Quantitative glioma grading using transformed gray-scale invariant textures of MRI

BACKGROUND A computer-aided diagnosis (CAD) system based on intensity-invariant magnetic resonance (MR) imaging features was proposed to grade gliomas for general application to various scanning systems and settings. METHOD In total, 34 glioblastomas and 73 lower-grade gliomas comprised the image database to evaluate the proposed CAD system. For each case, the local texture on MR images was transformed into a local binary pattern (LBP) which was intensity-invariant. From the LBP, quantitative image features, including the histogram moment and textures, were extracted and combined in a logistic regression classifier to establish a malignancy prediction model. The performance was compared to conventional texture features to demonstrate the improvement. RESULTS The performance of the CAD system based on LBP features achieved an accuracy of 93% (100/107), a sensitivity of 97% (33/34), a negative predictive value of 99% (67/68), and an area under the receiver operating characteristic curve (Az) of 0.94, which were significantly better than the conventional texture features: an accuracy of 84% (90/107), a sensitivity of 76% (26/34), a negative predictive value of 89% (64/72), and an Az of 0.89 with respective p values of 0.0303, 0.0122, 0.0201, and 0.0334. CONCLUSIONS More-robust texture features were extracted from MR images and combined into a significantly better CAD system for distinguishing glioblastomas from lower-grade gliomas. The proposed CAD system would be more practical in clinical use with various imaging systems and settings.

[1]  Sang Joon Park,et al.  Glioma: Application of Whole-Tumor Texture Analysis of Diffusion-Weighted Imaging for the Evaluation of Tumor Heterogeneity , 2014, PloS one.

[2]  Leonardo F. Jurado,et al.  Histopathology , 2019, Fungal Infections of the Central Nervous System.

[3]  F. Abdul-Karim,et al.  Interobserver reproducibility among neuropathologists and surgical pathologists in fibrillary astrocytoma grading , 2000, Journal of the Neurological Sciences.

[4]  S.H. Kim,et al.  Peripheral compressing artifacts in brain tissue from stereotactic biopsy with side-cutting biopsy needle: a pitfall for adequate glioma grading , 2011, Clinical neuropathology.

[5]  Ying Liu,et al.  Grading of supratentorial astrocytic tumors by using the difference of ADC value , 2011, Neuroradiology.

[6]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[7]  B. Scheithauer,et al.  The 2007 WHO classification of tumours of the central nervous system , 2007, Acta Neuropathologica.

[8]  Andy P. Field,et al.  Discovering Statistics Using SPSS , 2000 .

[9]  Xavier Lladó,et al.  False Positive Reduction in Mammographic Mass Detection Using Local Binary Patterns , 2007, MICCAI.

[10]  Ronald G Blasberg,et al.  Imaging Update: New Windows, New Views , 2007, Clinical Cancer Research.

[11]  Naoki Kagawa,et al.  Introduction of High Throughput Magnetic Resonance T2-Weighted Image Texture Analysis for WHO Grade 2 and 3 Gliomas , 2016, PloS one.

[12]  Matti Pietikäinen,et al.  Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2000, ECCV.

[13]  Teresa Wu,et al.  Multi-Parametric MRI and Texture Analysis to Visualize Spatial Histologic Heterogeneity and Tumor Extent in Glioblastoma , 2015, PloS one.

[14]  K. Aldape,et al.  Identification of noninvasive imaging surrogates for brain tumor gene-expression modules , 2008, Proceedings of the National Academy of Sciences.

[15]  Geoff J M Parker,et al.  Imaging Tumor Vascular Heterogeneity and Angiogenesis using Dynamic Contrast-Enhanced Magnetic Resonance Imaging , 2007, Clinical Cancer Research.

[16]  B. Scheithauer,et al.  Histopathology, classification, and grading of gliomas , 1995, Glia.

[17]  G. Reifenberger,et al.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary , 2016, Acta Neuropathologica.

[18]  Namkug Kim,et al.  Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. , 2012, Radiology.

[19]  S. Green,et al.  Glioblastoma multiforme and anaplastic astrocytoma pathologic criteria and prognostic implications , 1985, Cancer.

[20]  C. Mettlin,et al.  National survey of patterns of care for brain-tumor patients. , 1989, Journal of neurosurgery.

[21]  Ronald G. Blasberg,et al.  Imaging Update : NewWindows , NewViews , 2007 .

[22]  Ruey-Feng Chang,et al.  Quantitative breast lesion classification based on multichannel distributions in shear-wave imaging , 2015, Comput. Methods Programs Biomed..

[23]  B. Nieman,et al.  Cellular imaging and texture analysis distinguish differences in cellular dynamics in mouse brain tumors , 2014, Magnetic resonance in medicine.

[24]  P. Kleihues,et al.  Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas. , 2005, Journal of neuropathology and experimental neurology.

[25]  Jeon-Hor Chen,et al.  Computer-aided diagnosis of breast masses using quantified BI-RADS findings , 2013, Comput. Methods Programs Biomed..

[26]  Zhenhua Guo,et al.  Local directional derivative pattern for rotation invariant texture classification , 2011, Neural Computing and Applications.

[27]  Paul S Mischel,et al.  Relationship between gene expression and enhancement in glioblastoma multiforme: exploratory DNA microarray analysis. , 2008, Radiology.

[28]  Ahmet Ekin,et al.  Intensity versus texture for medical image search and retrival , 2008, 2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[29]  Yimin,et al.  Staging liver fibrosis by analysis of non-linear normalization texture in gadolinium-enhanced magnetic resonance imaging , 2015 .

[30]  D K Pearl,et al.  Improving diagnostic accuracy and interobserver concordance in the classification and grading of primary gliomas , 1997, Cancer.

[31]  J. Uhm.  Comprehensive genomic characterization defines human glioblastoma genes and core pathways , 2009 .

[32]  Ruey-Feng Chang,et al.  Multi-Dimensional Tumor Detection in Automated Whole Breast Ultrasound Using Topographic Watershed , 2014, IEEE Transactions on Medical Imaging.

[33]  Alexander R. Pico,et al.  Glioma Groups Based on 1p/19q, IDH, and TERT Promoter Mutations in Tumors. , 2015, The New England journal of medicine.

[34]  A Leviton,et al.  Limitations of the World Health Organization classification of childhood supratentorial astrocytic tumors , 2000, Cancer.

[35]  Steven J. M. Jones,et al.  Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas. , 2015, The New England journal of medicine.

[36]  Massimo Bellomi,et al.  Session: Evaluation of lung nodules: role of PET/CT , 2014, Cancer Imaging.

[37]  Manuel Desco,et al.  Added value of advanced over conventional magnetic resonance imaging in grading gliomas and other primary brain tumors , 2014, Cancer Imaging.

[38]  Jeon-Hor Chen,et al.  Intensity-Invariant Texture Analysis for Classification of BI-RADS Category 3 Breast Masses. , 2015, Ultrasound in medicine & biology.

[39]  Richard A. Groeneveld,et al.  Measuring Skewness and Kurtosis , 1984 .