Literature DB >> 32243728

Review of the accuracy of multi-parametric MRI prostate in detecting prostate cancer within a local reporting service.

Wei Che Tsai1,2, Lee Field1,3, Sophie Stewart1,3, Martin Schultz2,4.   

Abstract

INTRODUCTION: Multi-parametric magnetic resonance imaging of the prostate is crucial in detecting prostate cancer (CaP) and staging local disease. The Prostate Imaging Reporting and Data System (PIRADS) scoring system is used to assess and classify lesions and enables communication between clinicians and radiologists. This study aimed to assess the accuracy of PIRADSv2 in detecting CaP using histopathology specimens within our local service.
METHODS: This retrospective study included 192 patients between September 2016 and May 2019. All had mpMRI prostate examinations prior to biopsy or prostatectomy. Lesions on MRI were assigned a PIRADS score and comparison made with histopathology results. Gleason score ≥7 was considered as clinically significant prostate cancer (csCaP). We calculated accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for detecting all CaP and csCaP.
RESULTS: In the PIRADS 3 group, 32% were Gleason 6 and 32% were Gleason 7 lesions. In the PIRADS 4 group, 37% were Gleason 6 and 41% were Gleason ≥7. For PIRADS 5 lesions, 32% were Gleason 6 and 68% were Gleason ≥7. For all CaP, sensitivity was 84.7%, specificity 54.6%, PPV 82.3% and NPV 58.8%. For csCaP Gleason ≥7, PIRADS cut-off ≥3 had sensitivity, specificity, PPV and NPV of 95.7%, 39.3%, 47.5% and 94.1%, respectively, and cut-off ≥4 had sensitivity, specificity, PPV and NPV of 84.3%, 53.3%, 50.9% and 85.5%.
CONCLUSIONS: This study confirms PIRADS has high accuracy, sensitivity and NPV for detecting all CaP and csCaP. A high NPV may obviate need for biopsy in low-risk patients.
© 2020 The Royal Australian and New Zealand College of Radiologists.

Entities:  

Keywords:  Gleason; PIRADS; multi-parametric MRI; prostate biopsy; prostate cancer

Mesh:

Year:  2020        PMID: 32243728     DOI: 10.1111/1754-9485.13029

Source DB:  PubMed          Journal:  J Med Imaging Radiat Oncol        ISSN: 1754-9477            Impact factor:   1.735


  4 in total

1.  3D Isotropic Super-resolution Prostate MRI Using Generative Adversarial Networks and Unpaired Multiplane Slices.

Authors:  Yucheng Liu; Yulin Liu; Rami Vanguri; Daniel Litwiller; Michael Liu; Hao-Yun Hsu; Richard Ha; Hiram Shaish; Sachin Jambawalikar
Journal:  J Digit Imaging       Date:  2021-09-10       Impact factor: 4.903

2.  The diagnostic accuracy of multiparametric MRI for detection and localization of prostate cancer depends on the affected region.

Authors:  Martina Martins; Stefano Regusci; Stephane Rohner; Ildiko Szalay-Quinodoz; Georges-Antoine De Boccard; Louise Strom; Gerjon Hannink; Sonia Ramos-Pascual; Charles Henry Rochat
Journal:  BJUI Compass       Date:  2020-11-28

3.  Can machine learning-based analysis of multiparameter MRI and clinical parameters improve the performance of clinically significant prostate cancer diagnosis?

Authors:  Tao Peng; JianMing Xiao; Lin Li; BingJie Pu; XiangKe Niu; XiaoHui Zeng; ZongYong Wang; ChaoBang Gao; Ci Li; Lin Chen; Jin Yang
Journal:  Int J Comput Assist Radiol Surg       Date:  2021-10-22       Impact factor: 2.924

Review 4.  Diffusion-Weighted MRI in the Genitourinary System.

Authors:  Thomas De Perrot; Christine Sadjo Zoua; Carl G Glessgen; Diomidis Botsikas; Lena Berchtold; Rares Salomir; Sophie De Seigneux; Harriet C Thoeny; Jean-Paul Vallée
Journal:  J Clin Med       Date:  2022-03-30       Impact factor: 4.241

  4 in total

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