ADC texture--an imaging biomarker for high-grade glioma? (scientific article published in October 2014)

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Principal component analysis

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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification

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P433

issue

P407

language of work or name

English

Q1860

P921

main subject

biomarker

Q864574

P304

page(s)

101903

P577

publication date

2014-10-01

P1433

published in

Medical Physics

Q15764251

P1476

title

ADC texture--an imaging biomarker for high-grade glioma?

P478

volume

Reverse relations

cites work (P2860)

Q39817583	Characterization of the serum metabolome following radiation treatment in patients with high-grade gliomas
Q89629807	Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades
Q31132701	Detecting tumor progression in glioma: Current standards and new techniques.
Q47830783	Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters analysis (DTPA): A feasibility study.
Q41654243	Evaluation of pseudoprogression rates and tumor progression patterns in a phase III trial of bevacizumab plus radiotherapy/temozolomide for newly diagnosed glioblastoma
Q38397651	Functional imaging for radiotherapy treatment planning: current status and future directions-a review
Q33702758	Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.
Q64114868	Gray-level invariant Haralick texture features
Q41063802	Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters
Q92850355	High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management
Q55229702	MRI texture analysis in predicting treatment response to neoadjuvant chemoradiotherapy in rectal cancer.
Q97418633	Machine learning assisted DSC-MRI radiomics as a tool for glioma classification by grade and mutation status
Q38997187	Medical Imaging Informatics
Q38733841	Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features
Q58079185	Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Q47124309	Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging
Q55340974	Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest.
Q39019381	Texture analysis of medical images for radiotherapy applications
Q52582028	Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study.
Q41017618	Tumor spatial heterogeneity in myxoid-containing soft tissue using texture analysis of diffusion-weighted MRI.
Q53080016	Whole-Tumor Histogram and Texture Analyses of DTI for Evaluation of IDH1-Mutation and 1p/19q-Codeletion Status in World Health Organization Grade II Gliomas.