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id="c-sort1" form="facetForm"><option selected="" value="rel">Relevance</option><option value="a-title">A-Z By Title</option><option value="z-title">Z-A By Title</option><option value="a-author">A-Z By Author</option><option value="z-author">Z-A By Author</option><option value="asc">Date Ascending</option><option value="desc">Date Descending</option></select></div></div><input type="hidden" name="start" form="facetForm" value="0"/></div><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/4rs615c9"><div class="c-clientmarkup">Introduction to the Optics and the Brain 2023 feature issue.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3AGibson%2C%20Emily">Gibson, Emily</a>; </li><li><a href="/search/?q=author%3AWang%2C%20Hui">Wang, Hui</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ASrinivasan%2C%20Vivek">Srinivasan, Vivek</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucd_postprints">UC Davis Previously Published Works</a> (2024)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">A feature issue is being presented by a team of guest editors containing papers based on contributed submissions including studies presented at Optics and the Brain, held April 24-27, 2023 as part of Optica Biophotonics Congress: Optics in the Life Sciences, in Vancouver, Canada.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/4rs615c9"><img src="/cms-assets/6902833a2573e5672cc44e0c31447f49a1ec9c2022cddea0f79dcc60dce68c10" alt="Cover page: Introduction to the Optics and the Brain 2023 feature issue."/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/1wz030k5"><div class="c-clientmarkup">Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AMahmoudi%2C%20Keon">Mahmoudi, Keon</a>; </li><li><a href="/search/?q=author%3AKim%2C%20Daniel">Kim, Daniel</a>; </li><li><a href="/search/?q=author%3ATavakkol%2C%20Elham">Tavakkol, Elham</a>; </li><li><a href="/search/?q=author%3AKihira%2C%20Shingo">Kihira, Shingo</a>; </li><li><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3ATsankova%2C%20Nadejda">Tsankova, Nadejda</a>; </li><li><a href="/search/?q=author%3AKhan%2C%20Fahad">Khan, Fahad</a>; </li><li><a href="/search/?q=author%3AHormigo%2C%20Adilia">Hormigo, Adilia</a>; </li><li><a href="/search/?q=author%3AYedavalli%2C%20Vivek">Yedavalli, Vivek</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANael%2C%20Kambiz">Nael, Kambiz</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2024)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup">BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of ≥18 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival ≥ 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival ≥ 18 months in patients with GBM.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/1wz030k5"><img src="/cms-assets/f01eae86ff2856f2add51bd399ca0ee0028c2f67b18d15c3d669476bccdf69d8" alt="Cover page: Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma."/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/80s7v9kh"><div class="c-clientmarkup">Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKihira%2C%20Shingo">Kihira, Shingo</a>; </li><li><a href="/search/?q=author%3ATsankova%2C%20Nadejda">Tsankova, Nadejda</a>; </li><li><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3ASakai%2C%20Yu">Sakai, Yu</a>; </li><li><a href="/search/?q=author%3AMahmoudi%2C%20Keon">Mahmoudi, Keon</a>; </li><li><a href="/search/?q=author%3AZubizarreta%2C%20Nicole">Zubizarreta, Nicole</a>; </li><li><a href="/search/?q=author%3AHouldsworth%2C%20Jane">Houldsworth, Jane</a>; </li><li><a href="/search/?q=author%3AKhan%2C%20Fahad">Khan, Fahad</a>; </li><li><a href="/search/?q=author%3ASalamon%2C%20Noriko">Salamon, Noriko</a>; </li><li><a href="/search/?q=author%3AHormigo%2C%20Adilia">Hormigo, Adilia</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANael%2C%20Kambiz">Nael, Kambiz</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2021)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><h3>Background</h3>Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h3>Methods</h3>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h3>Results</h3>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h3>Conclusion</h3>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/80s7v9kh"><img