ORIGINAL RESEARCH article
Front. Oncol.
Sec. Genitourinary Oncology
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1626529
This article is part of the Research TopicClinical prediction models in cancer through bioinformaticsView all 19 articles
The risk prediction model for acute urine retention after perineal prostate biopsy based on The LASSO approach and Boruta feature selection
Provisionally accepted
- 1Nantong First People’s Hospital, Nantong, China
- 2Nantong Second People’s Hospital, Nantong, China
- 3Rudong Hospital, Xinglin College of Nantong University, Nantong, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective One known side effect of transperineal (TP) prostate biopsies is acute urine retention (AUR). We aimed to create and evaluate a predictive model for the post-paracentesis risk of acquiring AUR. Methods This study included 599 patients undergoing prostate biopsies (April 2020-July 2023) at the Second Affiliated Hospital of Nantong University, selected based on abnormal digital rectal examination and/or PSA (prostate-specificantigen) > 4 ng/mL. Acute urinary retention (AUR) was defined as the inability to void within 72 hours post-biopsy, requiring catheterization. Patients were randomly divided into training (419 cases) and test (180 cases) sets. Univariate logistic analysis and feature selection Boruta and LASSO (Least absolute shrinkage and selection operator) identified predictors, followed by multivariate logistic regression to develop a predictive nomogram for AUR. Internal validation used the test set, with model performance assessed via the c-index, ROC (Receiver Operating Characteristic) curve, calibration plot, and decision curve analysis. The nomogram demonstrated strong discrimination, calibration, and clinical utility for AUR risk prediction. Results In 86 patients (14.3%), AUR happened. An examination of multivariate logistic regression revealed six distinct risk variables for AUR. Based on these independent risk factors, a nomogram was constructed. The training and validation groups' c-indices showed the model's high accuracy and stability. The calibration curve demonstrates that the corrective effect of the training and verification groups is perfect, and the area under the receiver operating characteristic curve indicates great identification capacity. DCA (Decision Curve Analysis) curves, or decision curve analysis, demonstrated the model's significant net therapeutic effect. Discussion The nomogram model created in this work can offer a personalized and intuitive analysis of the risk of AUR and has intense discrimination and accuracy. It can help create efficient preventative measures and identify high-risk populations.
Keywords: Acute urinary retention, prostate biopsy, machine learning, predictive model, Boruta feature selection
Received: 13 May 2025; Accepted: 29 Aug 2025.
Copyright: © 2025 Cheng, Chen, Chen, You and Zheng. 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) or licensor 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.
* Correspondence:
Junjie You, Rudong Hospital, Xinglin College of Nantong University, Nantong, China
Bing Zheng, Nantong First People’s Hospital, Nantong, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.