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Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 10, p e0164268 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
Abstract
- Reports have suggested that tumor textures presented on T2-weighted images correlate with the genetic status of glioma. Therefore, development of an image analyzing framework that is capable of objective and high throughput image texture analysis for large scale image data collection is needed. The current study aimed to address the development of such a framework by introducing two novel parameters for image textures on T2-weighted images, i.e., Shannon entropy and Prewitt filtering. Twenty-two WHO grade 2 and 28 grade 3 glioma patients were collected whose pre-surgical MRI and IDH1 mutation status were available. Heterogeneous lesions showed statistically higher Shannon entropy than homogenous lesions (p = 0.006) and ROC curve analysis proved that Shannon entropy on T2WI was a reliable indicator for discrimination of homogenous and heterogeneous lesions (p = 0.015, AUC = 0.73). Lesions with well-defined borders exhibited statistically higher Edge mean and Edge median values using Prewitt filtering than those with vague lesion borders (p = 0.0003 and p = 0.0005 respectively). ROC curve analysis also proved that both Edge mean and median values were promising indicators for discrimination of lesions with vague and well defined borders and both Edge mean and median values performed in a comparable manner (p = 0.0002, AUC = 0.81 and p < 0.0001, AUC = 0.83, respectively). Finally, IDH1 wild type gliomas showed statistically lower Shannon entropy on T2WI than IDH1 mutated gliomas (p = 0.007) but no difference was observed between IDH1 wild type and mutated gliomas in Edge median values using Prewitt filtering. The current study introduced two image metrics that reflect lesion texture described on T2WI. These two metrics were validated by readings of a neuro-radiologist who was blinded to the results. This observation will facilitate further use of this technique in future large scale image analysis of glioma.
- Subjects :
- Male
Pathology
Gene Identification and Analysis
lcsh:Medicine
Contrast Media
Pathology and Laboratory Medicine
Grayscale
Diagnostic Radiology
0302 clinical medicine
Image texture
Medicine and Health Sciences
lcsh:Science
Neurological Tumors
Throughput (business)
Mathematics
Aged, 80 and over
Multidisciplinary
medicine.diagnostic_test
Brain Neoplasms
Texture (cosmology)
Radiology and Imaging
Glioma
Middle Aged
Magnetic Resonance Imaging
Oncology
Neurology
030220 oncology & carcinogenesis
Physical Sciences
Female
Statistics (Mathematics)
Research Article
Adult
medicine.medical_specialty
Imaging Techniques
Image Analysis
Research and Analysis Methods
Young Adult
03 medical and health sciences
Signs and Symptoms
Diagnostic Medicine
Prewitt operator
Image Interpretation, Computer-Assisted
Confidence Intervals
Genetics
medicine
Humans
Mutation Detection
Aged
business.industry
lcsh:R
Cancers and Neoplasms
Biology and Life Sciences
Pattern recognition
Magnetic resonance imaging
Image Enhancement
medicine.disease
Confidence interval
ROC Curve
Lesions
lcsh:Q
Artificial intelligence
business
030217 neurology & neurosurgery
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 11
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....ea793c701a52e68ba6cf9ed079952735