Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis

[1]  Xue-dong Wei,et al.  Extracapsular extension of transitional zone prostate cancer miss-detected by multiparametric magnetic resonance imaging , 2023, Journal of Cancer Research and Clinical Oncology.

[2]  M. Cravo,et al.  Early biomarkers of extracapsular extension of prostate cancer using MRI-derived semantic features , 2022, Cancer Imaging.

[3]  S. Terzoni,et al.  Pre-operative prediction of extracapsular extension of prostate cancer: first external validation of the PRECE model on an independent dataset , 2022, International Urology and Nephrology.

[4]  Hui-Xiong Xu,et al.  Added value of shear-wave elastography in the prediction of extracapsular extension and seminal vesicle invasion before radical prostatectomy , 2022, Asian journal of andrology.

[5]  S. Albisinni,et al.  External Validation of Models for Prediction of Side-specific Extracapsular Extension in Prostate Cancer Patients Undergoing Radical Prostatectomy. , 2022, European urology focus.

[6]  Jian-Xia Xu,et al.  MRI Extraprostatic Extension Grade: Accuracy and Clinical Incremental Value in the Assessment of Extraprostatic Cancer , 2022, BioMed research international.

[7]  M. Shiota,et al.  Validation of user-friendly models predicting extracapsular extension in prostate cancer patients , 2022, Asian journal of urology.

[8]  K. Karmelita-Katulska,et al.  Prostate Magnetic Resonance Imaging Analyses, Clinical Parameters, and Preoperative Nomograms in the Prediction of Extraprostatic Extension , 2021, Clinics and practice.

[9]  J. Dobruch,et al.  External validation of a magnetic resonance imaging-based algorithm for prediction of side-specific extracapsular extension in prostate cancer , 2021, Central European journal of urology.

[10]  W. Xia,et al.  MRI-based radiomics models to assess prostate cancer, extracapsular extension and positive surgical margins , 2021, Cancer Imaging.

[11]  M. Kattan,et al.  International Multi-Site Initiative to Develop an MRI-Inclusive Nomogram for Side-Specific Prediction of Extraprostatic Extension of Prostate Cancer , 2021, Cancers.

[12]  Ying Hou,et al.  Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study , 2021, European Journal of Nuclear Medicine and Molecular Imaging.

[13]  Xingyu Zhao,et al.  Multiparametric Magnetic Resonance Imaging‐Based Peritumoral Radiomics for Preoperative Prediction of the Presence of Extracapsular Extension With Prostate Cancer , 2021, Journal of magnetic resonance imaging : JMRI.

[14]  Zhengyu Jin,et al.  External Validation of the Extraprostatic Extension Grade on MRI and Its Incremental Value to Clinical Models for Assessing Extraprostatic Cancer , 2021, Frontiers in Oncology.

[15]  R. Cuocolo,et al.  MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study , 2021, European Radiology.

[16]  C. Ravi,et al.  Development of an Indian nomogram for predicting extracapsular extension in prostate cancer , 2021, Indian journal of urology : IJU : journal of the Urological Society of India.

[17]  A. Stenzinger,et al.  Combined Clinical Parameters and Multiparametric Magnetic Resonance Imaging for the Prediction of Extraprostatic Disease-A Risk Model for Patient-tailored Risk Stratification When Planning Radical Prostatectomy. , 2020, European urology focus.

[18]  C. Iselin,et al.  External Validation of a Multiparametric Magnetic Resonance Imaging-based Nomogram for the Prediction of Extracapsular Extension and Seminal Vesicle Invasion in Prostate Cancer Patients Undergoing Radical Prostatectomy. , 2020, European urology.

[19]  J. Witjes,et al.  Development and External Validation of a Novel Nomogram to Predict Side-specific Extraprostatic Extension in Patients with Prostate Cancer Undergoing Radical Prostatectomy. , 2020, European urology oncology.

[20]  P. Summers,et al.  MRI-based radiomics signature for localized prostate cancer: a new clinical tool for cancer aggressiveness prediction? Sub-study of prospective phase II trial on ultra-hypofractionated radiotherapy (AIRC IG-13218) , 2020, European Radiology.

[21]  Jérémie F. Cohen,et al.  Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist , 2020, BMJ.

[22]  E. Peng,et al.  Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer , 2020, Cancer management and research.

[23]  M. Matikainen,et al.  Prostate MRI added to CAPRA, MSKCC and Partin cancer nomograms significantly enhances the prediction of adverse findings and biochemical recurrence after radical prostatectomy , 2020, PloS one.

[24]  Hao Sun,et al.  Radiomics Based on Multiparametric Magnetic Resonance Imaging to Predict Extraprostatic Extension of Prostate Cancer , 2020, Frontiers in Oncology.

