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MRI-Based Deep-Learning Method for Determining Glioma MGMT Promoter Methylation Status.
Yogananda, C G B; Shah, B R; Nalawade, S S; Murugesan, G K; Yu, F F; Pinho, M C; Wagner, B C; Mickey, B; Patel, T R; Fei, B; Madhuranthakam, A J; Maldjian, J A.
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  • Yogananda CGB; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Shah BR; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Nalawade SS; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Murugesan GK; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Yu FF; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Pinho MC; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Wagner BC; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Mickey B; Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas.
  • Patel TR; Department of Neurological Surgery (B.M., T.R.P.), University of Texas Southwestern Medical Center, Dallas, Texas.
  • Fei B; Department of Bioengineering (B.F.), University of Texas at Dallas, Richardson, Texas.
  • Madhuranthakam AJ; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas.
  • Maldjian JA; From the Advanced Neuroscience Imaging Research Lab (C.G.B.Y., B.R.S., S.S.N., G.K.M., F.F.Y., M.C.P., B.C.W., A.J.M., J.A.M.), Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas joseph.maldjian@utsouthwestern.edu.
AJNR Am J Neuroradiol ; 42(5): 845-852, 2021 May.
Article en En | MEDLINE | ID: mdl-33664111
BACKGROUND AND PURPOSE: O6-Methylguanine-DNA methyltransferase (MGMT) promoter methylation confers an improved prognosis and treatment response in gliomas. We developed a deep learning network for determining MGMT promoter methylation status using T2 weighted Images (T2WI) only. MATERIALS AND METHODS: Brain MR imaging and corresponding genomic information were obtained for 247 subjects from The Cancer Imaging Archive and The Cancer Genome Atlas. One hundred sixty-three subjects had a methylated MGMT promoter. A T2WI-only network (MGMT-net) was developed to determine MGMT promoter methylation status and simultaneous single-label tumor segmentation. The network was trained using 3D-dense-UNets. Three-fold cross-validation was performed to generalize the performance of the networks. Dice scores were computed to determine tumor-segmentation accuracy. RESULTS: The MGMT-net demonstrated a mean cross-validation accuracy of 94.73% across the 3 folds (95.12%, 93.98%, and 95.12%, [SD, 0.66%]) in predicting MGMT methylation status with a sensitivity and specificity of 96.31% [SD, 0.04%] and 91.66% [SD, 2.06%], respectively, and a mean area under the curve of 0.93 [SD, 0.01]. The whole tumor-segmentation mean Dice score was 0.82 [SD, 0.008]. CONCLUSIONS: We demonstrate high classification accuracy in predicting MGMT promoter methylation status using only T2WI. Our network surpasses the sensitivity, specificity, and accuracy of histologic and molecular methods. This result represents an important milestone toward using MR imaging to predict prognosis and treatment response.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Metilasas de Modificación del ADN / Proteínas Supresoras de Tumor / Enzimas Reparadoras del ADN / Aprendizaje Profundo / Glioma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Humans / Male / Middle aged Idioma: En Revista: AJNR Am J Neuroradiol Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Imagen por Resonancia Magnética / Metilasas de Modificación del ADN / Proteínas Supresoras de Tumor / Enzimas Reparadoras del ADN / Aprendizaje Profundo / Glioma Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Adult / Aged / Humans / Male / Middle aged Idioma: En Revista: AJNR Am J Neuroradiol Año: 2021 Tipo del documento: Article Pais de publicación: Estados Unidos