Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images-Reference-Cited by-同舟云学术

Deep Radiogenomics of Lower-Grade Gliomas: Convolutional Neural Networks Predict Tumor Genomic Subtypes Using MR Images

Published:2020-01-01 Issue:1 Volume:2 Page:e180050
ISSN:2638-6100
Container-title:Radiology: Artificial Intelligence
language:en
Short-container-title:Radiology: Artificial Intelligence

Author:

Buda Mateusz¹^ORCID,AlBadawy Ehab A.¹^ORCID,Saha Ashirbani¹^ORCID,Mazurowski Maciej A.¹^ORCID

Affiliation:

1. From the Department of Radiology, Duke University School of Medicine, 2424 Erwin Rd, Suite 302, Durham, NC 27705 (M.B., E.A.A., A.S., M.A.M.); Department of Electrical and Computer Engineering, Duke University, Durham, NC (M.A.M.); and Department of Biostatistics and Bioinformatics, Duke University, Durham, NC (M.A.M.).

Publisher

Radiological Society of North America (RSNA)

Subject

General Medicine

Link

http://pubs.rsna.org/doi/pdf/10.1148/ryai.2019180050

Reference23 articles.

1. Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas

2. Genomic profiling of lower-grade gliomas uncovers cohesive disease groups: implications for diagnosis and treatment

3. Glioma Groups Based on 1p/19q,IDH, andTERTPromoter Mutations in Tumors

4. Radiogenomics: What It Is and Why It Is Important

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