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Items: 1 to 20 of 99

1.

Automated Machine Learning Based on Radiomics Features Predicts H3 K27M Mutation in Midline Gliomas of the Brain.

Su X, Chen N, Sun H, Liu Y, Yang X, Wang W, Zhang S, Tan Q, Su J, Gong Q, Yue Q.

Neuro Oncol. 2019 Sep 29. pii: noz184. doi: 10.1093/neuonc/noz184. [Epub ahead of print]

PMID:
31563963
2.

Differentiation between spinal cord diffuse midline glioma with histone H3 K27M mutation and wild type: comparative magnetic resonance imaging.

Jung JS, Choi YS, Ahn SS, Yi S, Kim SH, Lee SK.

Neuroradiology. 2019 Mar;61(3):313-322. doi: 10.1007/s00234-019-02154-8. Epub 2019 Jan 20.

PMID:
30662997
3.

Incidence and clinicopathologic features of H3 K27M mutations in adults with radiographically-determined midline gliomas.

Schreck KC, Ranjan S, Skorupan N, Bettegowda C, Eberhart CG, Ames HM, Holdhoff M.

J Neurooncol. 2019 May;143(1):87-93. doi: 10.1007/s11060-019-03134-x. Epub 2019 Mar 12.

4.

Noninvasive assessment of H3 K27M mutational status in diffuse midline gliomas by using apparent diffusion coefficient measurements.

Chen H, Hu W, He H, Yang Y, Wen G, Lv X.

Eur J Radiol. 2019 May;114:152-159. doi: 10.1016/j.ejrad.2019.03.006. Epub 2019 Mar 14.

PMID:
31005167
5.

Imaging Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3 K27M Mutation.

Aboian MS, Solomon DA, Felton E, Mabray MC, Villanueva-Meyer JE, Mueller S, Cha S.

AJNR Am J Neuroradiol. 2017 Apr;38(4):795-800. doi: 10.3174/ajnr.A5076. Epub 2017 Feb 9.

6.

Transcriptomic and epigenetic profiling of 'diffuse midline gliomas, H3 K27M-mutant' discriminate two subgroups based on the type of histone H3 mutated and not supratentorial or infratentorial location.

Castel D, Philippe C, Kergrohen T, Sill M, Merlevede J, Barret E, Puget S, Sainte-Rose C, Kramm CM, Jones C, Varlet P, Pfister SM, Grill J, Jones DTW, Debily MA.

Acta Neuropathol Commun. 2018 Nov 5;6(1):117. doi: 10.1186/s40478-018-0614-1.

7.

H3 K27M-mutant diffuse midline gliomas in different anatomical locations.

Wang L, Li Z, Zhang M, Piao Y, Chen L, Liang H, Wei Y, Hu Z, Zhao L, Teng L, Lu D.

Hum Pathol. 2018 Aug;78:89-96. doi: 10.1016/j.humpath.2018.04.015. Epub 2018 May 1.

PMID:
29727696
8.

Imaging characteristics of adult H3 K27M-mutant gliomas.

Qiu T, Chanchotisatien A, Qin Z, Wu J, Du Z, Zhang X, Gong F, Yao Z, Chu S.

J Neurosurg. 2019 Nov 15:1-9. doi: 10.3171/2019.9.JNS191920. [Epub ahead of print]

PMID:
31731269
9.

Diffusion Characteristics of Pediatric Diffuse Midline Gliomas with Histone H3-K27M Mutation Using Apparent Diffusion Coefficient Histogram Analysis.

Aboian MS, Tong E, Solomon DA, Kline C, Gautam A, Vardapetyan A, Tamrazi B, Li Y, Jordan CD, Felton E, Weinberg B, Braunstein S, Mueller S, Cha S.

AJNR Am J Neuroradiol. 2019 Nov;40(11):1804-1810. doi: 10.3174/ajnr.A6302. Epub 2019 Nov 6.

10.

Diffuse high-grade gliomas with H3 K27M mutations carry a dismal prognosis independent of tumor location.

