Haralick textural features on T2‐weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer

To explore the association between magnetic resonance imaging (MRI), including Haralick textural features, and biochemical recurrence following prostate cancer radiotherapy.

[1]  Michael Unser,et al.  Snakes with Ellipse-Reproducing Property , 2011 .

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

[3]  M. Giger,et al.  Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study. , 2013, Radiology.

[4]  V. Goh,et al.  Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. , 2013, Radiology.

[5]  H. Hricak,et al.  Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores , 2015, European Radiology.

[6]  A. Renshaw,et al.  Risk of prostate cancer recurrence in men treated with radiation alone or in conjunction with combined or less than combined androgen suppression therapy. , 2008, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[7]  Yousef Mazaheri,et al.  Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness. , 2011, Radiology.

[8]  Y. Yamashita,et al.  Clinical utility of the normalized apparent diffusion coefficient for preoperative evaluation of the aggressiveness of prostate cancer , 2014, Japanese Journal of Radiology.

[9]  A. Oto,et al.  Diffusion-weighted and dynamic contrast-enhanced MRI of prostate cancer: correlation of quantitative MR parameters with Gleason score and tumor angiogenesis. , 2011, AJR. American journal of roentgenology.

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

[11]  J. Concato,et al.  A simulation study of the number of events per variable in logistic regression analysis. , 1996, Journal of clinical epidemiology.

[12]  Jing Fang,et al.  Computerized characterization of prostate cancer by fractal analysis in MR images , 2009, Journal of magnetic resonance imaging : JMRI.

[13]  M. Hatt,et al.  18F-FDG PET Uptake Characterization Through Texture Analysis: Investigating the Complementary Nature of Heterogeneity and Functional Tumor Volume in a Multi–Cancer Site Patient Cohort , 2015, The Journal of Nuclear Medicine.

[14]  H. Eskola,et al.  Non-Hodgkin lymphoma response evaluation with MRI texture classification , 2009, Journal of experimental & clinical cancer research : CR.

[15]  Marek Kretowski,et al.  Multi-Image Texture Analysis in Classification of Prostatic Tissues from MRI. Preliminary Results , 2014 .

[16]  Baris Turkbey,et al.  Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? , 2011, Radiology.

[17]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[18]  Chun-Kai Chang,et al.  3D Computation of Gray Level Co-occurrence in Hyperspectral Image Cubes , 2007, EMMCVPR.

[19]  Cary Siegel,et al.  Prostate cancer vs. post-biopsy hemorrhage: diagnosis with T2- and diffusion-weighted imaging. , 2011, The Journal of urology.

[20]  Hemant Ishwaran,et al.  Random Survival Forests , 2008, Wiley StatsRef: Statistics Reference Online.

[21]  Ying Lu,et al.  Prostate cancer: role of pretreatment MR in predicting outcome after external-beam radiation therapy--initial experience. , 2008, Radiology.

[22]  J. Witjes,et al.  Use of the Prostate Imaging Reporting and Data System (PI-RADS) for Prostate Cancer Detection with Multiparametric Magnetic Resonance Imaging: A Diagnostic Meta-analysis. , 2015, European urology.

[23]  Juan J. Martinez,et al.  Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. , 2015, Medical physics.

[24]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[25]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

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

[27]  J. Kirkpatrick.  Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. , 1998, Journal of insurance medicine.

[28]  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.

[29]  G. Viani,et al.  Whole brain radiotherapy with radiosensitizer for brain metastases , 2009, Journal of experimental & clinical cancer research : CR.

[30]  Eric Stindel,et al.  A framework for multimodal imaging-based prognostic model building: Preliminary study on multimodal MRI in Glioblastoma Multiforme , 2015 .

[31]  Margie Hunt,et al.  Intensity-modulated radiation therapy: supportive data for prostate cancer. , 2008, Seminars in radiation oncology.

[32]  F. Turkheimer,et al.  A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities , 2015 .

[33]  P. Box.  Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer , 2011 .

[34]  M. Stasi,et al.  Texture features on T2-weighted magnetic resonance imaging: new potential biomarkers for prostate cancer aggressiveness , 2015, Physics in medicine and biology.

[35]  Patrick Granton,et al.  Radiomics: extracting more information from medical images using advanced feature analysis. , 2012, European journal of cancer.

[36]  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.

[37]  E. Horwitz,et al.  The Phoenix definition of biochemical failure predicts for overall survival in patients with prostate cancer , 2008, Cancer.

[38]  Fernando J. Kim,et al.  Is apparent diffusion coefficient associated with clinical risk scores for prostate cancers that are visible on 3-T MR images? , 2011 .

[39]  Katarzyna J Macura,et al.  Synopsis of the PI-RADS v2 Guidelines for Multiparametric Prostate Magnetic Resonance Imaging and Recommendations for Use. , 2016, European urology.

[40]  L. Schad,et al.  MR image texture analysis--an approach to tissue characterization. , 1993, Magnetic resonance imaging.

[41]  Carole Lartizien,et al.  Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI , 2012, Physics in medicine and biology.

[42]  Daniele Regge,et al.  MR-T2-weighted signal intensity: a new imaging biomarker of prostate cancer aggressiveness , 2016, Comput. methods Biomech. Biomed. Eng. Imaging Vis..

[43]  R. Lenkinski,et al.  Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imagery , 2012, Journal of magnetic resonance imaging : JMRI.

[44]  Thomas Bäck,et al.  Novel head and neck cancer survival analysis approach: Random survival forests versus cox proportional hazards regression , 2012, Head & neck.

[45]  N. Michoux,et al.  Texture analysis on MR images helps predicting non-response to NAC in breast cancer , 2015, BMC Cancer.

[46]  Andrzej Materka,et al.  Texture analysis for tissue discrimination on T1‐weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers , 2005, Journal of magnetic resonance imaging : JMRI.

[47]  H. Hricak,et al.  Pretreatment endorectal coil magnetic resonance imaging findings predict biochemical tumor control in prostate cancer patients treated with combination brachytherapy and external-beam radiotherapy. , 2012, International journal of radiation oncology, biology, physics.

[48]  J. Babb,et al.  Prostate cancer vs. post‐biopsy hemorrhage: Diagnosis with T2‐ and diffusion‐weighted imaging , 2010, Journal of magnetic resonance imaging : JMRI.

[49]  I. Hsu,et al.  Pretreatment endorectal magnetic resonance imaging and magnetic resonance spectroscopic imaging features of prostate cancer as predictors of response to external beam radiotherapy. , 2009, International journal of radiation oncology, biology, physics.

[50]  A. Renshaw,et al.  Quantifying the impact of seminal vesicle invasion identified using endorectal magnetic resonance imaging on PSA outcome after radiation therapy for patients with clinically localized prostate cancer. , 2004, International journal of radiation oncology, biology, physics.