src="/cms-assets/b23628ad5854dae279b2838ac0f928e0755645fc15f27641a47ff9f029b1d00f" alt="Cover page: Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion"/></a></div></section><section class="c-scholworks"><div class="c-scholworks__main-column"><ul class="c-scholworks__tag-list"><li class="c-scholworks__tag-article">Article</li><li class="c-scholworks__tag-peer">Peer Reviewed</li></ul><div><h3 class="c-scholworks__heading"><a href="/uc/item/02725159"><div class="c-clientmarkup">Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign</div></a></h3></div><div class="c-authorlist"><ul class="c-authorlist__list"><li class="c-authorlist__begin"><a href="/search/?q=author%3AKihira%2C%20Shingo">Kihira, Shingo</a>; </li><li><a href="/search/?q=author%3ADerakhshani%2C%20Ahrya">Derakhshani, Ahrya</a>; </li><li><a href="/search/?q=author%3ALeung%2C%20Michael">Leung, Michael</a>; </li><li><a href="/search/?q=author%3AMahmoudi%2C%20Keon">Mahmoudi, Keon</a>; </li><li><a href="/search/?q=author%3ABauer%2C%20Adam">Bauer, Adam</a>; </li><li><a href="/search/?q=author%3AZhang%2C%20Haoyue">Zhang, Haoyue</a>; </li><li><a href="/search/?q=author%3APolson%2C%20Jennifer">Polson, Jennifer</a>; </li><li><a href="/search/?q=author%3AArnold%2C%20Corey">Arnold, Corey</a>; </li><li><a href="/search/?q=author%3ATsankova%2C%20Nadejda%20M">Tsankova, Nadejda M</a>; </li><li><a href="/search/?q=author%3AHormigo%2C%20Adilia">Hormigo, Adilia</a>; </li><li><a href="/search/?q=author%3ASalehi%2C%20Banafsheh">Salehi, Banafsheh</a>; </li><li><a href="/search/?q=author%3APham%2C%20Nancy">Pham, Nancy</a>; </li><li><a href="/search/?q=author%3AEllingson%2C%20Benjamin%20M">Ellingson, Benjamin M</a>; </li><li><a href="/search/?q=author%3ACloughesy%2C%20Timothy%20F">Cloughesy, Timothy F</a>; </li><li class="c-authorlist__end"><a href="/search/?q=author%3ANael%2C%20Kambiz">Nael, Kambiz</a> </li></ul></div><div class="c-scholworks__publication"><a href="/uc/ucla_postprints">UCLA Previously Published Works</a> (2023)</div><div class="c-scholworks__abstract"><div class="c-clientmarkup"><h3>Purpose</h3>The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+.<h3>Methods</h3>In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets.<h3>Results</h3>A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (<i>p</i> = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (<i>p</i> = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign.<h3>Conclusion</h3>The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.</div></div><div class="c-scholworks__media"><ul class="c-medialist"></ul></div></div><div class="c-scholworks__ancillary"><a class="c-scholworks__thumbnail" href="/uc/item/02725159"><img src="/cms-assets/31bdfd6a2e6122d14e1c6ff7bdb3c6d11fe0a232e2892d2a1653baf53370820d" alt="Cover page: Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign"/></a></div></section></section></main></form></div><div><div class="c-toplink"><a href="javascript:window.scrollTo(0, 0)">Top</a></div><footer class="c-footer"><nav class="c-footer__nav"><ul><li><a href="/">Home</a></li><li><a href="/aboutEschol">About eScholarship</a></li><li><a href="/campuses">Campus Sites</a></li><li><a href="/ucoapolicies">UC Open Access Policy</a></li><li><a href="/publishing">eScholarship Publishing</a></li><li><a href="https://www.cdlib.org/about/accessibility.html">Accessibility</a></li><li><a href="/privacypolicy">Privacy Statement</a></li><li><a href="/policies">Site Policies</a></li><li><a href="/terms">Terms of Use</a></li><li><a href="/login"><strong>Admin Login</strong></a></li><li><a href="https://help.escholarship.org"><strong>Help</strong></a></li></ul></nav><div class="c-footer__logo"><a href="/"><img class="c-lazyimage" data-src="/images/logo_footer-eschol.svg" alt="eScholarship, University of California"/></a></div><div 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Publishing","slug":"publishing","type":"page","url":"/publishing"}],"social":{"facebook":null,"twitter":null,"rss":"/rss/unit/root"},"breadcrumb":[{"name":"eScholarship","id":"root","url":"/"}]},"campuses":[{"id":"","name":"eScholarship at..."