A prediction model of targeted biopsy for PI-RADS 4-5 based on mp-MRI and PSAD
10.3969/j.issn.1009-8291.2025.07.003
- VernacularTitle:基于mp-MRI和PSAD建立PI-RADS评分4～5分患者的前列腺靶向穿刺预测模型
- Author:
Yibo LI
1
;
Pan ZANG
1
;
Lei DING
1
;
Zhentao TANG
1
;
Chao LIANG
1
;
Jie LI
1
Author Information
1. 南京医科大学第一附属医院泌尿外科,江苏南京 210003
- Publication Type:Journal Article
- Keywords:
prostate cancer;
targeted biopsy;
systematic biopsy;
Prostate Imaging Reporting and Data System;
multiparameter magnetic resonance imaging;
clinically significant prostate cancer
- From:
Journal of Modern Urology
2025;30(7):565-570,575
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a prediction model for targeted biopsy(TB)of the prostate based on multiparameter magnetic resonance imaging(mp-MRI)and prostate-specific antigen density(PSAD)to predict the outcomes TB in patients with a score of 4-5 on the Prostate Imaging Reporting and Data System(PI-RADS).Methods Clinical data of 669 patients with PI-RADS 4-5 receiving transperineal TB in our hospital during Jan.2022 and Dec.2023 were retrospectively analyzed.The data were divided into the training set and validation set with a ratio of 2∶1.Independent predictors of TB results were identified with univariate and multivariate logistic regression to construct a formula for the prediction model.A prediction model was subsequently constructed and validated using the validation set to assess its efficacy and predictive performance with the area under the receiver operating characteristic curve(AUC).The relative importance of each independent predictor in the formula was analyzed.Results Univariate and multivariate logistic regression analyses showed that age,total number of lesions,histological location,PI-RADS score and PSAD were significantly associated with the TB outcomes(P＜0.05)and could be used as independent predictors,with PI-RADS score and PSAD making the highest contribution to outcome prediction,accounting for 27.59％and 37.58％,respectively.The training set had an AUC of 0.840(95％CI:0.800-0.881),which was more predictive than other single predictors,and the high-risk group based on the optimal threshold of 0.833 increased the positive biopsy rate from 79.3％to 94.4％.The validation set had an AUC of 0.865(95％CI:0.810-0.920),and the high-risk group based on the optimal threshold of 0.594 increased the positive biopsy rate from 80.0％to 96.2％.Conclusion The prediction model has good predictive ability for lesions with PI-RADS 4-5,which can significantly improve the positive detection rate and reduce a large number of unnecessary systematic puncture.
