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Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers
by
Haldar, Debanjan
,
Kazerooni, Anahita Fathi
,
Arif, Sherjeel
,
Familiar, Ariana
,
Madhogarhia, Rachel
,
Khalili, Nastaran
,
Bagheri, Sina
,
Anderson, Hannah
,
Shaikh, Ibraheem Salman
,
Mahtabfar, Aria
,
Kim, Meen Chul
,
Tu, Wenxin
,
Ware, Jefferey
,
Vossough, Arastoo
,
Davatzikos, Christos
,
Storm, Phillip B.
,
Resnick, Adam
,
Nabavizadeh, Ali
Published in
Neoplasia (New York, N.Y.)
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Neoplasia
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Biomarkers
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Brain Neoplasms - Diagnostic Imaging
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Brain Neoplasms - Genetics
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Brain Neoplasms - Pathology
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Child
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Glioma - Diagnostic Imaging
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Glioma - Genetics
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Glioma - Metabolism
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Humans
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Life Sciences & Biomedicine
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Magnetic Resonance Imaging - Methods
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Oncology
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Pediatric Brain Tumor
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Pediatric Low-Grade Glioma
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Proto-Oncogene Proteins B-Raf
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Radiogenomics
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Radiomics
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Retrospective Studies
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Science & Technology
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Unsupervised Machine Learning
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