{"id":1915,"date":"2021-07-12T19:09:50","date_gmt":"2021-07-12T19:09:50","guid":{"rendered":"https:\/\/sites.wp.odu.edu\/VisionLab\/?page_id=1915"},"modified":"2023-03-15T14:08:00","modified_gmt":"2023-03-15T14:08:00","slug":"prediction-of-molecular-mutations-in-diffuse-low-grade-gliomas-using-mr-imaging-features","status":"publish","type":"page","link":"https:\/\/sites.wp.odu.edu\/VisionLab\/research\/prediction-of-molecular-mutations-in-diffuse-low-grade-gliomas-using-mr-imaging-features\/","title":{"rendered":"Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features"},"content":{"rendered":"<p>Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require<br \/> invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas<br \/> non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of<br \/> low-grade gliomas using imaging features based on the updated classification. We introduce molecular<br \/> (MGMT methylation, IDH mutation, 1p\/19q co-deletion, ATRX mutation, and TERT mutations) prediction<br \/> methods of low-grade gliomas with imaging. Imaging features are extracted from magnetic resonance<br \/> imaging data and include texture features, fractal and multi-resolution fractal texture features, and<br \/> volumetric features. Training models include nested leave-one-out cross-validation to select features,<br \/> train the model, and estimate model performance. The prediction models of MGMT methylation, IDH<br \/> mutations, 1p\/19q co-deletion, ATRX mutation, and TERT mutations achieve a test performance AUC of<br \/> 0.83\u00b10.04, 0.84\u00b10.03, 0.80\u00b10.04, 0.70\u00b10.09, and 0.82\u00b10.04, respectively. Furthermore, our analysis<br \/> shows that the fractal features have a significant effect on the predictive performance of MGMT<br \/> methylation IDH mutations, 1p\/19q co-deletion, and ATRX mutations. The performance of our<br \/> prediction methods indicates the potential of correlating computed imaging features with LGG<br \/> molecular mutations types and identifies candidates that may be considered potential predictive<br \/> biomarkers of LGG molecular classification.<\/p> <p><a href=\"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-content\/uploads\/sites\/1499\/2021\/07\/Zeina-poster-1.png\"><br \/> <img decoding=\"async\" class=\"alignnone size-full wp-image-883\" src=\"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-content\/uploads\/sites\/1499\/2021\/07\/Zeina-poster-1.png\"><br \/> <\/a><br \/> click image to enlarge<br \/> <a href=\"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-content\/uploads\/sites\/1499\/2021\/07\/Zeina-poster-2.png\"><br \/> <img decoding=\"async\" class=\"alignnone size-full wp-image-883\" src=\"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-content\/uploads\/sites\/1499\/2021\/07\/Zeina-poster-2.png\"><br \/> <\/a><br \/> click image to enlarge<\/p> ","protected":false},"excerpt":{"rendered":"<p>Diffuse low-grade gliomas (LGG) have been reclassified based on molecular mutations, which require invasive tumor tissue sampling. Tissue sampling by biopsy may be limited by sampling error, whereas non-invasive imaging can evaluate the entirety of a tumor. This study presents a non-invasive analysis of low-grade gliomas using imaging features based on the updated classification. We [&hellip;]<\/p>\n","protected":false},"author":5165,"featured_media":0,"parent":35,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/pages\/1915"}],"collection":[{"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/users\/5165"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/comments?post=1915"}],"version-history":[{"count":4,"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/pages\/1915\/revisions"}],"predecessor-version":[{"id":1935,"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/pages\/1915\/revisions\/1935"}],"up":[{"embeddable":true,"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/pages\/35"}],"wp:attachment":[{"href":"https:\/\/sites.wp.odu.edu\/VisionLab\/wp-json\/wp\/v2\/media?parent=1915"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}