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Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer
OBJECTIVE: To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsi...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457849/ https://www.ncbi.nlm.nih.gov/pubmed/32922077 http://dx.doi.org/10.2147/CMAR.S260986 |
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author | Liu, Hailang Tang, Kun Xia, Ding Wang, Xinguang Zhu, Wei Wang, Liang Yang, Weimin Peng, Ejun Chen, Zhiqiang |
author_facet | Liu, Hailang Tang, Kun Xia, Ding Wang, Xinguang Zhu, Wei Wang, Liang Yang, Weimin Peng, Ejun Chen, Zhiqiang |
author_sort | Liu, Hailang |
collection | PubMed |
description | OBJECTIVE: To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies. PATIENTS AND METHODS: We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator. RESULTS: A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit. CONCLUSION: We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients. |
format | Online Article Text |
id | pubmed-7457849 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Dove |
record_format | MEDLINE/PubMed |
spelling | pubmed-74578492020-09-11 Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer Liu, Hailang Tang, Kun Xia, Ding Wang, Xinguang Zhu, Wei Wang, Liang Yang, Weimin Peng, Ejun Chen, Zhiqiang Cancer Manag Res Original Research OBJECTIVE: To develop novel models for predicting extracapsular extension (EPE), seminal vesicle invasion (SVI), or upgrading in prostate cancer (PCa) patients using clinical parameters, biparametric magnetic resonance imaging (bp-MRI), and transrectal ultrasonography (TRUS)-guided systematic biopsies. PATIENTS AND METHODS: We retrospectively collected data from PCa patients who underwent standard (12-core) systematic biopsy and radical prostatectomy. To develop predictive models, the following variables were included in multivariable logistic regression analyses: total prostate-specific antigen (TPSA), central transition zone volume (CTZV), prostate-specific antigen (PSAD), maximum diameter of the index lesion at bp-MRI, EPE at bp-MRI, SVI at bp-MRI, biopsy Gleason grade group, and number of positive biopsy cores. Three risk calculators were built based on the coefficients of the logit function. The area under the curve (AUC) was applied to determine the models with the highest discrimination. Decision curve analyses (DCAs) were performed to evaluate the net benefit of each risk calculator. RESULTS: A total of 222 patients were included in this study. Overall, 83 (37.4%), 75 (33.8%), and 107 (48.2%) patients had EPE, SVI, and upgrading at final pathology, respectively. The addition of bp-MRI data improved the discrimination of models for predicting SVI (0.807 vs 0.816) and upgrading (0.548 vs 0.625) but not EPE (0.766 vs 0.763). Similarly, models including clinical parameters, bp-MRI data, and information on systematic biopsies achieved the highest AUC in the prediction of EPE (0.842), SVI (0.913), and upgrading (0.794), and the three corresponding risk calculators yielded the highest net benefit. CONCLUSION: We developed three easy-to-use risk calculators for the prediction of adverse pathological features based on patient clinical parameters, bp-MRI data, and information on systematic biopsies. This may be greatly beneficial to urologists in the decision-making process for PCa patients. Dove 2020-08-24 /pmc/articles/PMC7457849/ /pubmed/32922077 http://dx.doi.org/10.2147/CMAR.S260986 Text en © 2020 Liu et al. http://creativecommons.org/licenses/by-nc/3.0/ This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). |
spellingShingle | Original Research Liu, Hailang Tang, Kun Xia, Ding Wang, Xinguang Zhu, Wei Wang, Liang Yang, Weimin Peng, Ejun Chen, Zhiqiang Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title | Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title_full | Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title_fullStr | Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title_full_unstemmed | Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title_short | Added Value of Biparametric MRI and TRUS-Guided Systematic Biopsies to Clinical Parameters in Predicting Adverse Pathology in Prostate Cancer |
title_sort | added value of biparametric mri and trus-guided systematic biopsies to clinical parameters in predicting adverse pathology in prostate cancer |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7457849/ https://www.ncbi.nlm.nih.gov/pubmed/32922077 http://dx.doi.org/10.2147/CMAR.S260986 |
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