Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

[1]  Mehrdad J. Gangeh,et al.  A priori Prediction of Neoadjuvant Chemotherapy Response and Survival in Breast Cancer Patients using Quantitative Ultrasound , 2017, Scientific Reports.

[2]  Y. Murata,et al.  Correlation between Ultrasound Findings of Tumor Margin and Clinicopathological Findings in Patients with Invasive Ductal Carcinoma of the Breast , 2016, Yonago acta medica.

[3]  Gregory J. Czarnota,et al.  Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach , 2016, Oncotarget.

[4]  Chris Bakal,et al.  Microenvironmental Heterogeneity Parallels Breast Cancer Progression: A Histology–Genomic Integration Analysis , 2016, PLoS medicine.

[5]  Jonathan Mamou,et al.  Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound , 2016, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control.

[6]  G. Pratx,et al.  Imaging metabolic heterogeneity in cancer , 2016, Molecular Cancer.

[7]  Ali Sadeghi-Naini,et al.  Early detection of chemotherapy-refractory patients by monitoring textural alterations in diffuse optical spectroscopic images. , 2015, Medical physics.

[8]  Gregory J. Czarnota,et al.  Non-invasive evaluation of breast cancer response to chemotherapy using quantitative ultrasonic backscatter parameters , 2015, Medical Image Anal..

[9]  Sidra Nawaz,et al.  Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology , 2015, Laboratory Investigation.

[10]  Christopher J. Sevinsky,et al.  Emerging Understanding of Multiscale Tumor Heterogeneity , 2014, Front. Oncol..

[11]  Gregory J. Czarnota,et al.  Noninvasive Characterization of Locally Advanced Breast Cancer Using Textural Analysis of Quantitative Ultrasound Parametric Images , 2014, Translational oncology.

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

[13]  Martin J. Yaffe,et al.  Early prediction of therapy responses and outcomes in breast cancer patients using quantitative ultrasound spectral texture , 2014, Oncotarget.

[14]  Sarah E Bohndiek,et al.  Analysis of image heterogeneity using 2D Minkowski functionals detects tumor responses to treatment , 2014, Magnetic resonance in medicine.

[15]  Roberto J Lavarello,et al.  Characterization of thyroid cancer in mouse models using high-frequency quantitative ultrasound techniques. , 2013, Ultrasound in medicine & biology.

[16]  Jonathan Mamou,et al.  Quantitative Ultrasound in Soft Tissues , 2013, Springer Netherlands.

[17]  Michael C. Kolios,et al.  Low-frequency quantitative ultrasound imaging of cell death in vivo. , 2013, Medical Physics (Lancaster).

[18]  Peter Gibbs,et al.  Texture analysis in assessment and prediction of chemotherapy response in breast cancer , 2013, Journal of magnetic resonance imaging : JMRI.

[19]  Michael C. Kolios,et al.  Conventional frequency ultrasonic biomarkers of cancer treatment response in vivo. , 2013, Translational oncology.

[20]  Shan Tan,et al.  Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy. , 2013, International journal of radiation oncology, biology, physics.

[21]  Michael C. Kolios,et al.  Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy , 2013, Clinical Cancer Research.

[22]  Omar Falou,et al.  Quantitative ultrasound spectral parametric maps: Early surrogates of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[24]  Michael L. Oelze,et al.  Quantitative ultrasound techniques and improvements to diagnostic ultrasonic imaging , 2012, 2012 IEEE International Ultrasonics Symposium.

[25]  Martin J. Yaffe,et al.  Imaging innovations for cancer therapy response monitoring , 2012 .

[26]  J. Bradley,et al.  Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[27]  Kornelia Polyak,et al.  Heterogeneity in breast cancer. , 2011, The Journal of clinical investigation.

[28]  V. Goh,et al.  Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. , 2011, Radiology.

[29]  Chih-Kuang Yeh,et al.  Classification of scattering media within benign and malignant breast tumors based on ultrasound texture-feature-based and Nakagami-parameter images. , 2011, Medical physics.

[30]  M. Oelze,et al.  Three-dimensional high-frequency backscatter and envelope quantification of cancerous human lymph nodes. , 2011, Ultrasound in medicine & biology.

[31]  E. Feleppa,et al.  Quantitative ultrasound in cancer imaging. , 2011, Seminars in oncology.

[32]  T. Bigelow,et al.  Estimating the total ultrasound attenuation along the propagation path by using a reference phantom. , 2010, The Journal of the Acoustical Society of America.

[33]  André Victor Alvarenga,et al.  Complexity curve and grey level co-occurrence matrix in the texture evaluation of breast tumor on ultrasound images. , 2007, Medical physics.