[25]  F. Montorsi,et al.  The Key Combined Value of Multiparametric Magnetic Resonance Imaging, and Magnetic Resonance Imaging-targeted and Concomitant Systematic Biopsies for the Prediction of Adverse Pathological Features in Prostate Cancer Patients Undergoing Radical Prostatectomy. , 2020, European urology.

[26]  F. Lucchesi,et al.  Independent external validation of nomogram to predict extracapsular extension in patients with prostate cancer , 2020, European Radiology.

[27]  C. Beisland,et al.  Magnetic resonance radiomics for prediction of extraprostatic extension in non-favorable intermediate- and high-risk prostate cancer patients , 2020, Acta radiologica.

[28]  J. Witjes,et al.  External validation of the Martini nomogram for prediction of side-specific extraprostatic extension of prostate cancer in patients undergoing robot-assisted radical prostatectomy. , 2020, Urologic oncology.

[29]  Zhiyong Lin,et al.  MRI‐Based Radiomics Signature for the Preoperative Prediction of Extracapsular Extension of Prostate Cancer , 2019, Journal of magnetic resonance imaging : JMRI.

[30]  C. Zuiani,et al.  Head‐to‐head comparison between multiparametric MRI, the partin tables, memorial sloan kettering cancer center nomogram, and CAPRA score in predicting extraprostatic cancer in patients undergoing radical prostatectomy , 2019, Journal of magnetic resonance imaging : JMRI.

[31]  D. Margolis,et al.  Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. , 2019, European urology.

[32]  M. Gołębiowski,et al.  Predicting side-specific prostate cancer extracapsular extension: a simple decision rule of PSA, biopsy, and MRI parameters , 2019, International Urology and Nephrology.

[33]  T. Barrett,et al.  Multiparametric MRI - Local Staging of Prostate Cancer and Beyond , 2019, Radiology and oncology.

[34]  A. Lont,et al.  Adding multiparametric MRI to the MSKCC and Partin nomograms for primary prostate cancer: Improving local tumor staging? , 2019, Urologic oncology.

[35]  Clayton P Smith,et al.  A Grading System for the Assessment of Risk of Extraprostatic Extension of Prostate Cancer at Multiparametric MRI. , 2019, Radiology.

[36]  S. Eberhardt Local Staging of Prostate Cancer with MRI: A Need for Standardization. , 2019, Radiology.

[37]  A. Tewari,et al.  Development and internal validation of a side‐specific, multiparametric magnetic resonance imaging‐based nomogram for the prediction of extracapsular extension of prostate cancer , 2018, BJU international.

[38]  P. Choyke,et al.  Added Value of Multiparametric Magnetic Resonance Imaging to Clinical Nomograms for Predicting Adverse Pathology in Prostate Cancer , 2018, The Journal of urology.

[39]  Jing Zhang,et al.  Using support vector machine analysis to assess PartinMR: A new prediction model for organ‐confined prostate cancer , 2018, Journal of magnetic resonance imaging : JMRI.

[40]  M. Schwaiger,et al.  One-Stop-Shop Whole-Body 68Ga-PSMA-11 PET/MRI Compared with Clinical Nomograms for Preoperative T and N Staging of High-Risk Prostate Cancer , 2018, The Journal of Nuclear Medicine.

[41]  M. Rugge,et al.  Study of diagnostic accuracy of Fagan’s two-step nomogram in increasing the value of predictive tools for prostate cancer: application of specific spatial distribution of positive/negative bioptic cores to predict extracapsular extension , 2018, Aging Clinical and Experimental Research.

[42]  A. Mottrie,et al.  A novel tool for predicting extracapsular extension during graded partial nerve sparing in radical prostatectomy , 2018, BJU international.

[43]  C. Roehrborn,et al.  Diagnostic Utility of a Likert Scale Versus Qualitative Descriptors and Length of Capsular Contact for Determining Extraprostatic Tumor Extension at Multiparametric Prostate MRI. , 2018, AJR. American journal of roentgenology.

[44]  G. Andriole,et al.  Prostate Magnetic Resonance Imaging Provides Limited Incremental Value Over the Memorial Sloan Kettering Cancer Center Preradical Prostatectomy Nomogram. , 2017, Urology.

[45]  L. Salomon,et al.  Integration of MRI to clinical nomogram for predicting pathological stage before radical prostatectomy , 2017, World Journal of Urology.

[46]  Adam T Froemming,et al.  The Incremental Role of Magnetic Resonance Imaging for Prostate Cancer Staging before Radical Prostatectomy. , 2017, European urology.

[47]  Dan Jackson,et al.  Power analysis for random‐effects meta‐analysis , 2017, Research synthesis methods.

[48]  Xiaoying Wang,et al.  Development and comparison of a Chinese nomogram adding multi-parametric MRI information for predicting extracapsular extension of prostate cancer , 2016, Oncotarget.