},{"id":"ucb","name":"UC Berkeley"},{"id":"ucd","name":"UC Davis"},{"id":"uci","name":"UC Irvine"},{"id":"ucla","name":"UCLA"},{"id":"ucm","name":"UC Merced"},{"id":"ucr","name":"UC Riverside"},{"id":"ucsd","name":"UC San Diego"},{"id":"ucsf","name":"UCSF"},{"id":"ucsb","name":"UC Santa Barbara"},{"id":"ucsc","name":"UC Santa Cruz"},{"id":"ucop","name":"UC Office of the President"},{"id":"lbnl","name":"Lawrence Berkeley National Laboratory"},{"id":"anrcs","name":"UC Agriculture & Natural Resources"}],"query":{"q":"author:Bauer, Adam","sort":"rel","rows":"10","info_start":"0","start":"0","filters":{}},"count":4,"info_count":0,"infoResults":[],"searchResults":[{"id":"qt4rs615c9","title":"Introduction to the Optics and the Brain 2023 feature issue.","abstract":"A feature issue is being presented by a team of guest editors containing papers based on contributed submissions including studies presented at Optics and the Brain, held April 24-27, 2023 as part of Optica Biophotonics Congress: Optics in the Life Sciences, in Vancouver, Canada.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Bauer, Adam","fname":"Adam","lname":"Bauer"},{"name":"Gibson, Emily","fname":"Emily","lname":"Gibson"},{"name":"Wang, Hui","fname":"Hui","lname":"Wang"},{"name":"Srinivasan, Vivek","fname":"Vivek","lname":"Srinivasan"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":199,"asset_id":"6902833a2573e5672cc44e0c31447f49a1ec9c2022cddea0f79dcc60dce68c10","timestamp":1714659404,"image_type":"jpeg"},"pub_year":2024,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UC Davis Previously Published Works","link_path":"ucd_postprints"}},{"id":"qt1wz030k5","title":"Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.","abstract":"BACKGROUND: Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM. METHODS: In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. \u226518 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic model in combination with clinical and histopathological data was inserted into a backward stepwise logistic regression model to assess survival. The diagnostic performance of this model was reported for the training and external validation sets. RESULTS: A total of 116 patients were included for model development and 40 patients for external testing validation. The diagnostic performance (AUC/sensitivity/specificity) of the radiomic model generated from seven texture features in determination of \u226518 months survival was 0.71/69.0/70.3. Three variables remained as independent predictors of survival, including radiomics (p = 0.004), age (p = 0.039), and MGMT status (p = 0.025). This model yielded diagnostic performance (AUC/sensitivity/specificity) of 0.77/81.0/66.0 (training) and 0.89/100/78.6 (testing) in determination of survival \u2265 18 months. CONCLUSIONS: Results show that our radiogenomic model generated from radiomic features at baseline MRI, age, and MGMT status can predict survival \u2265 18 months in patients with GBM.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Mahmoudi, Keon","fname":"Keon","lname":"Mahmoudi"},{"name":"Kim, Daniel","fname":"Daniel","lname":"Kim"},{"name":"Tavakkol, Elham","fname":"Elham","lname":"Tavakkol"},{"name":"Kihira, Shingo","fname":"Shingo","lname":"Kihira"},{"name":"Bauer, Adam","fname":"Adam","lname":"Bauer"},{"name":"Tsankova, Nadejda","fname":"Nadejda","lname":"Tsankova"},{"name":"Khan, Fahad","fname":"Fahad","lname":"Khan"},{"name":"Hormigo, Adilia","fname":"Adilia","lname":"Hormigo"},{"name":"Yedavalli, Vivek","fname":"Vivek","lname":"Yedavalli"},{"name":"Nael, Kambiz","email":"kanael@mednet.ucla.edu","fname":"Kambiz","lname":"Nael"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":177,"asset_id":"f01eae86ff2856f2add51bd399ca0ee0028c2f67b18d15c3d669476bccdf69d8","timestamp":1709052023,"image_type":"png"},"pub_year":2024,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UCLA Previously Published Works","link_path":"ucla_postprints"}},{"id":"qt80s7v9kh","title":"Multiparametric MRI Texture Analysis in Prediction of Glioma Biomarker Status: Added Value of MR Diffusion","abstract":"<h4>Background</h4>Early identification of glioma molecular phenotypes can lead to understanding of patient prognosis and treatment guidance. We aimed to develop a multiparametric MRI texture analysis model using a combination of conventional and diffusion MRI to predict a wide range of biomarkers in patients with glioma.