Textural features of dynamic contrast‐enhanced MRI derived model‐free and model‐based parameter maps in glioma grading

Presurgical glioma grading by dynamic contrast‐enhanced MRI (DCE‐MRI) has unresolved issues.

[1]  S. Thust,et al.  Advanced MRI Techniques in the Monitoring of Treatment of Gliomas , 2017, Current Treatment Options in Neurology.

[2]  Paul Kinahan,et al.  Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.

[3]  Rajan Jain,et al.  Measurements of tumor vascular leakiness using DCE in brain tumors: clinical applications , 2013, NMR in biomedicine.

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

[5]  Thomas E Yankeelov,et al.  Early assessment of breast cancer response to neoadjuvant chemotherapy by semi-quantitative analysis of high-temporal resolution DCE-MRI: preliminary results. , 2013, Magnetic resonance imaging.

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

[7]  Lawrence H. Schwartz,et al.  Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab , 2017, Neuro-oncology.

[8]  A Gregory Sorensen,et al.  Dynamic susceptibility contrast MRI measures of relative cerebral blood volume as a prognostic marker for overall survival in recurrent glioblastoma: results from the ACRIN 6677/RTOG 0625 multicenter trial. , 2015, Neuro-oncology.

[9]  C S Patlak,et al.  Graphical Evaluation of Blood-to-Brain Transfer Constants from Multiple-Time Uptake Data , 1983, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[10]  Sumei Wang,et al.  Quantitative in vivo imaging of tissue factor expression in glioma using dynamic contrast-enhanced MRI derived parameters. , 2017, European journal of radiology.

[11]  Oscar Acosta,et al.  Haralick textural features on T2‐weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer , 2017, Journal of magnetic resonance imaging : JMRI.

[12]  N. Thacker,et al.  Quantifying heterogeneity in human tumours using MRI and PET. , 2012, European journal of cancer.

[13]  Sumei Wang,et al.  Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging , 2015, Cancer Imaging.

[14]  Raymond Y Huang,et al.  Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas , 2017, Neuro-oncology.

[15]  Peter Gibbs,et al.  Pretreatment Prognostic Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Vascular, Texture, Shape, and Size Parameters Compared With Traditional Survival Indicators Obtained From Locally Advanced Breast Cancer Patients , 2016, Investigative radiology.

[16]  K. Hoang-Xuan,et al.  Primary brain tumours in adults , 2003, The Lancet.

[17]  Joanna M. Wardlaw,et al.  Assessment of blood–brain barrier disruption using dynamic contrast-enhanced MRI. A systematic review , 2014, NeuroImage: Clinical.

[18]  Charles Duyckaerts,et al.  The 2007 WHO classification of tumors of the central nervous system – what has changed? , 2008, Current opinion in neurology.

[19]  Santosh Kesari,et al.  Malignant gliomas in adults. , 2008, The New England journal of medicine.

[20]  M. Sharma,et al.  Morphology of angiogenesis in human cancer: a conceptual overview, histoprognostic perspective and significance of neoangiogenesis , 2005, Histopathology.

[21]  Rajikha Raja,et al.  Assessment of tissue heterogeneity using diffusion tensor and diffusion kurtosis imaging for grading gliomas , 2016, Neuroradiology.

[22]  G. Reifenberger,et al.  European Association for Neuro-Oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. , 2017, The Lancet. Oncology.

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

[24]  K. Peck,et al.  Dynamic Contrast‐Enhanced MRI in Low‐Grade Versus Anaplastic Oligodendrogliomas , 2016, Journal of neuroimaging : official journal of the American Society of Neuroimaging.

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

[26]  A. Bjørnerud,et al.  Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. , 2008, Radiology.

[27]  Jian Z. Wang,et al.  Predicting Control of Primary Tumor and Survival by DCE MRI During Early Therapy in Cervical Cancer , 2009, Investigative radiology.

[28]  Ruijiang Li,et al.  Intratumor partitioning and texture analysis of dynamic contrast‐enhanced (DCE)‐MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy , 2016, Journal of magnetic resonance imaging : JMRI.

[29]  Yanqi Huang,et al.  Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer. , 2016, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  Balaji Ganeshan,et al.  Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. , 2016, European journal of radiology.

[31]  Bensheng Qiu,et al.  Correlation of volume transfer coefficient Ktrans with histopathologic grades of gliomas , 2012, Journal of magnetic resonance imaging : JMRI.

[32]  G. Parker,et al.  Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome , 2014, Clinical Cancer Research.

[33]  E. Wallen,et al.  Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell–Renal-Cell-Carcinoma: Proof-of-Concept Study , 2017, Scientific Reports.

[34]  D. Zagzag,et al.  Mechanisms of glioma-associated neovascularization. , 2012, The American journal of pathology.

[35]  K. Hoang-Xuan,et al.  Primary brain tumours in adults , 2003, The Lancet.

[36]  J. O'Connor,et al.  Cancer heterogeneity and imaging. , 2017, Seminars in cell & developmental biology.

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

[38]  S. Heiland,et al.  Evaluation of dynamic contrast-enhanced MRI derived microvascular permeability in recurrent glioblastoma treated with bevacizumab , 2014, Journal of Neuro-Oncology.

[39]  Guillem Pratx,et al.  Imaging metabolic heterogeneity in cancer , 2016, Molecular Cancer.

[40]  S. Matsumoto,et al.  Prognostic value of dynamic MR imaging for non‐small‐cell lung cancer patients after chemoradiotherapy , 2005, Journal of magnetic resonance imaging : JMRI.

[41]  Milica Medved,et al.  Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time , 2004, Journal of magnetic resonance imaging : JMRI.

[42]  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.

[43]  P. Tofts.  Modeling tracer kinetics in dynamic Gd‐DTPA MR imaging , 1997, Journal of magnetic resonance imaging : JMRI.

[44]  L. Costaridou,et al.  Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis. , 2010, The British journal of radiology.

[45]  Amy C. Dwyer,et al.  Models and methods for analyzing DCE-MRI: a review. , 2014, Medical physics.

[46]  P. Tofts,et al.  Measurement of the blood‐brain barrier permeability and leakage space using dynamic MR imaging. 1. Fundamental concepts , 1991, Magnetic resonance in medicine.

[47]  E. Shaw,et al.  Phase III trial of chemoradiotherapy for anaplastic oligodendroglioma: long-term results of RTOG 9402. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.