Karremann M, Gielen GH, Hoffmann M, Wiese M, Colditz N, Warmuth-Metz M, Bison B, Claviez A, van Vuurden DG, von Bueren AO, Gessi M, Kühnle I, Hans VH, Benesch M, Sturm D, Kortmann RD, Waha A, Pietsch T, Kramm CM.

Neuro Oncol. 2018 Jan 10;20(1):123-131. doi: 10.1093/neuonc/nox149.

11.

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning (AutoML).

Orlenko A, Kofink D, Lyytikäinen LP, Nikus K, Mishra P, Kuukasjärvi P, Karhunen PJ, Kähönen M, Laurikka JO, Lehtimäki T, Asselberg FW, Moore JH.

Bioinformatics. 2019 Nov 8. pii: btz796. doi: 10.1093/bioinformatics/btz796. [Epub ahead of print]

PMID:
31702773
12.

Scaling tree-based automated machine learning to biomedical big data with a feature set selector.

Le TT, Fu W, Moore JH.

Bioinformatics. 2020 Jan 1;36(1):250-256. doi: 10.1093/bioinformatics/btz470.

13.

Classification of the glioma grading using radiomics analysis.

Cho HH, Lee SH, Kim J, Park H.

PeerJ. 2018 Nov 22;6:e5982. doi: 10.7717/peerj.5982. eCollection 2018.

14.

High frequency of H3 K27M mutations in adult midline gliomas.

Ebrahimi A, Skardelly M, Schuhmann MU, Ebinger M, Reuss D, Neumann M, Tabatabai G, Kohlhof-Meinecke P, Schittenhelm J.

J Cancer Res Clin Oncol. 2019 Apr;145(4):839-850. doi: 10.1007/s00432-018-02836-5. Epub 2019 Jan 4.

PMID:
30610375
15.

Co-occurrence of histone H3 K27M and BRAF V600E mutations in paediatric midline grade I ganglioglioma.

Pagès M, Beccaria K, Boddaert N, Saffroy R, Besnard A, Castel D, Fina F, Barets D, Barret E, Lacroix L, Bielle F, Andreiuolo F, Tauziède-Espariat A, Figarella-Branger D, Puget S, Grill J, Chrétien F, Varlet P.

Brain Pathol. 2018 Jan;28(1):103-111. doi: 10.1111/bpa.12473. Epub 2017 Feb 8.

PMID:
27984673
16.

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.

Park YW, Choi YS, Ahn SS, Chang JH, Kim SH, Lee SK.

Korean J Radiol. 2019 Sep;20(9):1381-1389. doi: 10.3348/kjr.2018.0814.

17.

An investigation of machine learning methods in delta-radiomics feature analysis.

Chang Y, Lafata K, Sun W, Wang C, Chang Z, Kirkpatrick JP, Yin FF.

PLoS One. 2019 Dec 13;14(12):e0226348. doi: 10.1371/journal.pone.0226348. eCollection 2019.

18.

Diffusion tensor imaging radiomics in lower-grade glioma: improving subtyping of isocitrate dehydrogenase mutation status.

Park CJ, Choi YS, Park YW, Ahn SS, Kang SG, Chang JH, Kim SH, Lee SK.

Neuroradiology. 2019 Dec 9. doi: 10.1007/s00234-019-02312-y. [Epub ahead of print]

PMID:
31820065
19.

Brainstem pilocytic astrocytoma with H3 K27M mutation: case report.

Morita S, Nitta M, Muragaki Y, Komori T, Masui K, Maruyama T, Ichimura K, Nakano Y, Sawada T, Koriyama S, Tsuzuki S, Yasuda T, Hashimoto K, Niwa A, Kawamata T.

J Neurosurg. 2018 Sep;129(3):593-597. doi: 10.3171/2017.4.JNS162443. Epub 2017 Sep 29.

PMID:
28960151
20.

Radiomics-based machine learning methods for isocitrate dehydrogenase genotype prediction of diffuse gliomas.

Wu S, Meng J, Yu Q, Li P, Fu S.

J Cancer Res Clin Oncol. 2019 Mar;145(3):543-550. doi: 10.1007/s00432-018-2787-1. Epub 2019 Feb 4.

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