[34]  D. Vanel.  The American College of Radiology (ACR) Breast Imaging and Reporting Data System (BI-RADS): a step towards a universal radiological language? , 2007, European journal of radiology.

[35]  M. Oelze,et al.  Examination of cancer in mouse models using high-frequency quantitative ultrasound. , 2006, Ultrasound in medicine & biology.

[36]  Olsi Rama,et al.  Development of ultrasound tomography for breast imaging: technical assessment. , 2005, Medical physics.

[37]  William D. O'Brien,et al.  Differentiation and characterization of rat mammary fibroadenomas and 4T1 mouse carcinomas using quantitative ultrasound imaging , 2004, IEEE Transactions on Medical Imaging.

[38]  Tian Liu,et al.  Role of advanced 2 and 3-dimensional ultrasound for detecting prostate cancer. , 2002, The Journal of urology.

[39]  William D O'Brien,et al.  Parametric Imaging of Rat Mammary Tumors In Vivo for the Purposes of Tissue Characterization , 2002, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[40]  William D O'Brien,et al.  Frequency-dependent attenuation-compensation functions for ultrasonic signals backscattered from random media. , 2002, The Journal of the Acoustical Society of America.

[41]  V. Reuter,et al.  Typing of prostate tissue by ultrasonic spectrum analysis , 1996, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[42]  B. Sigel,et al.  Ultrasonic tissue characterization of blood clots. , 1990, The Surgical clinics of North America.

[43]  L. X. Yao,et al.  Backscatter Coefficient Measurements Using a Reference Phantom to Extract Depth-Dependent Instrumentation Factors , 1990, Ultrasonic imaging.

[44]  E. Feleppa,et al.  Theoretical framework for spectrum analysis in ultrasonic tissue characterization. , 1983, The Journal of the Acoustical Society of America.

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

[46]  A. Jemal,et al.  Cancer statistics, 2016 , 2016, CA: a cancer journal for clinicians.

[47]  Ali Sadeghi-Naini,et al.  Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties. , 2014, Medical physics.

[48]  Y. Chou,et al.  In Memoriam: Hsi-Yao Chen, MD, FAIUM, 1922–2012, The Father of Modern Obstetrics and Pioneer of Diagnostic Ultrasound in Taiwan , 2013 .

[49]  Omar Falou,et al.  Quantitative ultrasound visualization of cell death: Emerging clinical applications for detection of cancer treatment response , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[50]  Vicky Goh,et al.  Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis , 2012, European Journal of Nuclear Medicine and Molecular Imaging.

[51]  Yassin Labyed,et al.  Estimate of the attenuation coefficient using a clinical array transducer for the detection of cervical ripening in human pregnancy. , 2011, Ultrasonics.

[52]  Ernest J Feleppa,et al.  Ultrasonic tissue-type imaging of the prostate: implications for biopsy and treatment guidance. , 2008, Cancer biomarkers : section A of Disease markers.

[53]  Markus F Neurath,et al.  Virtual histology. , 2008, Best practice & research. Clinical gastroenterology.

[54]  Tian Liu,et al.  Ultrasonic spectrum-analysis and neural-network classification as a basis for ultrasonic imaging to target brachytherapy of prostate cancer. , 2002, Brachytherapy.

[55]  E J Feleppa,et al.  Three-dimensional ultrasound analyses of the prostate. , 2000, Molecular urology.

[56]  Balaji,et al.  Progress in Two-Dimensional and Three-Dimensional Ultrasonic Tissue-Type Imaging of the Prostate Based on Spectrum Analysis and Nonlinear Classifiers. , 1999, Molecular urology.

[57]  T. Noritomi,et al.  Carotid plaque typing by multiple-parameter ultrasonic tissue characterization. , 1997, Ultrasound in medicine & biology.

[58]  E. Feleppa,et al.  Statistical framework for ultrasonic spectral parameter imaging. , 1997, Ultrasound in medicine & biology.

[59]  Ronald H. Silverman,et al.  Ultrasonic spectrum analysis for tissue assays and therapy evaluation , 1997, Int. J. Imaging Syst. Technol..

[60]  R. F. Wagner,et al.  High resolution ultrasonic backscatter coefficient estimation based on autoregressive spectral estimation using Burg's algorithm , 1994, IEEE Trans. Medical Imaging.

[61]  R. F. Wagner,et al.  Application of autoregressive spectral analysis to cepstral estimation of mean scatterer spacing , 1993, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[62]  S C Horii,et al.  Improving the distinction between benign and malignant breast lesions: the value of sonographic texture analysis. , 1993, Ultrasonic imaging.

[63]  N. Dubrawsky.  Cancer statistics , 1989, CA: a cancer journal for clinicians.

[64]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.