26Z","2021-05","2021-05-01T05:00:00.000Z","May 2021","2023-06-07T18:15:27Z"],"dc:description":["Standard care of cervical cancer is chemoradiation therapy followed by a boost to the cervix tumor site with High-dose-rate brachytherapy (HDRBT). HDRBT is a procedure that involves the insertion of radioactive sources into the tumorous area to ablate the cancer. Treatment planning for the procedure is typically performed on the day of treatment. The complex treatment planning process has led to issues such as prolonged treatment planning time and suboptimal plan quality. This dissertation reports systematic studies to improve treatment planning for HDRBT of cervical cancer. After a brief overview in Chapter 1, Chapter 2 will analyze the problems of sub-optimal plan quality and time management in the treatment planning workflow at our institution. In Chapter 3, developments on computational modules for automatic organ segmentation will be presented. Current clinical practice primarily relies on manual organ segmentation, but we have developed deep-learning models to segment the bladder, rectum, and sigmoid colon automatically, organs of particular importance for cervical cancer HDRBT. In Chapter 4, I present integration of the organ segmentation modules and other modules into the AutoBrachy system for clinical use to automate the planning process. Chapter 5 presents a deep-learning based method that can predict the physician's preference for a patient-specific treatment plan as defined by EQD2 to the bladder, rectum, sigmoid, and CTV D90. This method will serve as a guide in the future to automatically create patient-specific physician preferred treatment plans. Chapter 6 reports the study analyzing benefits from the use of joint intracavitary and interstitial HDRBT. Finally, Chapter 7 concludes the dissertation with discussions and future work."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2152.5/10057","1381370389"],"dc:language":["en"],"dc:subject":["Brachytherapy","Deep Learning","Uterine Cervical Neoplasms"],"dc:title":["Automated Treatment Planning in High Dose-Rate Brachytherapy for Cervical Cancer"],"dc:type":["Thesis","text"]},"updated_at":"2026-07-24T05:52:24Z"}