Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity.
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Detecting tumor progression in glioma: Current standards and new techniques.Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences.Geometrical Measures Obtained from Pretreatment Postcontrast T1 Weighted MRIs Predict Survival Benefits from Bevacizumab in Glioblastoma PatientsIntroduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas.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.Utility of histogram analysis of ADC maps for differentiating orbital tumors.Large-volume low apparent diffusion coefficient lesions predict poor survival in bevacizumab-treated glioblastoma patients.Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy.Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features.Radiomics: a new application from established techniques.Enhancing tumor apparent diffusion coefficient histogram skewness stratifies the postoperative survival in recurrent glioblastoma multiforme patients undergoing salvage surgery.Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parametersTumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival.Whole-lesion apparent diffusion coefficient histogram analysis: significance in T and N staging of gastric cancers.The link between diffusion MRI and tumor heterogeneity: Mapping cell eccentricity and density by diffusional variance decomposition (DIVIDE).Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging.Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT.Advanced and amplified BOLD fluctuations in high-grade gliomas.Differentiating enhancing multiple sclerosis lesions, glioblastoma, and lymphoma with dynamic texture parameters analysis (DTPA): A feasibility study.Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas.Radiomic features predict Ki-67 expression level and survival in lower grade gliomas.Risk stratification of gallbladder polyps larger than 10 mm using high-resolution ultrasonography and texture analysis.Apparent diffusion coefficient and arterial spin labeling perfusion of conventional chondrosarcoma in the parafalcine region: a case report.Quantitative Sonographic Texture Analysis in Preterm Neonates With White Matter Injury: Correlation of Texture Features With White Matter Injury Severity.Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis.Apparent diffusion coefficient measurement in glioma: Influence of region-of-interest determination methods on apparent diffusion coefficient values, interobserver variability, time efficiency, and diagnostic ability.Could texture features from preoperative CT image be used for predicting occult peritoneal carcinomatosis in patients with advanced gastric cancer?Differentiation of orbital lymphoma and idiopathic orbital inflammatory pseudotumor: combined diagnostic value of conventional MRI and histogram analysis of ADC maps.Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas.Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest.Brain Tumor Characterization Using Multibiometric Evaluation of MRI.
P2860
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P2860
Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity.
description
2014 nî lūn-bûn
@nan
2014 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@ast
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@en
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@nl
type
label
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@ast
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@en
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@nl
prefLabel
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@ast
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@en
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@nl
P2093
P2860
P1433
P1476
Glioma: application of whole-t ...... uation of tumor heterogeneity.
@en
P2093
Chul-Ho Sohn
Ji-Hoon Kim
Sang Joon Park
Tae Jin Yun
Young Jin Ryu
P2860
P304
P356
10.1371/JOURNAL.PONE.0108335
P407
P577
2014-09-30T00:00:00Z