Literature DB >> 28987685

Sub-differentiating equivocal PI-RADS-3 lesions in multiparametric magnetic resonance imaging of the prostate to improve cancer detection.

N L Hansen1, B C Koo2, A Y Warren3, C Kastner4, T Barrett5.   

Abstract

PURPOSE: To evaluate sub-differentiation of PI-RADS-3 prostate lesions using pre-defined T2- and diffusion-weighted (DWI) MRI criteria, to aid the biopsy decision process.
METHODS: 143 patients with PIRADS-3 index lesions on MRI underwent targeted transperineal-MR/US fusion biopsy. Radiologists with 2 and 7-years experience performed blinded retrospective second-reads using set criteria and assigned biopsy recommendations. Inter-reader agreement, Gleason score (GS), positive (PPV) predictive values (±95% confidence intervals) were calculated and compared by Fisher's exact test with Bonferroni-Hom correction.
RESULTS: 43% (61/143) patients had GS 6-10 and 21% (30/143) GS≥3+4 cancer. For peripheral zone lesions, significant differences in any cancer detection were found for shape (0.26±0.13 geographical vs. 0.69±0.23 rounded; p=0.0055) and ADC (mild 0.21±0.12 vs marked 0.81±0.19; p=0.0001). For transition zone, significantly increased cancer detection was shown for location (anterior 0.63±0.15 vs. mid/posterior 0.31±0.14; p=0.0048), border (pseudo-capsule 0.32±0.14 vs. ill-defined 0.61±0.15; p=0.0092), and ADC (mild 0.35±0.12 vs marked restriction 0.68±0.17; p=0.0057). Biopsy recommendations had 62% inter-reader agreement (89/143). Experienced reader PPVs were significantly higher for any cancer with "biopsy-recommended" 0.61±0.11 vs. "no biopsy" 0.21±0.10 (p=0.0001), and for GS 7-10 cancers: 0.32±0.10 vs. 0.08±0.07, respectively (p=0.0003).
CONCLUSION: Identification of certain objective imaging criteria as well as a subjective biopsy recommendation from an experienced radiologist can help to increase the predictive value of equivocal prostate lesions and inform the decision making process of whether or not to biopsy.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Indeterminate; MRI; PIRADS; Prostate

Mesh:

Year:  2017        PMID: 28987685     DOI: 10.1016/j.ejrad.2017.08.017

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  13 in total

1.  Clinico-radiological characteristic-based machine learning in reducing unnecessary prostate biopsies of PI-RADS 3 lesions with dual validation.

Authors:  Yansheng Kan; Qing Zhang; Jiange Hao; Wei Wang; Junlong Zhuang; Jie Gao; Haifeng Huang; Jing Liang; Giancarlo Marra; Giorgio Calleris; Marco Oderda; Xiaozhi Zhao; Paolo Gontero; Hongqian Guo
Journal:  Eur Radiol       Date:  2020-06-10       Impact factor: 5.315

2.  A radiomics machine learning-based redefining score robustly identifies clinically significant prostate cancer in equivocal PI-RADS score 3 lesions.

Authors:  Ying Hou; Mei-Ling Bao; Chen-Jiang Wu; Jing Zhang; Yu-Dong Zhang; Hai-Bin Shi
Journal:  Abdom Radiol (NY)       Date:  2020-08-01

3.  Retrospective analysis of the development of PIRADS 3 lesions over time: when is a follow-up MRI reasonable?

Authors:  Fabian Steinkohl; Leonhard Gruber; Jasmin Bektic; Udo Nagele; Friedrich Aigner; Thomas R W Herrmann; Michael Rieger; Daniel Junker
Journal:  World J Urol       Date:  2017-12-14       Impact factor: 4.226

4.  Multicenter analysis of clinical and MRI characteristics associated with detecting clinically significant prostate cancer in PI-RADS (v2.0) category 3 lesions.

Authors:  Bashir Al Hussein Al Awamlh; Leonard S Marks; Geoffrey A Sonn; Shyam Natarajan; Richard E Fan; Michael D Gross; Elizabeth Mauer; Samprit Banerjee; Stefanie Hectors; Sigrid Carlsson; Daniel J Margolis; Jim C Hu
Journal:  Urol Oncol       Date:  2020-04-17       Impact factor: 3.498

5.  Added value of diffusion-weighted MRI for nodal radiotherapy planning in pelvic malignancies.

Authors:  N Sushentsev; H Martin; Y Rimmer; T Barrett
Journal:  Clin Transl Oncol       Date:  2019-03-13       Impact factor: 3.405

6.  Evaluating the performance of clinical and radiological data in predicting prostate cancer in prostate imaging reporting and data system version 2.1 category 3 lesions of the peripheral and the transition zones.

Authors:  Caterina Gaudiano; Lorenzo Bianchi; Beniamino Corcioni; Francesca Giunchi; Riccardo Schiavina; Federica Ciccarese; Lorenzo Braccischi; Arianna Rustici; Michelangelo Fiorentino; Eugenio Brunocilla; Rita Golfieri
Journal:  Int Urol Nephrol       Date:  2021-11-25       Impact factor: 2.370

7.  Combining clinical and MRI data to manage PI-RADS 3 lesions and reduce excessive biopsy.

Authors:  Shuo Yang; Wenlu Zhao; Shuangxiu Tan; Yueyue Zhang; Chaogang Wei; Tong Chen; Junkang Shen
Journal:  Transl Androl Urol       Date:  2020-06

8.  Bi-parametric magnetic resonance imaging based radiomics for the identification of benign and malignant prostate lesions: cross-vendor validation.

Authors:  Xuefu Ji; Jiayi Zhang; Yuguo Tang; Wei Xia; Wei Shi; Dong He; Jie Bao; Xuedong Wei; Yuhua Huang; Yangchuan Liu; Jyh-Cheng Chen; Xin Gao
Journal:  Phys Eng Sci Med       Date:  2021-06-01

9.  Comparison of Likert and PI-RADS version 2 MRI scoring systems for the detection of clinically significant prostate cancer.

Authors:  Jeries P Zawaideh; Evis Sala; Maria Pantelidou; Nadeem Shaida; Brendan Koo; Iztok Caglic; Anne Y Warren; Luca Carmisciano; Kasra Saeb-Parsy; Vincent J Gnanapragasam; Christof Kastner; Tristan Barrett
Journal:  Br J Radiol       Date:  2020-06-11       Impact factor: 3.039

Review 10.  Multiparametric MRI in Active Surveillance of Prostate Cancer: An Overview and a Practical Approach.

Authors:  Chau Hung Lee; Teck Wei Tan; Cher Heng Tan
Journal:  Korean J Radiol       Date:  2021-04-01       Impact factor: 3.500

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