一种新型列线图结合全身炎症综合指数和 PI-RADS 评分预测临床显著前列腺癌的风险。

A Novel Nomogram Combined the Aggregate Index of Systemic Inflammation and PIRADS Score to Predict the Risk of Clinically Significant Prostate Cancer.

机构信息

Department of Urology, The First Affiliated Hospital of Soochow University, Jiangsu, Suzhou 215006, China.

Key Laboratory of Tumor Immunology and Microenvironmental Regulation of Guangxi, Guilin Medical University, Guilin, 541004 Guangxi, China.

出版信息

Biomed Res Int. 2023 Jan 12;2023:9936087. doi: 10.1155/2023/9936087. eCollection 2023.

DOI:10.1155/2023/9936087

PMID:36685670

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9851778/

Abstract

BACKGROUND

This study is aimed at constructing a nomogram to predict the risk of clinically significant prostate cancer (csPCa) based on the aggregate index of systemic inflammation (AISI) and prostate imaging-reporting and data system version (PIRADS) score.

METHODS

Clinical data on patients who had undergone initial prostate biopsy from January 2019 to December 2021 were collected. Patients were randomized in a 7 : 3 ratio to the training cohort and the validation cohort. Potential risk factors for csPCa were identified by univariable and multivariate logistic regression. Nomogram was conducted with these independent risk factors, and calibration curves, the receiver operating characteristic (ROC), and decision curve analysis (DCA) were employed to assess the nomogram's ability for prediction.

RESULTS

A total of 1219 patients were enrolled in this study. Multivariate logistic regression identified that age, AISI, total prostatic specific-antigen (tPSA), free to total PSA (f/tPSA), prostate volume (PV), and PIRADS score were potential risk predictors of csPCa, and the nomogram was developed based on these factors. The area under the curve (AUC) of the training cohort and validation cohort was 0.884 (95% CI: 0.862-0.906) and 0.899 (95% CI: 0.867-0.931). The calibration curves showed that the apparent curves were closer to the ideal curves. The DCA results revealed that the nomogram model seemed to have clinical application value per DCA.

CONCLUSION

The nomogram model can efficiently predict the risk of csPCa and may assist clinicians in determining if a prostate biopsy is necessary.

摘要

背景

本研究旨在构建一个基于全身炎症综合指数（AISI）和前列腺影像报告和数据系统评分（PIRADS）的列线图，以预测临床显著前列腺癌（csPCa）的风险。

方法

收集了 2019 年 1 月至 2021 年 12 月期间接受初始前列腺活检的患者的临床数据。患者按照 7:3 的比例随机分为训练队列和验证队列。通过单变量和多变量逻辑回归确定 csPCa 的潜在风险因素。使用这些独立的风险因素构建列线图，并通过校准曲线、受试者工作特征（ROC）曲线和决策曲线分析（DCA）评估列线图的预测能力。

结果

本研究共纳入 1219 例患者。多变量逻辑回归确定年龄、AISI、总前列腺特异性抗原（tPSA）、游离前列腺特异性抗原与总前列腺特异性抗原比值（f/tPSA）、前列腺体积（PV）和 PIRADS 评分是 csPCa 的潜在风险预测因素，并基于这些因素构建了列线图。训练队列和验证队列的曲线下面积（AUC）分别为 0.884（95%CI：0.862-0.906）和 0.899（95%CI：0.867-0.931）。校准曲线显示，实际曲线更接近理想曲线。DCA 结果表明，该列线图模型在 DCA 方面似乎具有临床应用价值。