<h4>Methods</h4>In this retrospective study, patients were included if they (1) had diagnosis of gliomas with known <i>IDH1</i>, <i>EGFR</i>, <i>MGMT</i>, <i>ATRX</i>, <i>TP53</i>, and <i>PTEN</i> status from surgical pathology and (2) had preoperative MRI including FLAIR, T1c+ and diffusion for radiomic texture analysis. Statistical analysis included logistic regression and receiver-operating characteristic (ROC) curve analysis to determine the optimal model for predicting glioma biomarkers. A comparative analysis between ROCs (conventional only vs conventional + diffusion) was performed.<h4>Results</h4>From a total of 111 patients included, 91 (82%) were categorized to training and 20 (18%) to test datasets. Constructed cross-validated model using a combination of texture features from conventional and diffusion MRI resulted in overall AUC/accuracy of 1/79% for <i>IDH1</i>, 0.99/80% for <i>ATRX</i>, 0.79/67% for <i>MGMT</i>, and 0.77/66% for <i>EGFR</i>. The addition of diffusion data to conventional MRI features significantly (<i>P</i> < .05) increased predictive performance for <i>IDH1</i>, <i>MGMT</i>, and <i>ATRX</i>. The overall accuracy of the final model in predicting biomarkers in the test group was 80% (<i>IDH1</i>), 70% (<i>ATRX</i>), 70% (<i>MGMT</i>), and 75% (<i>EGFR</i>).<h4>Conclusion</h4>Addition of MR diffusion to conventional MRI features provides added diagnostic value in preoperative determination of IDH1, MGMT, and ATRX in patients with glioma.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Kihira, Shingo","fname":"Shingo","lname":"Kihira"},{"name":"Tsankova, Nadejda","fname":"Nadejda","lname":"Tsankova"},{"name":"Bauer, Adam","fname":"Adam","lname":"Bauer"},{"name":"Sakai, Yu","fname":"Yu","lname":"Sakai"},{"name":"Mahmoudi, Keon","fname":"Keon","lname":"Mahmoudi"},{"name":"Zubizarreta, Nicole","fname":"Nicole","lname":"Zubizarreta"},{"name":"Houldsworth, Jane","fname":"Jane","lname":"Houldsworth"},{"name":"Khan, Fahad","fname":"Fahad","lname":"Khan"},{"name":"Salamon, Noriko","email":"nsalamon@mednet.ucla.edu","fname":"Noriko","lname":"Salamon","ORCID_id":"0000-0002-3520-9467"},{"name":"Hormigo, Adilia","fname":"Adilia","lname":"Hormigo"},{"name":"Nael, Kambiz","email":"kanael@mednet.ucla.edu","fname":"Kambiz","lname":"Nael","ORCID_id":"0000-0002-4194-9488"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":156,"asset_id":"b23628ad5854dae279b2838ac0f928e0755645fc15f27641a47ff9f029b1d00f","timestamp":1632144030,"image_type":"jpeg"},"pub_year":2021,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UCLA Previously Published Works","link_path":"ucla_postprints"}},{"id":"qt02725159","title":"Multi-Parametric Radiomic Model to Predict 1p/19q Co-Deletion in Patients with IDH-1 Mutant Glioma: Added Value to the T2-FLAIR Mismatch Sign","abstract":"<h4>Purpose</h4>The T2-FLAIR mismatch sign has shown promise in determining IDH mutant 1p/19q non-co-deleted gliomas with a high specificity and modest sensitivity. To develop a multi-parametric radiomic model using MRI to predict 1p/19q co-deletion status in patients with newly diagnosed IDH1 mutant glioma and to perform a comparative analysis to T2-FLAIR mismatch sign+.<h4>Methods</h4>In this retrospective study, patients with diagnosis of IDH1 mutant gliomas with known 1p/19q status who had preoperative MRI were included. T2-FLAIR mismatch was evaluated independently by two board-certified neuroradiologists. Texture features were extracted from glioma segmentation of FLAIR images. eXtremeGradient Boosting (XGboost) classifiers were used for model development. Leave-one-out-cross-validation (LOOCV) and external validation performances were reported for both the training and external validation sets.<h4>Results</h4>A total of 103 patients were included for model development and 18 patients for external testing validation. The diagnostic performance (sensitivity/specificity/accuracy) in the determination of the 1p/19q co-deletion status was 59%/83%/67% (training) and 62.5%/70.0%/66.3% (testing) for the T2-FLAIR mismatch sign. This was significantly improved (<i>p</i> = 0.04) using the radiomics model to 77.9%/82.8%/80.3% (training) and 87.5%/89.9%/88.8% (testing), respectively. The addition of radiomics as a computer-assisted tool resulted in significant (<i>p</i> = 0.02) improvement in the performance of the neuroradiologist with 13 additional corrected cases in comparison to just using the T2-FLAIR mismatch sign.