Literature DB >> 24863013

Comparing 3-T multiparametric MRI and the Partin tables to predict organ-confined prostate cancer after radical prostatectomy.

Rajan T Gupta1, Kamil F Faridi2, Abhay A Singh3, Niccolò M Passoni3, Kirema Garcia-Reyes4, John F Madden5, Thomas J Polascik3.   

Abstract

OBJECTIVES: The purpose of our study was to test our hypothesis that multiparametric magnetic resonance imaging (mpMRI) may have a higher prognostic accuracy than the Partin tables in predicting organ-confined (OC) prostate cancer and extracapsular extension (ECE) after radical prostatectomy (RP). METHODS AND MATERIALS: After institutional review board approval, we retrospectively reviewed 60 patients who underwent 3-T mpMRI before RP. mpMRI was used to assess clinical stage and the updated version of the Partin tables was used to calculate the probability of each patient to harbor OC disease. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of mpMRI in detecting OC and ECE were calculated. Logistic regression models predicting OC pathology were created using either clinical stage at mpMRI or Partin tables probability. The area under the curve was used to calculate the predictive accuracy of each model.
RESULTS: Median prostate-specific antigen level at diagnosis was 5 ng/ml (range: 4.1-6.7 ng/ml). Overall, 52 (86.7%) men had cT1 disease, 7 (11.7%) had cT2a/b, and 1 (1.6%) had cT3b at digital rectal examination. Biopsy Gleason score was 6, 3+4 = 7, 4+3 = 7, 8, and 9 to 10 in 28 (46.7%), 15 (25%), 3 (5%), 10 (16.7%), and 4 (6.6%) patients, respectively. At mpMRI, clinical stage was defined as cT2a/b, cT2c, cT3a, and cT3b in 11 (18.3%), 23 (38.3%), 21 (35%), and 5 (8.4%) patients, respectively. At final pathology, 38 men (63.3%) had OC disease, whereas 18 (30%) had ECE and 4 (6.7%) had seminal vesicle invasion. The sensitivity, specificity, PPV, and NPV of mpMRI in detecting OC disease were 81.6%, 86.4%, 91.2%, and 73.1%, respectively, whereas in detecting ECE were 77.8%, 83.4%, 66.7%, and 89.7%, respectively. At logistic regression, both the Partin tables-derived probability and the mpMRI clinical staging were significantly associated with OC disease (all P<0.01). The area under the curves of the model built using the Partin tables and that of the mpMRI model were 0.62 and 0.82, respectively (P = 0.04).
CONCLUSIONS: The predictive accuracy of mpMRI in predicting OC disease on pathological analysis is significantly greater than that of the Partin tables. mpMRI had a high PPV (91.2%) when predicting OC disease and a high NPV (89.7%) with regard to ECE. mpMRI should be considered when planning prostate cancer treatment in addition to readily available clinical parameters.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Multiparametric prostate MRI; Partin tables; Prostate cancer

Mesh:

Year:  2014        PMID: 24863013     DOI: 10.1016/j.urolonc.2014.04.017

Source DB:  PubMed          Journal:  Urol Oncol        ISSN: 1078-1439            Impact factor:   3.498


  31 in total

1.  Added Value of Multiparametric Magnetic Resonance Imaging to Clinical Nomograms for Predicting Adverse Pathology in Prostate Cancer.

Authors:  Kareem N Rayn; Jonathan B Bloom; Samuel A Gold; Graham R Hale; Joseph A Baiocco; Sherif Mehralivand; Marcin Czarniecki; Vikram K Sabarwal; Vladimir Valera; Bradford J Wood; Maria J Merino; Peter Choyke; Baris Turkbey; Peter A Pinto
Journal:  J Urol       Date:  2018-05-29       Impact factor: 7.450

Review 2.  Current trends and new frontiers in focal therapy for localized prostate cancer.

Authors:  Melissa H Mendez; Daniel Y Joh; Rajan Gupta; Thomas J Polascik
Journal:  Curr Urol Rep       Date:  2015-06       Impact factor: 3.092

3.  Preoperative Prediction of Extracapsular Extension: Radiomics Signature Based on Magnetic Resonance Imaging to Stage Prostate Cancer.

Authors:  Shuai Ma; Huihui Xie; Huihui Wang; Jiejin Yang; Chao Han; Xiaoying Wang; Xiaodong Zhang
Journal:  Mol Imaging Biol       Date:  2020-06       Impact factor: 3.488

Review 4.  Prostate Biopsy in Active Surveillance Protocols: Immediate Re-biopsy and Timing of Subsequent Biopsies.

Authors:  Jonathan H Wang; Tracy M Downs; E Jason Abel; Kyle A Richards; David F Jarrard
Journal:  Curr Urol Rep       Date:  2017-07       Impact factor: 3.092

5.  Multiparametric magnetic resonance imaging versus Partin tables and the Memorial Sloan-Kettering cancer center nomogram in risk stratification of patients with prostate cancer referred to external beam radiation therapy.

Authors:  Rossano Girometti; Martina Pancot; Marco Andrea Signor; Martina Urbani; Luca Balestreri; Chiara Zuiani
Journal:  Radiol Med       Date:  2018-05-12       Impact factor: 3.469

Review 6.  Optimizing Patient Population for MP-MRI and Fusion Biopsy for Prostate Cancer Detection.

Authors:  Thomas P Frye; Peter A Pinto; Arvin K George
Journal:  Curr Urol Rep       Date:  2015-07       Impact factor: 3.092

Review 7.  Risk stratification of prostate cancer: integrating multiparametric MRI, nomograms and biomarkers.

Authors:  Matthew J Watson; Arvin K George; Mahir Maruf; Thomas P Frye; Akhil Muthigi; Michael Kongnyuy; Subin G Valayil; Peter A Pinto
Journal:  Future Oncol       Date:  2016-07-12       Impact factor: 3.404

Review 8.  Extraprostatic extension in prostate cancer: primer for radiologists.

Authors:  Alice C Shieh; Ezgi Guler; Vijayanadh Ojili; Raj Mohan Paspulati; Robin Elliott; Nikhil H Ramaiya; Sree Harsha Tirumani
Journal:  Abdom Radiol (NY)       Date:  2020-12

9.  A urologist's perspective on prostate cancer imaging: past, present, and future.

Authors:  Arvin K George; Baris Turkbey; Subin G Valayil; Akhil Muthigi; Francesca Mertan; Michael Kongnyuy; Peter A Pinto
Journal:  Abdom Radiol (NY)       Date:  2016-05

Review 10.  Multiparametric prostate magnetic resonance imaging in the evaluation of prostate cancer.

Authors:  Baris Turkbey; Anna M Brown; Sandeep Sankineni; Bradford J Wood; Peter A Pinto; Peter L Choyke
Journal:  CA Cancer J Clin       Date:  2015-11-23       Impact factor: 508.702

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