A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

[1]  M. Sydes,et al.  Adjuvant or early salvage radiotherapy for the treatment of localised and locally advanced prostate cancer: a prospectively planned systematic review and meta-analysis of aggregate data , 2020, The Lancet.

[2]  A. Tewari,et al.  Prediction of biochemical recurrence in prostate cancer patients who underwent prostatectomy using routine clinical prostate multiparametric MRI and decipher genomic score , 2020, Journal of magnetic resonance imaging : JMRI.

[3]  Andrew J. Vickers,et al.  A simple, step-by-step guide to interpreting decision curve analysis , 2019, Diagnostic and Prognostic Research.

[4]  M. Hatt,et al.  MRI-Derived Radiomics to Guide Post-operative Management for High-Risk Prostate Cancer , 2019, Front. Oncol..

[5]  Bachir Taouli,et al.  Radiomics Features Measured with Multiparametric Magnetic Resonance Imaging Predict Prostate Cancer Aggressiveness. , 2019, The Journal of urology.

[6]  C. Moore,et al.  Role of MRI in planning radical prostatectomy: what is the added value? , 2019, World Journal of Urology.

[7]  A. Madabhushi,et al.  Correlation between MRI phenotypes and a genomic classifier of prostate cancer: preliminary findings , 2019, European Radiology.

[8]  H. G. van der Poel,et al.  Prognostic Value of Biochemical Recurrence Following Treatment with Curative Intent for Prostate Cancer: A Systematic Review. , 2019, European urology.

[9]  Bahram Mofid,et al.  Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer , 2019, La radiologia medica.

[10]  B. Trock,et al.  Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance , 2018, Prostate Cancer and Prostatic Diseases.

[11]  R. Kikinis,et al.  An annotated test-retest collection of prostate multiparametric MRI , 2018, Scientific Data.

[12]  Anant Madabhushi,et al.  Radiomic features from pretreatment biparametric MRI predict prostate cancer biochemical recurrence: Preliminary findings , 2018, Journal of magnetic resonance imaging : JMRI.

[13]  C. Zuiani,et al.  Interreader agreement of PI‐RADS v. 2 in assessing prostate cancer with multiparametric MRI: A study using whole‐mount histology as the standard of reference , 2018, Journal of magnetic resonance imaging : JMRI.

[14]  Anant Madabhushi,et al.  Identifying the morphologic basis for radiomic features in distinguishing different Gleason grades of prostate cancer on MRI: Preliminary findings , 2018, PloS one.

[15]  Scott G. Williams,et al.  Surrogate endpoints in early prostate cancer research , 2018, Translational andrology and urology.

[16]  A. Kishan,et al.  Pretreatment 3T multiparametric MRI staging predicts for biochemical failure in high-risk prostate cancer treated with combination high-dose-rate brachytherapy and external beam radiotherapy. , 2017, Brachytherapy.

[17]  M. Cooperberg,et al.  The CAPRA Score at 10 Years: Contemporary Perspectives and Analysis of Supporting Studies. , 2017, European urology.

[18]  M. Parmar,et al.  Diagnostic accuracy of multi-parametric MRI and TRUS biopsy in prostate cancer (PROMIS): a paired validating confirmatory study , 2017, The Lancet.

[19]  B. Hadaschik,et al.  Transcriptome Wide Analysis of Magnetic Resonance Imaging-targeted Biopsy and Matching Surgical Specimens from High-risk Prostate Cancer Patients Treated with Radical Prostatectomy: The Target Must Be Hit. , 2017, European urology focus.

[20]  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.

[21]  Prateek Prasanna,et al.  Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor , 2016, Scientific Reports.

[22]  Jianbo Li,et al.  Decipher Genomic Classifier Measured on Prostate Biopsy Predicts Metastasis Risk. , 2016, Urology.

[23]  H. Hricak,et al.  Diagnosis of Extracapsular Extension of Prostate Cancer on Prostate MRI: Impact of Second-Opinion Readings by Subspecialized Genitourinary Oncologic Radiologists. , 2015, AJR. American journal of roentgenology.

[24]  Anamaria Crisan,et al.  Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort. , 2015, European urology.

[25]  Anirban P. Mitra,et al.  Discovery and Validation of a Prostate Cancer Genomic Classifier that Predicts Early Metastasis Following Radical Prostatectomy , 2013, PloS one.

[26]  Ron Kikinis,et al.  3D Slicer , 2012, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[27]  M. Cooperberg,et al.  The CAPRA‐S score , 2011, Cancer.

[28]  Alan W Partin,et al.  Risk of prostate cancer-specific mortality following biochemical recurrence after radical prostatectomy. , 2005, JAMA.

[29]  A. Renshaw,et al.  Invasion of seminal vesicles by adenocarcinoma of the prostate: PSA outcome determined by preoperative and postoperative factors. , 2004, Urology.

[30]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  A W Partin,et al.  Natural history of progression after PSA elevation following radical prostatectomy. , 1999, JAMA.

[32]  Alan C. Evans,et al.  A nonparametric method for automatic correction of intensity nonuniformity in MRI data , 1998, IEEE Transactions on Medical Imaging.

[33]  D. Sagi,et al.  Gabor filters as texture discriminator , 1989, Biological Cybernetics.

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

[35]  J. Epstein,et al.  Adverse Pathologic Findings for Men Electing Immediate Radical Prostatectomy: Defining a Favorable Intermediate-Risk Group , 2018, JAMA Oncology.

[36]  M. Baghdadi,et al.  Biochemical recurrence after radical prostatectomy: what does it mean? , 2018, International braz j urol : official journal of the Brazilian Society of Urology.

[37]  D. Margolis,et al.  PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. , 2016, European urology.

[38]  B. Trock,et al.  Tissue-based Genomics Augments Post-prostatectomy Risk Stratification in a Natural History Cohort of Intermediate- and High-Risk Men. , 2016, European urology.

[39]  S. Boorjian,et al.  The presence of extracapsular extension is associated with an increased risk of death from prostate cancer after radical prostatectomy for patients with seminal vesicle invasion and negative lymph nodes. , 2014, Urologic oncology.

[40]  Jan Sijbers,et al.  Bias Field Correction for MRI Images , 2005, CORES.

[41]  K. Laws.  Textured Image Segmentation , 1980 .