[49]  J Alfred Witjes,et al.  Accuracy of Magnetic Resonance Imaging for Local Staging of Prostate Cancer: A Diagnostic Meta-analysis. , 2016, European urology.

[50]  F. Montorsi,et al.  Apparent diffusion coefficient in the evaluation of side-specific extracapsular extension in prostate cancer: Development and external validation of a nomogram of clinical use. , 2016, Urologic oncology.

[51]  Xin Gao,et al.  ERG rearrangement as a novel marker for predicting the extra-prostatic extension of clinically localised prostate cancer. , 2016, Oncology letters.

[52]  R. Saouaf,et al.  Multiparametric MRI Improves Accuracy of Clinical Nomograms for Predicting Extracapsular Extension of Prostate Cancer. , 2015, Urology.

[53]  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.

[54]  B. Trock,et al.  The relationship between the extent of extraprostatic extension and survival following radical prostatectomy. , 2015, European urology.

[55]  G. Collins,et al.  Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement , 2015, BMC Medicine.

[56]  A. Paul,et al.  Do prostate cancer nomograms give accurate information when applied to European patients? , 2015, Scandinavian journal of urology.

[57]  Sheng-Tang Wu,et al.  Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population , 2014, Journal of the Chinese Medical Association : JCMA.

[58]  Markus Graefen,et al.  Positive surgical margins after radical prostatectomy: a systematic review and contemporary update. , 2014, European urology.

[59]  Thomas Brendan Murphy,et al.  Evaluation of prediction models for the staging of prostate cancer , 2013, BMC Medical Informatics and Decision Making.

[60]  Y. Kanai,et al.  Ability of preoperative 3.0‐Tesla magnetic resonance imaging to predict the absence of side‐specific extracapsular extension of prostate cancer , 2013, International journal of urology : official journal of the Japanese Urological Association.

[61]  N. Hara,et al.  Lower urinary tract symptoms in patients with Niigata Minamata disease: A case–control study 50 years after methyl mercury pollution , 2013, International journal of urology : official journal of the Japanese Urological Association.

[62]  P. Grenier,et al.  Accuracy of high resolution (1.5 tesla) pelvic phased array magnetic resonance imaging (MRI) in staging prostate cancer in candidates for radical prostatectomy: results from a prospective study. , 2013, Urologic oncology.

[63]  B. Trock,et al.  An updated prostate cancer staging nomogram (Partin tables) based on cases from 2006 to 2011 , 2013, BJU international.

[64]  J. Fütterer,et al.  ESUR prostate MR guidelines 2012 , 2012, European Radiology.

[65]  M. Cooperberg,et al.  The University of California, San Francisco Cancer of the Prostate Risk Assessment score: a straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. , 2005, The Journal of urology.

[66]  Choung-Soo Kim,et al.  Nomograms for the Prediction of Pathologic Stage of Clinically Localized Prostate Cancer in Korean Men , 2005, Journal of Korean medical science.

[67]  Michael W Kattan,et al.  Prostate cancer: incremental value of endorectal MR imaging findings for prediction of extracapsular extension. , 2004, Radiology.

[68]  M. Kattan,et al.  Predicting the presence and side of extracapsular extension: a nomogram for staging prostate cancer. , 2004, The Journal of urology.

[69]  Michael W Kattan,et al.  The addition of interleukin-6 soluble receptor and transforming growth factor beta1 improves a preoperative nomogram for predicting biochemical progression in patients with clinically localized prostate cancer. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[70]  D. Altman,et al.  Measuring inconsistency in meta-analyses , 2003, BMJ : British Medical Journal.

[71]  C M Rutter,et al.  A hierarchical regression approach to meta‐analysis of diagnostic test accuracy evaluations , 2001, Statistics in medicine.

[72]  T. Uchida,et al.  Improved predictability of extracapsular extension and seminal vesicle involvement based on clinical and biopsy findings in prostate cancer in Japanese men. , 1998, Urology.

[73]  A W Partin,et al.  Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer. A multi-institutional update. , 1997, JAMA.

[74]  G. Collins,et al.  PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies , 2019, Annals of Internal Medicine.

[75]  C. Siegel Re: Diagnosis of Extracapsular Extension of Prostate Cancer on Prostate MRI: Impact of Second-Opinion Readings by Subspecialized Genitourinary Oncologic Radiologists. , 2016, The Journal of urology.

[76]  Hilde van der Togt,et al.  Publisher's Note , 2003, J. Netw. Comput. Appl..

[77]  P. Walsh,et al.  Combination of prostate-specific antigen, clinical stage, and Gleason score to predict pathological stage of localized prostate cancer: a multi-institutional update. , 1997, The Journal of urology.