About: Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain

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About: Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain Goto Sponge NotDistinct Permalink

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scientific article published on 01 March 2020

Attributes	Values
rdf:type	Item
description	wetenschappelijk artikel (nl) artículu científicu espublizáu en marzu de 2020 (ast) scientific article published on 01 March 2020 (en) im März 2020 veröffentlichter wissenschaftlicher Artikel (de) наукова стаття, опублікована 1 березня 2020 (uk)
publication date	wds:Q90348415-FF175E9C-C706-4668-A30E-66B929556072
publication date	2020-03-01 00:00:00Z (xsd:dateTime)
cites work	wds:Q90348415-0E3D5EFE-496D-4FFD-906C-40E8C605A860 wds:Q90348415-1279DEB9-B64D-452E-AE73-53C0DAF37467 wds:Q90348415-15B9F779-5092-4B82-91EE-01A9BF197068 wds:Q90348415-25B8EEE9-837A-424D-B0C4-E4A572A4CBE4 wds:Q90348415-2A5B9A3A-6468-4865-B171-683252F01859 wds:Q90348415-2BA0AD33-E3D4-4CEE-86B6-8A6FFFE38A1D wds:Q90348415-302FF45A-A668-40BA-BE1C-F161FCC1844A wds:Q90348415-37F9A34A-5CA9-4F7C-9401-4AF8CA5A76FC wds:Q90348415-38C4E80B-C70E-4C98-8190-5968CDA7B2C4 wds:Q90348415-41162289-0D99-4E50-9612-CAE6F948940D wds:Q90348415-43E21D8C-4D2B-4C61-860B-D8164775F48F wds:Q90348415-51C59396-9A85-4AEB-B962-44479DDADEFD wds:Q90348415-5B9030DF-876C-4304-8010-165333F227D7 wds:Q90348415-63FCC867-3847-47B7-A603-014A7A9943E5 wds:Q90348415-7346ED9A-BE33-4E17-AD56-36E5B5922F36 wds:Q90348415-7413A7D6-15C1-4177-8E54-7DADA74FDDBA wds:Q90348415-81D4CE4A-2A20-41D4-ABBF-28A235D353FA wds:Q90348415-893B388B-7818-4109-B79D-173846D3E189 wds:Q90348415-9A9E3ED1-8D31-4E2A-9789-BCF6DD238482 wds:Q90348415-B43ED51F-E391-433C-B694-3A43C1C7EDB7 wds:Q90348415-BB019424-A82B-4C10-9288-EE7490AD06F1 wds:Q90348415-C65C132A-D7E9-45AD-89D0-F56391A2D10E wds:Q90348415-D2015616-14FD-4735-95C3-8D2D80AB12E1 wds:Q90348415-DBD84089-353E-4768-AB4A-75A18691F6B6 wds:Q90348415-EB527278-0C9D-46E8-832A-CA5DEC4A13F1 wds:Q90348415-EB9948A3-28EF-4A3B-8E15-3D7855C79746 wds:Q90348415-EE823338-826F-4787-8BE6-32F57F913B91 wds:Q90348415-F77FA219-663C-47A9-AB19-2DDF75FAC86D wds:Q90348415-FC58ED88-E7F1-4B14-BBCB-AD31961D635C
cites work	FSL Radiomics: extracting more information from medical images using advanced feature analysis Radiomics: Images Are More than Pictures, They Are Data User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) Targeted detection of genetic alterations reveal the prognostic impact of H3K27M and MAPK pathway aberrations in paediatric thalamic glioma H3F3A K27M mutations in thalamic gliomas from young adult patients Diffuse Midline Gliomas with Histone H3-K27M Mutation: A Series of 47 Cases Assessing the Spectrum of Morphologic Variation and Associated Genetic Alterations. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. K27M-mutant histone-3 as a novel target for glioma immunotherapy Quantitative texture analysis in the prediction of IDH status in low-grade gliomas. Computational Radiomics System to Decode the Radiographic Phenotype. Diffuse high-grade gliomas with H3 K27M mutations carry a dismal prognosis independent of tumor location. Radiomics-based Assessment of Radiation-induced Lung Injury After Stereotactic Body Radiotherapy Considerations for automated machine learning in clinical metabolic profiling: Altered homocysteine plasma concentration associated with metformin exposure. H3 K27M-mutant gliomas in adults vs. children share similar histological features and adverse prognosis. cIMPACT-NOW update 2: diagnostic clarifications for diffuse midline glioma, H3 K27M-mutant and diffuse astrocytoma/anaplastic astrocytoma, IDH-mutant. Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type Genetics of Glioblastomas in Rare Anatomical Locations: Spinal Cord and Optic Nerve MRI radiomics analysis of molecular alterations in low-grade gliomas. Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation. Reproducibility of F18-FDG PET radiomic features for different cervical tumor segmentation methods, gray-level discretization, and reconstruction algorithms. Pixel classification method in optical coherence tomography for tumor segmentation and its complementary usage with OCT microangiography. Radiomics Strategy for Molecular Subtype Stratification of Lower-Grade Glioma: Detecting IDH and TP53 Mutations Based on Multimodal MRI. Adult Brainstem Gliomas With H3K27M Mutation: Radiology, Pathology, and Prognosis. MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Differentiation between spinal cord diffuse midline glioma with histone H3 K27M mutation and wild type: comparative magnetic resonance imaging
author name string	wds:Q90348415-12ECAA98-831F-454C-8B0B-AB24618BF509 wds:Q90348415-2435671D-8D9F-46D4-863D-12B008612DDA wds:Q90348415-33471996-76E4-4612-9ABD-94DEEE9A0F15 wds:Q90348415-38E17480-2082-4EE5-8E4E-12F4C1AE2947 wds:Q90348415-39CC394B-4E45-4ADA-9019-376D9C50796E wds:Q90348415-3F001EC5-34FE-4F6D-A48F-21182C2001E2 wds:Q90348415-4331E136-670B-495C-954B-6201F7AC8CE6 wds:Q90348415-4CFF8FD0-6128-4FC9-9F52-2A87EF5FF32D wds:Q90348415-EB21196D-1133-4FCE-95DF-2F8B22D41C33 wds:Q90348415-F7711B04-951D-4DF5-AEB2-CFA42800ACCA
author name string	Yanhui Liu Ni Chen Simin Zhang Qiang Yue Weina Wang Huaiqiang Sun Qiaoyue Tan Xiaorui Su Xibiao Yang Jingkai Su
rdfs:label	Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (en) Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (nl)
skos:prefLabel	Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (en) Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (nl)
name	Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (en) Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (nl)
author	wds:Q90348415-4C55CD0C-4B6C-45F6-B961-158150EDB23C
author	Q Gong
title	wds:Q90348415-41308F0F-C096-4168-AB0C-487B5F8750AE
title	Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain (en)
page(s)	wds:Q90348415-EA88EE41-2F78-4FDC-8472-895F4149B20A
page(s)	393-401
instance of	wds:Q90348415-B8029AB7-6E48-477B-8207-7AA6F4CD2298
instance of	scholarly article

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