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Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy
OBJECTIVE: This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers. METHODS: First, we retrospectively collected the clinicopathologic data from 475 patients w...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751862/ https://www.ncbi.nlm.nih.gov/pubmed/36531011 http://dx.doi.org/10.3389/fonc.2022.1038177 |
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author | Ren, Wenming Xu, Yujie Yang, Congcong Cheng, Li Yao, Peng Fu, Shimin Han, Jie Zhuo, Dong |
author_facet | Ren, Wenming Xu, Yujie Yang, Congcong Cheng, Li Yao, Peng Fu, Shimin Han, Jie Zhuo, Dong |
author_sort | Ren, Wenming |
collection | PubMed |
description | OBJECTIVE: This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers. METHODS: First, we retrospectively collected the clinicopathologic data from 475 patients who underwent prostate biopsy at our hospital between January 2019 to August 2021. Univariate and multivariate logistic regression analyses were used to select risk factors. Then, we established the nomogram prediction model based on the risk factors. The model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots and the Hosmer–Lemeshow test. Decision curve analysis (DCA) was used to evaluate the net benefit of the model at different threshold probabilities. The model was validated in an independent cohort of 197 patients between September 2021 and June 2022. RESULTS: The univariate and multivariate logistic regression analyses based on the development cohort indicated that the model should include the following factors: age (OR = 1.056, p = 0.001), NEUT (OR = 0.787, p = 0.008), HPR (OR = 0.139, p < 0.001), free/total (f/T) PSA (OR = 0.013, p = 0.015), and PI-RADS (OR = 3.356, p < 0.001). The calibration curve revealed great agreement. The internal nomogram validation showed that the C-index was 0.851 (95% CI 0.809-0.894). Additionally, the AUC was 0.851 (95% CI 0.809-0.894), and the Hosmer–Lemeshow test result presented p = 0.143 > 0.05. Finally, according to decision curve analysis, the model was clinically beneficial. CONCLUSION: Herein, we provided a nomogram combining patients’ clinical data with biomarkers to help diagnose prostate cancers. |
format | Online Article Text |
id | pubmed-9751862 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97518622022-12-16 Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy Ren, Wenming Xu, Yujie Yang, Congcong Cheng, Li Yao, Peng Fu, Shimin Han, Jie Zhuo, Dong Front Oncol Oncology OBJECTIVE: This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers. METHODS: First, we retrospectively collected the clinicopathologic data from 475 patients who underwent prostate biopsy at our hospital between January 2019 to August 2021. Univariate and multivariate logistic regression analyses were used to select risk factors. Then, we established the nomogram prediction model based on the risk factors. The model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots and the Hosmer–Lemeshow test. Decision curve analysis (DCA) was used to evaluate the net benefit of the model at different threshold probabilities. The model was validated in an independent cohort of 197 patients between September 2021 and June 2022. RESULTS: The univariate and multivariate logistic regression analyses based on the development cohort indicated that the model should include the following factors: age (OR = 1.056, p = 0.001), NEUT (OR = 0.787, p = 0.008), HPR (OR = 0.139, p < 0.001), free/total (f/T) PSA (OR = 0.013, p = 0.015), and PI-RADS (OR = 3.356, p < 0.001). The calibration curve revealed great agreement. The internal nomogram validation showed that the C-index was 0.851 (95% CI 0.809-0.894). Additionally, the AUC was 0.851 (95% CI 0.809-0.894), and the Hosmer–Lemeshow test result presented p = 0.143 > 0.05. Finally, according to decision curve analysis, the model was clinically beneficial. CONCLUSION: Herein, we provided a nomogram combining patients’ clinical data with biomarkers to help diagnose prostate cancers. Frontiers Media S.A. 2022-12-01 /pmc/articles/PMC9751862/ /pubmed/36531011 http://dx.doi.org/10.3389/fonc.2022.1038177 Text en Copyright © 2022 Ren, Xu, Yang, Cheng, Yao, Fu, Han and Zhuo https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Ren, Wenming Xu, Yujie Yang, Congcong Cheng, Li Yao, Peng Fu, Shimin Han, Jie Zhuo, Dong Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title | Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title_full | Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title_fullStr | Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title_full_unstemmed | Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title_short | Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
title_sort | development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9751862/ https://www.ncbi.nlm.nih.gov/pubmed/36531011 http://dx.doi.org/10.3389/fonc.2022.1038177 |
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