Literature DB >> 31547938

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.

Giorgio Gandaglia1, Guillaume Ploussard2, Massimo Valerio3, Agostino Mattei4, Cristian Fiori5, Mathieu Roumiguié6, Nicola Fossati1, Armando Stabile1, Jean-Baptiste Beauval6, Bernard Malavaud6, Simone Scuderi1, Francesco Barletta1, Marco Moschini4, Stefania Zamboni4, Arnas Rakauskas3, Zhe Tian7, Pierre I Karakiewicz7, Francesco De Cobelli8, Francesco Porpiglia5, Francesco Montorsi9, Alberto Briganti10.   

Abstract

BACKGROUND: The combined role of multiparametric magnetic resonance imaging (mp-MRI), and magnetic resonance imaging (MRI)-targeted and concomitant systematic biopsies in the identification of prostate cancer (PCa) patients at a higher risk of adverse pathology at radical prostatectomy (RP) is still unclear.
OBJECTIVE: To develop novel models to predict extracapsular extension (ECE), seminal vesicle invasion (SVI), or upgrading in patients diagnosed with MRI-targeted and concomitant systematic biopsies. DESIGN, SETTING, AND PARTICIPANTS: We included 614 men with clinical stage≤T2 at digital rectal examination who underwent MRI-targeted biopsy with concomitant systematic biopsy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSES: Logistic regression analyses predicting ECE, SVI, and upgrading (ie, a shift from biopsy International Society of Urological Pathology grade group to any higher grade at RP) based on clinical variables with or without mp-MRI features and systematic biopsy information (the percentage of cores with grade group ≥2 PCa) were developed and internally validated. The area under the curve (AUC) was used to identify the models with the highest discrimination. Decision-curve analyses (DCAs) determined the net benefit associated with their use. RESULTS AND LIMITATIONS: Overall, 333 (54%), 88 (14%), and 169 (27%) patients had ECE, SVI, and upgrading at RP, respectively. The inclusion of mp-MRI data improved the discrimination of clinical models for ECE (67% vs 70%) and SVI (74% vs 76%). Models including mp-MRI, and MRI-targeted and concomitant systematic biopsy information achieved the highest AUC at internal validation for ECE (73%), SVI (81%), and upgrading (73%) and represented the basis for three risk calculators that yield the highest net benefit at DCA.
CONCLUSIONS: Not only mp-MRI and MRI-targeted sampling, but also concomitant systematic biopsies provide significant information to identify patients at a higher risk of adverse pathology. Although omitting systematic prostate sampling at the time of MRI-targeted biopsy might be associated with a reduced risk of detecting insignificant PCa and lower patient discomfort, it reduces the ability to accurately predict pathological features. PATIENT
SUMMARY: The combination of multiparametric magnetic resonance imaging (mp-MRI) with accurate biopsy information on MRI-targeted and systematic biopsies improves the accuracy of multivariable models based on clinical and mp-MRI data alone. Correct mp-MRI interpretation and proper extensive prostate sampling are both needed to predict adverse pathology accurately at radical prostatectomy.
Copyright © 2019 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Extracapsular extension; Magnetic resonance imaging–targeted biopsy; Multiparametric magnetic resonance imaging; Prostate cancer; Radical prostatectomy; Seminal vesicle invasion; Upgrading

Year:  2019        PMID: 31547938     DOI: 10.1016/j.eururo.2019.09.005

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  24 in total

1.  Artificial intelligence is a promising prospect for the detection of prostate cancer extracapsular extension with mpMRI: a two-center comparative study.

Authors:  Ying Hou; Yi-Hong Zhang; Jie Bao; Mei-Ling Bao; Guang Yang; Hai-Bin Shi; Yang Song; Yu-Dong Zhang
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-21       Impact factor: 9.236

2.  Prostate biopsy in the era of MRI-targeting: towards a judicious use of additional systematic biopsy.

Authors:  Dominik Deniffel; Nathan Perlis; Sangeet Ghai; Stephanie Girgis; Gerard M Healy; Neil Fleshner; Robert Hamilton; Girish Kulkarni; Ants Toi; Theodorus van der Kwast; Alexandre Zlotta; Antonio Finelli; Masoom A Haider
Journal:  Eur Radiol       Date:  2022-05-04       Impact factor: 7.034

3.  Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer.

Authors:  Hailang Liu; Kun Tang; Ding Xia; Xinguang Wang; Wei Zhu; Liang Wang; Weimin Yang; Ejun Peng; Zhiqiang Chen
Journal:  Cancer Manag Res       Date:  2020-08-24       Impact factor: 3.989

4.  Predicting Prostate Cancer Upgrading of Biopsy Gleason Grade Group at Radical Prostatectomy Using Machine Learning-Assisted Decision-Support Models.

Authors:  Hailang Liu; Kun Tang; Ejun Peng; Liang Wang; Ding Xia; Zhiqiang Chen
Journal:  Cancer Manag Res       Date:  2020-12-22       Impact factor: 3.989

5.  Clinical outcomes associated with prostate cancer conspicuity on biparametric and multiparametric MRI: a protocol for a systematic review and meta-analysis of biochemical recurrence following radical prostatectomy.

Authors:  Naomi Morka; Benjamin S Simpson; Rhys Ball; Alex Freeman; Alex Kirkham; Daniel Kelly; Hayley C Whitaker; Mark Emberton; Joseph M Norris
Journal:  BMJ Open       Date:  2021-05-05       Impact factor: 2.692

Review 6.  Role of Multiparametric Magnetic Resonance Imaging in Predicting Pathologic Outcomes in Prostate Cancer.

Authors:  Niklas Harland; Arnulf Stenzl; Tilman Todenhöfer
Journal:  World J Mens Health       Date:  2020-06-24       Impact factor: 5.400

7.  Development of an Indian nomogram for predicting extracapsular extension in prostate cancer.

Authors:  Chandran Ravi; Kalavampara V Sanjeevan; Appu Thomas; Ginil Kumar Pooleri
Journal:  Indian J Urol       Date:  2021-01-01

8.  Predictive model containing PI-RADS v2 score for postoperative seminal vesicle invasion among prostate cancer patients.

Authors:  Hao Wang; Mingjian Ruan; He Wang; Xueying Li; Xuege Hu; Hua Liu; Binyi Zhou; Gang Song
Journal:  Transl Androl Urol       Date:  2021-02

9.  Prognostic value of seminal vesicle invasion on preoperative multi-parametric magnetic resonance imaging in pathological stage T3b prostate cancer.

Authors:  Jung Kwon Kim; Hak Jong Lee; Sung Il Hwang; Gheeyoung Choe; Sung Kyu Hong
Journal:  Sci Rep       Date:  2020-03-30       Impact factor: 4.379

10.  Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

Authors:  Lizhi Shao; Ye Yan; Zhenyu Liu; Xiongjun Ye; Haizhui Xia; Xuehua Zhu; Yuting Zhang; Zhiying Zhang; Huiying Chen; Wei He; Cheng Liu; Min Lu; Yi Huang; Lulin Ma; Kai Sun; Xuezhi Zhou; Guanyu Yang; Jian Lu; Jie Tian
Journal:  Theranostics       Date:  2020-09-02       Impact factor: 11.556

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