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Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning?

A. Dasgupta and T. Gupta
American Journal of Neuroradiology May 2019, 40 (5) E24-E25; DOI: https://doi.org/10.3174/ajnr.A6040
A. Dasgupta
aDepartment of Radiation Oncology Tata Memorial Centre Mumbai, India
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T. Gupta
aDepartment of Radiation Oncology Tata Memorial Centre Mumbai, India
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Tables

  • Comparison of predictive accuracy defined as AUC on ROC analyses reported in the 2 studies1,4

    Molecular Subgrouping of MedulloblastomaAUC Using Radiomic MRI SignaturesAUC Using Semantic MRI Features
    Double 10-Fold Cross-Validation Model3-Dataset Cross-Validation ModelTraining Cohort (n = 76)Validation Cohort (n = 35)
    WNT0.450.450.750.69
    SHH0.790.700.940.99
    Group 30.700.390.730.60
    Group 40.830.800.850.79
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American Journal of Neuroradiology: 40 (5)
American Journal of Neuroradiology
Vol. 40, Issue 5
1 May 2019
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A. Dasgupta, T. Gupta
Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning?
American Journal of Neuroradiology May 2019, 40 (5) E24-E25; DOI: 10.3174/ajnr.A6040

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Radiogenomics in Medulloblastoma: Can the Human Brain Compete with Artificial Intelligence and Machine Learning?
A. Dasgupta, T. Gupta
American Journal of Neuroradiology May 2019, 40 (5) E24-E25; DOI: 10.3174/ajnr.A6040
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