ArticlePloS one2014
Glioma: application of whole-tumor texture analysis of diffusion-weighted imaging for the evaluation of tumor heterogeneity.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 papers, 2 of them syntheses that pooled it.
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Who cites it
88 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of ADC derived from DWI for differentiating high-grade from low-grade gliomas: Systematic review and meta-analysis.Medicine · 2020Pooled it
- Pooled it
- Textural analysis as an instrument to detect differences on sacral chordoma MRI post-radiation therapy.Skeletal radiology · 2026Article
- Histogram analysis of diffusion weighted magnetic resonance imaging for predicting isocitrate dehydrogenase 1 mutation status in brain gliomas.Current journal of neurology · 2025Article
- Comparison Between Visual Assessment and Apparent Diffusion Coefficient (ADC) Value Measurement on Diffusion Weighted Imaging (DWI) in Supratentorial Glioma Grading in Children.Medical archives (Sarajevo, Bosnia and Herzegovina) · 2025Article
- Advancing patient care with AI: a unified framework for medical image segmentation using transfer learning and hybrid feature extraction.Frontiers in medicine · 2025Article
- Multimodal MRI andDiagnostics (Basel, Switzerland) · 2024Article
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- Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer.World journal of radiology · 2023Article
- Performance comparison of different medical image fusion algorithms for clinical glioma grade classification with advanced magnetic resonance imaging (MRI).Scientific reports · 2023Article
- Diagnostic value of MRI radiomics in differentiating high‑grade glioma from low‑grade glioma: A meta‑analysis.Oncology letters · 2023Article
- Texture feature analysis of MRI-ADC images to differentiate glioma grades using machine learning techniques.Scientific reports · 2023Article
- Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging to predict extramural venous invasion in rectal cancer.BMC medical imaging · 2023Article
- Using radiomics based on multicenter magnetic resonance images to predict isocitrate dehydrogenase mutation status of gliomas.Quantitative imaging in medicine and surgery · 2023Article
- Novel 3D magnetic resonance fingerprinting radiomics in adult brain tumors: a feasibility study.European radiology · 2023Article
- The value of convolutional neural networks-based deep learning model in differential diagnosis of space-occupying brain diseases.Frontiers in neurology · 2023Article
- Diagnostic performance of gliomas grading and IDH status decoding A comparison between 3D amide proton transfer APT and four diffusion-weighted MRI models.Journal of magnetic resonance imaging : JMRI · 2022Article
- ADC textural features in patients with single brain metastases improve clinical risk models.Clinical & experimental metastasis · 2022Article
- Texture Analysis in Brain Tumor MR Imaging.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2022Article
- Assessment of Early Response to Lung Cancer Chemotherapy by Semiquantitative Analysis of Dynamic Contrast-Enhanced MRI.Disease markers · 2022Article
28 more citing papers are in PubMed but not listed here.
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
background and purposeTo apply a texture analysis of apparent diffusion coefficient (ADC) maps to evaluate glioma heterogeneity, which was correlated with tumor grade. MATERIALS AND
methodsForty patients with glioma (WHO grade II (n = 8), grade III (n = 10) and grade IV (n = 22)) underwent diffusion-weighted imaging (DWI), and the corresponding ADC maps were obtained. Regions of interest containing the lesions were drawn on every section of the ADC map containing the tumor, and volume-based data of the entire tumor were constructed. Texture and first order features including entropy, skewness and kurtosis were derived from the ADC map using in-house software. A histogram analysis of the ADC map was also performed. The texture and histogram parameters were compared between low-grade and high-grade gliomas using an unpaired student's t-test. Additionally, a one-way analysis of variance analysis with a post-hoc test was performed to compare the parameters of each grade.
resultsEntropy was observed to be significantly higher in high-grade gliomas than low-grade tumors (6.861±0.539 vs. 6.261±0.412, P = 0.006). The fifth percentiles of the ADC cumulative histogram also showed a significant difference between high and low grade gliomas (836±235 vs. 1030±185, P = 0.037). Only entropy proved to be significantly different between grades III and IV (6.295±0.4963 vs. 7.119±0.3165, P<0.001). The diagnostic accuracy of ADC entropy was significantly higher than that of the fifth percentile of the ADC histogram (P = 0.0034) in distinguishing high- from low-grade glioma.
conclusionA texture analysis of the ADC map based on the entire tumor volume can be useful for evaluating glioma grade, which provides tumor heterogeneity.
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