Lack of robustness of textural measures obtained from 3D brain tumor MRIs impose a need for standardization

Purpose Textural measures have been widely explored as imaging biomarkers in cancer. However, their robustness under dynamic range and spatial resolution changes in brain 3D magnetic resonance images (MRI) has not been assessed. The aim of this work was to study potential variations of textural measures due to changes in MRI protocols. Materials and methods Twenty patients harboring glioblastoma with pretreatment 3D T1-weighted MRIs were included in the study. Four different spatial resolution combinations and three dynamic ranges were studied for each patient. Sixteen three-dimensional textural heterogeneity measures were computed for each patient and configuration including co-occurrence matrices (CM) features and run-length matrices (RLM) features. The coefficient of variation was used to assess the robustness of the measures in two series of experiments corresponding to (i) changing the dynamic range and (ii) changing the matrix size. Results No textural measures were robust under dynamic range changes. Entropy was the only textural feature robust under spatial resolution changes (coefficient of variation under 10% in all cases). Conclusion Textural measures of three-dimensional brain tumor images are not robust neither under dynamic range nor under matrix size changes. Standards should be harmonized to use textural features as imaging biomarkers in radiomic-based studies. The implications of this work go beyond the specific tumor type studied here and pose the need for standardization in textural feature calculation of oncological images.

[1]  Frank J. Brooks,et al.  On some misconceptions about tumor heterogeneity quantification , 2013, European Journal of Nuclear Medicine and Molecular Imaging.

[2]  Jacob D. Furst,et al.  RUN-LENGTH ENCODING FOR VOLUMETRIC TEXTURE , 2004 .

[3]  Grégoire Toussaint,et al.  Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas. , 2003, Magnetic resonance imaging.

[4]  Balaji Ganeshan,et al.  Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage , 2010, Cancer imaging : the official publication of the International Cancer Imaging Society.

[5]  L. Mazzoni,et al.  Prognostic Value of MR Imaging Texture Analysis in Brain Non-Small Cell Lung Cancer Oligo-Metastases Undergoing Stereotactic Irradiation , 2016, Cureus.

[6]  Δημήτριος Γκλώτσος,et al.  Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods , 2015 .

[7]  J. Borrás,et al.  Tumour heterogeneity in glioblastoma assessed by MRI texture analysis: a potential marker of survival. , 2016, The British journal of radiology.

[8]  G. Collewet,et al.  Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. , 2004, Magnetic resonance imaging.

[9]  E Le Rumeur,et al.  MRI texture analysis on texture test objects, normal brain and intracranial tumors. , 2003, Magnetic resonance imaging.

[10]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[11]  G T Luk-Pat,et al.  Reducing off‐resonance distortion by echo‐time interpolation , 2001, Magnetic resonance in medicine.

[12]  Andre Dekker,et al.  Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.

[13]  Marion Smits,et al.  Consensus recommendations for a standardized Brain Tumor Imaging Protocol in clinical trials. , 2015, Neuro-oncology.

[14]  N. Just,et al.  Improving tumour heterogeneity MRI assessment with histograms , 2014, British Journal of Cancer.

[15]  M. Hatt,et al.  Reproducibility of Tumor Uptake Heterogeneity Characterization Through Textural Feature Analysis in 18F-FDG PET , 2012, The Journal of Nuclear Medicine.

[16]  R. A. Lerski,et al.  Magnetic resonance imaging texture analysis classification of primary breast cancer , 2016, European Radiology.

[17]  Jacob D. Furst,et al.  CO-OCCURRENCE MATRICES FOR VOLUMETRIC DATA , 2004 .

[18]  Robert J. Gillies,et al.  The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis , 2015, Scientific Reports.

[19]  Dakai Zhang,et al.  Use of texture analysis based on contrast‐enhanced MRI to predict treatment response to chemoradiotherapy in nasopharyngeal carcinoma , 2016, Journal of magnetic resonance imaging : JMRI.

[20]  Irène Buvat,et al.  Tumor Texture Analysis in 18F-FDG PET: Relationships Between Texture Parameters, Histogram Indices, Standardized Uptake Values, Metabolic Volumes, and Total Lesion Glycolysis , 2014, The Journal of Nuclear Medicine.

[21]  Estanislao Arana,et al.  Glioblastoma: does the pre-treatment geometry matter? A postcontrast T1 MRI-based study , 2017, European Radiology.

[22]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[23]  A. Madabhushi,et al.  Radiomic features for prostate cancer detection on MRI differ between the transition and peripheral zones: Preliminary findings from a multi‐institutional study , 2017, Journal of magnetic resonance imaging : JMRI.

[24]  Irène Buvat,et al.  Tumor Texture Analysis in PET: Where Do We Stand? , 2015, The Journal of Nuclear Medicine.

[25]  Dimitri Van De Ville,et al.  Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities , 2014, Medical Image Anal..

[26]  A. Rao,et al.  Texture Feature Ratios from Relative CBV Maps of Perfusion MRI Are Associated with Patient Survival in Glioblastoma , 2016, American Journal of Neuroradiology.

[27]  Bal Sanghera,et al.  Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? , 2012, Insights into Imaging.

[28]  Estanislao Arana,et al.  Influence of gray level and space discretization on brain tumor heterogeneity measures obtained from magnetic resonance images , 2016, Comput. Biol. Medicine.

[29]  Sang Min Lee,et al.  Prognostic Value of Computed Tomography Texture Features in Non–Small Cell Lung Cancers Treated With Definitive Concomitant Chemoradiotherapy , 2015, Investigative radiology.

[30]  Dawit Assefa,et al.  Robust texture features for response monitoring of glioblastoma multiforme on T1-weighted and T2-FLAIR MR images: a preliminary investigation in terms of identification and segmentation. , 2010, Medical physics.

[31]  George F. Reed,et al.  Use of Coefficient of Variation in Assessing Variability of Quantitative Assays , 2002, Clinical and Vaccine Immunology.

[32]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[33]  R. Gillies,et al.  Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction , 2016, Journal of magnetic resonance imaging : JMRI.

[34]  Andrzej Materka,et al.  Effects of MRI acquisition parameter variations and protocol heterogeneity on the results of texture analysis and pattern discrimination: an application-oriented study. , 2009, Medical physics.

[35]  Yeni Herdiyeni,et al.  Comparison of 2 D and 3 D Local Binary Pattern in Lung Cancer Diagnosis , 2012 .

[36]  Lawrence H. Schwartz,et al.  Assessing Agreement between Radiomic Features Computed for Multiple CT Imaging Settings , 2016, PloS one.

[37]  H. Aerts,et al.  Applications and limitations of radiomics , 2016, Physics in medicine and biology.

[38]  W. Niessen,et al.  Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review , 2014, PloS one.

[39]  Andrés Larroza,et al.  Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI , 2015, Journal of magnetic resonance imaging : JMRI.

[40]  Benjamin M. Ellingson,et al.  Radiogenomics and Imaging Phenotypes in Glioblastoma: Novel Observations and Correlation with Molecular Characteristics , 2014, Current Neurology and Neuroscience Reports.

[41]  Luc Bidaut,et al.  The influence of field strength and different clinical breast MRI protocols on the outcome of texture analysis using foam phantoms. , 2011, Medical physics.

[42]  Yeni Herdiyeni,et al.  Comparison of 2D and 3D Local Binary Pattern in Lung Cancer Diagnosis , 2012 .

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

[44]  S. Suo,et al.  Assessment of Heterogeneity Difference Between Edge and Core by Using Texture Analysis: Differentiation of Malignant From Inflammatory Pulmonary Nodules and Masses. , 2016, Academic radiology.