Ghasem Hajianfar1, Isaac Shiri2, Hassan Maleki3, Niki Oveisi4, Abbas Haghparast5, Hamid Abdollahi6, Mehrdad Oveisi7. 1. Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran; Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran. 2. Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, Geneva, Switzerland. 3. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran. 4. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada. 5. Department of Medical Physics, Faculty of Medicine, Kermanshah University of Medical Sciences, Kermanshah, Iran. 6. Department of Radiologic Sciences and Medical Physics, Faculty of Allied Medicine, Kerman University of Medical Sciences, Kerman, Iran. 7. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Department of Computer Science, University of British Columbia, Vancouver, BC, Canada.
Abstract
BACKGROUND: This study aimed to predict methylation status of the O6 methylguanine-DNA methyltransferase (MGMT) gene promoter status by using magnetic resonance imaging radiomics features, as well as univariate and multivariate analysis. METHODS: Eighty-two patients who had an MGMT methylation status were included in this study. Tumors were manually segmented in the 4 regions of magnetic resonance images, 1) whole tumor, 2) active/enhanced region, 3) necrotic regions, and 4) edema regions. About 7000 radiomics features were extracted for each patient. Feature selection and classifier were used to predict MGMT status through different machine learning algorithms. The area under the curve (AUC) of the receiver operating characteristic curve was used for model evaluations. RESULTS: Regarding univariate analysis, the Inverse Variance feature From Gray Level Co-occurrence Matrix in whole tumor segment with 4.5 mm Sigma of Laplacian of Gaussian filter with AUC of 0.71 (P value = 0.002) was found to be the best predictor. For multivariate analysis, the Decision Tree classifier with Select from Model feature selector and LOG (Laplacian of Gaussian) filter in edema region had the highest performance (AUC, 0.78), followed by Ada-Boost classifier with Select from Model feature selector and LOG filter in edema region (AUC, 0.74). CONCLUSIONS: This study showed that radiomics using machine learning algorithms is a feasible noninvasive approach to predict MGMT methylation status in patients with glioblastoma multiforme cancer.
BACKGROUND: This study aimed to predict methylation status of the O6 methylguanine-DNA methyltransferase (MGMT) gene promoter status by using magnetic resonance imaging radiomics features, as well as univariate and multivariate analysis. METHODS: Eighty-two patients who had an MGMT methylation status were included in this study. Tumors were manually segmented in the 4 regions of magnetic resonance images, 1) whole tumor, 2) active/enhanced region, 3) necrotic regions, and 4) edema regions. About 7000 radiomics features were extracted for each patient. Feature selection and classifier were used to predict MGMT status through different machine learning algorithms. The area under the curve (AUC) of the receiver operating characteristic curve was used for model evaluations. RESULTS: Regarding univariate analysis, the Inverse Variance feature From Gray Level Co-occurrence Matrix in whole tumor segment with 4.5 mm Sigma of Laplacian of Gaussian filter with AUC of 0.71 (P value = 0.002) was found to be the best predictor. For multivariate analysis, the Decision Tree classifier with Select from Model feature selector and LOG (Laplacian of Gaussian) filter in edema region had the highest performance (AUC, 0.78), followed by Ada-Boost classifier with Select from Model feature selector and LOG filter in edema region (AUC, 0.74). CONCLUSIONS: This study showed that radiomics using machine learning algorithms is a feasible noninvasive approach to predict MGMT methylation status in patients with glioblastoma multiforme cancer.
Authors: Elizabeth P V Le; Leonardo Rundo; Jason M Tarkin; Nicholas R Evans; Mohammed M Chowdhury; Patrick A Coughlin; Holly Pavey; Chris Wall; Fulvio Zaccagna; Ferdia A Gallagher; Yuan Huang; Rouchelle Sriranjan; Anthony Le; Jonathan R Weir-McCall; Michael Roberts; Fiona J Gilbert; Elizabeth A Warburton; Carola-Bibiane Schönlieb; Evis Sala; James H F Rudd Journal: Sci Rep Date: 2021-02-10 Impact factor: 4.379
Authors: Lin Lu; Firas S Ahmed; Oguz Akin; Lyndon Luk; Xiaotao Guo; Hao Yang; Jin Yoon; A Aari Hakimi; Lawrence H Schwartz; Binsheng Zhao Journal: Front Oncol Date: 2021-05-27 Impact factor: 6.244