Virendra Kumar Yadav, Suyash Mohan, Sumeet Agarwal, Laiz Laura de Godoy, Archith Rajan, MacLean P Nasrallah, Stephen J Bagley, Steven Brem, Laurie A Loevner, Harish Poptani, Anup Singh, Sanjeev Chawla
{"title":"基于多参数磁共振成像和O6-甲基鸟嘌呤甲基转移酶启动子甲基化状态的机器学习区分胶质母细胞瘤的假性进展和真性进展","authors":"Virendra Kumar Yadav, Suyash Mohan, Sumeet Agarwal, Laiz Laura de Godoy, Archith Rajan, MacLean P Nasrallah, Stephen J Bagley, Steven Brem, Laurie A Loevner, Harish Poptani, Anup Singh, Sanjeev Chawla","doi":"10.1093/noajnl/vdae159","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.</p><p><strong>Methods: </strong>GBM patients (<i>n</i> = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O<sup>6</sup>-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (<i>n</i> = 55) or PsP (<i>n</i> = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.</p><p><strong>Results: </strong>The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.</p><p><strong>Conclusions: </strong>Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.</p>","PeriodicalId":94157,"journal":{"name":"Neuro-oncology advances","volume":"6 1","pages":"vdae159"},"PeriodicalIF":3.7000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535496/pdf/","citationCount":"0","resultStr":"{\"title\":\"Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O<sup>6</sup>-methylguanine-methyltransferase promoter methylation status.\",\"authors\":\"Virendra Kumar Yadav, Suyash Mohan, Sumeet Agarwal, Laiz Laura de Godoy, Archith Rajan, MacLean P Nasrallah, Stephen J Bagley, Steven Brem, Laurie A Loevner, Harish Poptani, Anup Singh, Sanjeev Chawla\",\"doi\":\"10.1093/noajnl/vdae159\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.</p><p><strong>Methods: </strong>GBM patients (<i>n</i> = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O<sup>6</sup>-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (<i>n</i> = 55) or PsP (<i>n</i> = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.</p><p><strong>Results: </strong>The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.</p><p><strong>Conclusions: </strong>Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.</p>\",\"PeriodicalId\":94157,\"journal\":{\"name\":\"Neuro-oncology advances\",\"volume\":\"6 1\",\"pages\":\"vdae159\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11535496/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neuro-oncology advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/noajnl/vdae159\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuro-oncology advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/noajnl/vdae159","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Distinction of pseudoprogression from true progression in glioblastomas using machine learning based on multiparametric magnetic resonance imaging and O6-methylguanine-methyltransferase promoter methylation status.
Background: It is imperative to differentiate true progression (TP) from pseudoprogression (PsP) in glioblastomas (GBMs). We sought to investigate the potential of physiologically sensitive quantitative parameters derived from diffusion and perfusion magnetic resonance imaging (MRI), and molecular signature combined with machine learning in distinguishing TP from PsP in GBMs in the present study.
Methods: GBM patients (n = 93) exhibiting contrast-enhancing lesions within 6 months after completion of standard treatment underwent 3T MRI. Final data analyses were performed on 75 patients as O6-methylguanine-DNA-methyltransferase (MGMT) status was available only from these patients. Subsequently, patients were classified as TP (n = 55) or PsP (n = 20) based on histological features or mRANO criteria. Quantitative parameters were computed from contrast-enhancing regions of neoplasms. PsP datasets were artificially augmented to achieve balanced class distribution in 2 groups (TP and PsP). A random forest algorithm was applied to select the optimized features. The data were randomly split into training and testing subsets in an 8:2 ratio. To develop a robust prediction model in distinguishing TP from PsP, several machine-learning classifiers were employed. The cross-validation and receiver operating characteristic (ROC) curve analyses were performed to determine the diagnostic performance.
Results: The quadratic support vector machine was found to be the best classifier in distinguishing TP from PsP with a training accuracy of 91%, cross-validation accuracy of 86%, and testing accuracy of 85%. Additionally, ROC analysis revealed an accuracy of 85%, sensitivity of 70%, and specificity of 100%.
Conclusions: Machine learning using quantitative multiparametric MRI may be a promising approach to distinguishing TP from PsP in GBMs.
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