{"title":"Characterizing the relationship between MRI radiomics and AHR expression and deriving a predictive model for prognostic assessment in glioblastoma.","authors":"Chen Liu, Dingkang Xu, Limin Meng, Hongqi Li, Zhiguang Fu, Maohui Yan, Xiaolong Hu, Yingjie Wang","doi":"10.1007/s00234-024-03396-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Aryl hydrocarbon receptor (AHR), a crucial molecular marker associated with glioma, is a potential therapeutic target. We aimed to establish a non-invasive predictive model for AHR through radiomics.</p><p><strong>Methods: </strong>Contrast-enhanced T1-weighted (T1W) MRI and the corresponding and clinical variables of glioblastoma patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were obtained for analysis. KM curves and Cox regression analyses were used to assess the prognostic value of AHR expression. The radiomics features were screened by Max-Relevance and Min-Redundancy (mRMR) and recursive feature elimination (RFE), followed by the construction of two predictive models using logistic regression (LR) and a support vector machine (SVM).</p><p><strong>Results: </strong>The expression levels of AHR in tumour patients were significantly higher than those in the control group, and higher AHR expression was associated with worse prognosis (P<0.05). AHR remained a risk factor for poor prognosis in glioblastoma after multivariate adjustment (HR: 1.61, 95% CI: 1.085-2.39, P<0.05). The radiomics models constructed using LR and SVM based on three selected features achieved area under the curve (AUC) values of 0.887 and 0.872, respectively. Radiomics score emerged as a key factor influencing overall survival (OS) after multivariate adjustment in the Cox model (HR: 3.931, 95% CI: 1.272-12.148, P < 0.05).</p><p><strong>Conclusion: </strong>The radiomics models could effectively distinguish the expression levels of AHR and predict prognosis in patients with glioblastoma, which may serve as a powerful tool to assist clinical assessment and precision treatment.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neuroradiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00234-024-03396-x","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/6/19 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Purpose: Aryl hydrocarbon receptor (AHR), a crucial molecular marker associated with glioma, is a potential therapeutic target. We aimed to establish a non-invasive predictive model for AHR through radiomics.
Methods: Contrast-enhanced T1-weighted (T1W) MRI and the corresponding and clinical variables of glioblastoma patients from The Cancer Genome Atlas (TCGA) and The Cancer Imaging Archive (TCIA) were obtained for analysis. KM curves and Cox regression analyses were used to assess the prognostic value of AHR expression. The radiomics features were screened by Max-Relevance and Min-Redundancy (mRMR) and recursive feature elimination (RFE), followed by the construction of two predictive models using logistic regression (LR) and a support vector machine (SVM).
Results: The expression levels of AHR in tumour patients were significantly higher than those in the control group, and higher AHR expression was associated with worse prognosis (P<0.05). AHR remained a risk factor for poor prognosis in glioblastoma after multivariate adjustment (HR: 1.61, 95% CI: 1.085-2.39, P<0.05). The radiomics models constructed using LR and SVM based on three selected features achieved area under the curve (AUC) values of 0.887 and 0.872, respectively. Radiomics score emerged as a key factor influencing overall survival (OS) after multivariate adjustment in the Cox model (HR: 3.931, 95% CI: 1.272-12.148, P < 0.05).
Conclusion: The radiomics models could effectively distinguish the expression levels of AHR and predict prognosis in patients with glioblastoma, which may serve as a powerful tool to assist clinical assessment and precision treatment.
期刊介绍:
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.