Charit Tippareddy1, Louisa Onyewadume2, Andrew E Sloan3, Gi-Ming Wang4, Nirav T Patil5, Siyuan Hu5, Jill S Barnholtz-Sloan6,7, Rasim Boyacıoğlu1, Vikas Gulani8, Jeffrey Sunshine1, Mark Griswold1,5, Dan Ma1, Chaitra Badve9. 1. Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA. 2. Department of Neurosurgery, West Virginia University Health Sciences Center, Morgantown, WV, USA. 3. Departments of Neurosurgery and Pathology, Seidman Cancer Center and Case Comprehensive Cancer Center, Case Western Reserve University, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. 4. Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Research and Education Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. 5. Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA. 6. Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA. 7. Trans-Divisional Research Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA. 8. Department of Radiology, Michigan Institute of Imaging Technology and Translation, Michigan Medicine, Ann Arbor, MI, USA. 9. Department of Radiology, Case Western Reserve University and University Hospitals Cleveland Medical Center, Seidman Cancer Center and Case Comprehensive Cancer Center, 11100 Euclid Ave, Cleveland, OH, 44106, USA. chaitra.badve@uhhospitals.org.
Abstract
OBJECTIVES: To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. METHODS: 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. RESULTS: Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). CONCLUSION: Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. KEY POINTS: • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.
OBJECTIVES: To test the feasibility of using 3D MRF maps with radiomics analysis and machine learning in the characterization of adult brain intra-axial neoplasms. METHODS: 3D MRF acquisition was performed on 78 patients with newly diagnosed brain tumors including 33 glioblastomas (grade IV), 6 grade III gliomas, 12 grade II gliomas, and 27 patients with brain metastases. Regions of enhancing tumor, non-enhancing tumor, and peritumoral edema were segmented and radiomics analysis with gray-level co-occurrence matrices and gray-level run-length matrices was performed. Statistical analysis was performed to identify features capable of differentiating tumors based on type, grade, and isocitrate dehydrogenase (IDH1) status. Receiver operating curve analysis was performed and the area under the curve (AUC) was calculated for tumor classification and grading. For gliomas, Kaplan-Meier analysis for overall survival was performed using MRF T1 features from enhancing tumor region. RESULTS: Multiple MRF T1 and T2 features from enhancing tumor region were capable of differentiating glioblastomas from brain metastases. Although no differences were identified between grade 2 and grade 3 gliomas, differentiation between grade 2 and grade 4 gliomas as well as between grade 3 and grade 4 gliomas was achieved. MRF radiomics features were also able to differentiate IDH1 mutant from the wild-type gliomas. Radiomics T1 features for enhancing tumor region in gliomas correlated to overall survival (p < 0.05). CONCLUSION: Radiomics analysis of 3D MRF maps allows differentiating glioblastomas from metastases and is capable of differentiating glioblastomas from metastases and characterizing gliomas based on grade, IDH1 status, and survival. KEY POINTS: • 3D MRF data analysis using radiomics offers novel tissue characterization of brain tumors. • 3D MRF with radiomics offers glioma characterization based on grade, IDH1 status, and overall patient survival.
Authors: Charlie Yi Wang; Simone Coppo; Bhairav Bipin Mehta; Nicole Seiberlich; Xin Yu; Mark Alan Griswold Journal: Magn Reson Med Date: 2018-10-30 Impact factor: 4.668
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