Diagnostic performance of prediction models for extraprostatic extension in prostate cancer: a systematic review and meta-analysis
暂无分享,去创建一个
Jiawen Zhang | Jiahao Gao | F. Han | Longlin Yin | Lushun Zhang | Meilin Zhu | Yong Yang
[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.