Literature DB >> 28801025

Toward an MRI-based nomogram for the prediction of transperineal prostate biopsy outcome: A physician and patient decision tool.

Su-Min Lee1, Sidath H Liyanage2, Wahyu Wulaningsih3, Konrad Wolfe4, Thomas Carr5, Choudhry Younis5, Mieke Van Hemelrijck3, Rick Popert6, Peter Acher5.   

Abstract

PURPOSE: To develop and internally validate a nomogram using biparametric magnetic resonance imaging (B-MRI)-derived variables for the prediction of prostate cancer at transperineal sector-guided prostate biopsy (TPSB). SUBJECTS/PATIENTS AND METHODS: Consecutive patients referred to our institution with raised prostate-specific antigen (PSA), abnormal prostate examination, or persistent suspicion of prostate cancer after previous transrectal biopsy between July 2012 and November 2015 were reviewed from a prospective database. All patients underwent prebiopsy B-MRI with T2-weighted and diffusion-weighted imaging sequences, followed by 24 to 40 core TPSB with additional targeted cores using cognitive registration. Univariable and multivariable logistic regression analysis was used to determine predictors of prostate cancer outcomes. Multivariable coefficients were used to construct 2 MRI-based nomograms to predict any and significant (Gleason 4 or maximum cancer core length ≥6mm) prostate cancer at TPSB. Bootstrap resamples were used for internal validation. Accuracy was assessed by calculating the concordance index.
RESULTS: In total, 615 men were included in the study. Prostate cancer was diagnosed in 317 (51.5%) men with significant cancer diagnosed in 237 (38.5%) men. Age, Prostate Imaging Reporting and Data System (PI-RADS) score, PSA, PSA density, and primary biopsy were predictors of prostate cancer at TPSB on univariable analysis (P<0.0001). PSA showed strong correlation with PSA density and was excluded. The remaining variables were all independent predictors of prostate cancer on multivariable analysis (P<0.0001) and used to generate the nomograms. Both nomograms showed good discrimination for prostate cancer, with a concordance index of 87% for any cancer and 92% for significant disease. Using a nomogram-derived probability threshold of<15%, 111 (18.0%) biopsies can be saved, at the expense of 3 missed significant prostate cancers.
CONCLUSIONS: These internally validated MR-based nomograms were able to accurately predict TPSB outcomes for prostate cancer, especially significant disease. Our findings support the combination of prebiopsy MRI results and clinical factors as part of the biopsy decision-making process.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Magnetic resonance imaging; Nomogram; Prostate biopsy; Prostate cancer; Risk assessment

Mesh:

Substances:

Year:  2017        PMID: 28801025     DOI: 10.1016/j.urolonc.2017.07.018

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


  7 in total

1.  Comparative Analysis of PSA Density and an MRI-Based Predictive Model to Improve the Selection of Candidates for Prostate Biopsy.

Authors:  Juan Morote; Angel Borque-Fernando; Marina Triquell; Anna Celma; Lucas Regis; Richard Mast; Inés M de Torres; María E Semidey; José M Abascal; Pol Servian; Anna Santamaría; Jacques Planas; Luis M Esteban; Enrique Trilla
Journal:  Cancers (Basel)       Date:  2022-05-11       Impact factor: 6.575

Review 2.  Multivariate risk prediction tools including MRI for individualized biopsy decision in prostate cancer diagnosis: current status and future directions.

Authors:  Ivo G Schoots; Monique J Roobol
Journal:  World J Urol       Date:  2019-03-13       Impact factor: 4.226

3.  Comparison of three lymph node staging methods for predicting outcome in breast cancer patients with mastectomy.

Authors:  Meng-Shen Wang; Mo-Zhi Wang; Zhenning Wang; Yongxi Song; Peng Gao; Pengliang Wang; Chong Wang; Xueting Yu; Fengheng Wei; Jingyi Guo; Yingying Xu
Journal:  Ann Transl Med       Date:  2021-02

4.  Nomogram Predicts Risk and Prognostic Factors for Bone Metastasis of Pancreatic Cancer: A Population-Based Analysis.

Authors:  Wei Zhang; Lichen Ji; Xijun Wang; Senbo Zhu; Junchao Luo; Yin Zhang; Yu Tong; Fabo Feng; Yao Kang; Qing Bi
Journal:  Front Endocrinol (Lausanne)       Date:  2022-03-09       Impact factor: 5.555

5.  The value of magnetic resonance imaging and ultrasonography (MRI/US)-fusion biopsy in clinically significant prostate cancer detection in patients with biopsy-naïve men according to PSA levels: A propensity score matching analysis.

Authors:  Hye J Byun; Teak J Shin; Wonho Jung; Ji Y Ha; Byung H Kim; Young H Kim
Journal:  Prostate Int       Date:  2021-11-04

Review 6.  Magnetic Resonance Imaging-Based Predictive Models for Clinically Significant Prostate Cancer: A Systematic Review.

Authors:  Marina Triquell; Miriam Campistol; Ana Celma; Lucas Regis; Mercè Cuadras; Jacques Planas; Enrique Trilla; Juan Morote
Journal:  Cancers (Basel)       Date:  2022-09-29       Impact factor: 6.575

7.  The Barcelona Predictive Model of Clinically Significant Prostate Cancer.

Authors:  Juan Morote; Angel Borque-Fernando; Marina Triquell; Anna Celma; Lucas Regis; Manel Escobar; Richard Mast; Inés M de Torres; María E Semidey; José M Abascal; Carles Sola; Pol Servian; Daniel Salvador; Anna Santamaría; Jacques Planas; Luis M Esteban; Enrique Trilla
Journal:  Cancers (Basel)       Date:  2022-03-21       Impact factor: 6.639

  7 in total

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