A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each feature’s prediction performance. The topology-based model (AUC = 0.855 +/- 0.079) performed significantly better compared to the texture-based model (AUC = 0.707 +/- 0.118) while comparably against the CNN-based model (0.787 +/- 0.195). However, none of the models performed better than the baseline model that is built with only clinical variables, namely, age, gender, and Karnofsky Performance Score (AUC = 0.703 +/- 0.256). In summary, predicting 1p/19q chromosome codeletion status via MRI scan analysis can be a viable non-invasive assessment tool at an early stage of gliomas and in follow-ups although further investigation is needed to improve the model performance.
","abstract_html":"<p>A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each feature’s prediction performance. The topology-based model (AUC = 0.855 +/- 0.079) performed significantly better compared to the texture-based model (AUC = 0.707 +/- 0.118) while comparably against the CNN-based model (0.787 +/- 0.195). However, none of the models performed better than the baseline model that is built with only clinical variables, namely, age, gender, and Karnofsky Performance Score (AUC = 0.703 +/- 0.256). In summary, predicting 1p/19q chromosome codeletion status via MRI scan analysis can be a viable non-invasive assessment tool at an early stage of gliomas and in follow-ups although further investigation is needed to improve the model performance.</p>","abstract_has_math":false,"creators":["Kim, Donnie"],"institution":null,"degree_name":"Masters of Science (MS)","degree_level":"Thesis (MS)","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Arvind Rao","Richard Wendt","Ankit Patel"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-08-01T07:00:00Z","date_published":"2018-08-01T07:00:00Z","updated_at":"2026-07-24T05:49:23Z","subjects":["machine learning","deep learning","magnetic resonance imaging","diffuse glioma","1p/19q codeletion","radiomics","topology","data mining","Bioinformatics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.library.tmc.edu/utgsbs_dissertations/894","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Arvind Rao","Richard Wendt","Ankit Patel"]},{"key":"dc:creator","label":"Author","values":["Kim, Donnie"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2019-08-15T07:00:00Z"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis (MS)"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Masters of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["machine learning","deep learning","magnetic resonance imaging","diffuse glioma","1p/19q codeletion","radiomics","topology","data mining","Bioinformatics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/894"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each feature’s prediction performance. The topology-based model (AUC = 0.855 +/- 0.079) performed significantly better compared to the texture-based model (AUC = 0.707 +/- 0.118) while comparably against the CNN-based model (0.787 +/- 0.195). However, none of the models performed better than the baseline model that is built with only clinical variables, namely, age, gender, and Karnofsky Performance Score (AUC = 0.703 +/- 0.256). In summary, predicting 1p/19q chromosome codeletion status via MRI scan analysis can be a viable non-invasive assessment tool at an early stage of gliomas and in follow-ups although further investigation is needed to improve the model performance.
"]},{"key":"dc:title","label":"Title","values":["Prediction of 1P/19Q Codeletion Status In Diffuse Glioma Patients Using Preoperative Multiparametric Magnetic Resonance Imaging"]}]}],"canonical_facts":{"dc:contributor":["Arvind Rao","Richard Wendt","Ankit Patel"],"dc:creator":["Kim, Donnie"],"dc:date.available":["2019-08-15T07:00:00Z"],"dc:description.abstract":["A complete codeletion of chromosome 1p/19q is strongly correlated with better overall survival of diffuse glioma patients, hence determining the codeletion status early in the course of a patient’s disease would be valuable in that patient’s care. The current practice requires a surgical biopsy in order to assess the codeletion status, which exposes patients to risks and is limited in its accuracy by sampling variations. To overcome such limitations, we utilized four conventional magnetic resonance imaging sequences to predict the 1p/19q status. We extracted three sets of image-derived features, namely texture-based, topology-based, and convolutional neural network (CNN)-based, and analyzed each feature’s prediction performance. The topology-based model (AUC = 0.855 +/- 0.079) performed significantly better compared to the texture-based model (AUC = 0.707 +/- 0.118) while comparably against the CNN-based model (0.787 +/- 0.195). However, none of the models performed better than the baseline model that is built with only clinical variables, namely, age, gender, and Karnofsky Performance Score (AUC = 0.703 +/- 0.256). In summary, predicting 1p/19q chromosome codeletion status via MRI scan analysis can be a viable non-invasive assessment tool at an early stage of gliomas and in follow-ups although further investigation is needed to improve the model performance.
"],"dc:identifier":["https://digitalcommons.library.tmc.edu/utgsbs_dissertations/894"],"dc:subject":["machine learning","deep learning","magnetic resonance imaging","diffuse glioma","1p/19q codeletion","radiomics","topology","data mining","Bioinformatics"],"dc:title":["Prediction of 1P/19Q Codeletion Status In Diffuse Glioma Patients Using Preoperative Multiparametric Magnetic Resonance Imaging"],"thesis:degree_level":["Thesis (MS)"],"thesis:degree_name":["Masters of Science (MS)"]},"updated_at":"2026-07-24T05:49:23Z"}