<h4>Conclusion</h4>The proposed radiomic model provides much needed sensitivity to the highly specific T2-FLAIR mismatch sign in the determination of the 1p/19q non-co-deletion status and improves the overall diagnostic performance of neuroradiologists when used as an assistive tool.","content_type":"application/pdf","author_hide":null,"authors":[{"name":"Kihira, Shingo","fname":"Shingo","lname":"Kihira"},{"name":"Derakhshani, Ahrya","fname":"Ahrya","lname":"Derakhshani"},{"name":"Leung, Michael","fname":"Michael","lname":"Leung"},{"name":"Mahmoudi, Keon","fname":"Keon","lname":"Mahmoudi"},{"name":"Bauer, Adam","fname":"Adam","lname":"Bauer"},{"name":"Zhang, Haoyue","email":"harryzhangbruins@gmail.com","fname":"Haoyue","lname":"Zhang","ORCID_id":"0000-0002-9412-7584"},{"name":"Polson, Jennifer","fname":"Jennifer","lname":"Polson"},{"name":"Arnold, Corey","email":"cwarnold@mednet.ucla.edu","fname":"Corey","lname":"Arnold","ORCID_id":"0000-0002-4119-8143"},{"name":"Tsankova, Nadejda M","fname":"Nadejda M","lname":"Tsankova"},{"name":"Hormigo, Adilia","fname":"Adilia","lname":"Hormigo"},{"name":"Salehi, Banafsheh","fname":"Banafsheh","lname":"Salehi"},{"name":"Pham, Nancy","fname":"Nancy","lname":"Pham"},{"name":"Ellingson, Benjamin M","email":"bellingson@mednet.ucla.edu","fname":"Benjamin M","lname":"Ellingson"},{"name":"Cloughesy, Timothy F","fname":"Timothy F","lname":"Cloughesy"},{"name":"Nael, Kambiz","email":"kambiz.nael@ucsf.edu","fname":"Kambiz","lname":"Nael","ORCID_id":"0000-0002-4194-9488"}],"supp_files":[{"type":"pdf","count":0},{"type":"image","count":0},{"type":"video","count":0},{"type":"audio","count":0},{"type":"zip","count":0},{"type":"other","count":0}],"thumbnail":{"width":121,"height":177,"asset_id":"31bdfd6a2e6122d14e1c6ff7bdb3c6d11fe0a232e2892d2a1653baf53370820d","timestamp":1689171162,"image_type":"png"},"pub_year":2023,"genre":"article","rights":null,"peerReviewed":true,"unitInfo":{"displayName":"UCLA Previously Published Works","link_path":"ucla_postprints"}}],"facets":[{"display":"Type of Work","fieldName":"type_of_work","facets":[{"value":"article","count":4,"displayName":"Article"},{"value":"monograph","count":0,"displayName":"Book"},{"value":"dissertation","count":0,"displayName":"Theses"},{"value":"multimedia","count":0,"displayName":"Multimedia"}]},{"display":"Peer Review","fieldName":"peer_reviewed","facets":[{"value":"1","count":4,"displayName":"Peer-reviewed only"}]},{"display":"Supplemental Material","fieldName":"supp_file_types","facets":[{"value":"video","count":0,"displayName":"Video"},{"value":"audio","count":0,"displayName":"Audio"},{"value":"images","count":0,"displayName":"Images"},{"value":"zip","count":0,"displayName":"Zip"},{"value":"other files","count":0,"displayName":"Other files"}]},{"display":"Publication Year","fieldName":"pub_year","range":{"pub_year_start":null,"pub_year_end":null}},{"display":"Campus","fieldName":"campuses","facets":[{"value":"ucb","count":0,"displayName":"UC Berkeley"},{"value":"ucd","count":1,"displayName":"UC Davis"},{"value":"uci","count":0,"displayName":"UC Irvine"},{"value":"ucla","count":3,"displayName":"UCLA"},{"value":"ucm","count":0,"displayName":"UC Merced"},{"value":"ucr","count":0,"displayName":"UC Riverside"},{"value":"ucsd","count":0,"displayName":"UC San Diego"},{"value":"ucsf","count":1,"displayName":"UCSF"},{"value":"ucsb","count":0,"displayName":"UC Santa Barbara"},{"value":"ucsc","count":0,"displayName":"UC Santa Cruz"},{"value":"ucop","count":0,"displayName":"UC Office of the President"},{"value":"lbnl","count":0,"displayName":"Lawrence Berkeley National Laboratory"},{"value":"anrcs","count":0,"displayName":"UC Agriculture & Natural Resources"}]},{"display":"Department","fieldName":"departments","facets":[{"value":"ucla_ece","count":1,"displayName":"Electrical and Computer Engineering"}]},{"display":"Journal","fieldName":"journals","facets":[]},{"display":"Discipline","fieldName":"disciplines","facets":[]},{"display":"Reuse License","fieldName":"rights","facets":[]}]};</script> <script src="/js/vendors~app-bundle-7424603c338d723fd773.js"></script> <script src="/js/app-bundle-8362e6d7829414ab4baa.js"></script> </body> </html>