Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images

scientific article published on 07 February 2020

Better efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.1186/S12883-020-1613-Y
P932PMC publication ID7007642
P698PubMed publication ID32033580

P50authorWen WangQ59208368
P2093author name stringJie Zhang
Yu Han
Qiang Tian
Li Mao
Sha-Sha Zhao
Guang-Bin Cui
Lin-Feng Yan
Yu-Chuan Hu
Xiu-Li Li
Ying-Zhi Sun
Shu-Ning Shen
Si-Chao Cheng
Xiang-Wei Ge
Xiu-Long Feng
P2860cites workDecoding tumour phenotype by noninvasive imaging using a quantitative radiomics approachQ23760721
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A t(1;19)(q10;p10) mediates the combined deletions of 1p and 19q and predicts a better prognosis of patients with oligodendrogliomaQ34574621
Machine Learning methods for Quantitative Radiomic BiomarkersQ35960448
Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings.Q36173696
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Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomasQ38237785
Low-grade and anaplastic oligodendroglioma.Q38763032
ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images.Q38923628
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Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samplesQ44492730
Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imagingQ44660901
Disrupted white matter connectivity underlying developmental dyslexia: A machine learning approach.Q45952341
Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected].Q47200237
Tired in the Reading Room: The Influence of Fatigue in RadiologyQ47854325
Magnetic resonance perfusion and permeability imaging in brain tumorsQ48390514
Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for SuccessQ48501128
Distraction in diagnostic radiology: How is search through volumetric medical images affected by interruptions?Q50854178
Primary central nervous system lymphoma and atypical glioblastoma: Differentiation using radiomics approach.Q52601610
MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response after Neoadjuvant Therapy.Q52669254
Radiomics strategy for glioma grading using texture features from multiparametric MRI.Q55457404
Arterial spin-labeling and MR spectroscopy in the differentiation of gliomas.Q55467738
Glioma Grading Using a Machine-Learning Framework Based on Optimized Features Obtained From T Perfusion MRI and Volumes of Tumor ComponentsQ62488967
Emerging Applications of Artificial Intelligence in Neuro-OncologyQ62491509
Radiomics Analysis for Glioma Malignancy Evaluation Using Diffusion Kurtosis and Tensor ImagingQ92175393
Imaging biomarker analysis of advanced multiparametric MRI for glioma gradingQ92610731
P4510describes a project that usesmachine learningQ2539
P433issue1
P921main subjectmachine learningQ2539
P304page(s)48
P577publication date2020-02-07
P1433published inBMC NeurologyQ15763734
P1476titleBetter efficacy in differentiating WHO grade II from III oligodendrogliomas with machine-learning than radiologist's reading from conventional T1 contrast-enhanced and fluid attenuated inversion recovery images
P478volume20

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