A preoperative prediction model for pelvic lymph node metastasis in prostate cancer:Integrating clinical characteristics and multiparametric MRI
10.19723/j.issn.1671-167X.2025.04.009
- VernacularTitle:基于临床特征和多参数MRI的前列腺癌盆腔淋巴结转移的术前预测模型
- Author:
Zeyuan WANG
1
;
Shuanbao YU
1
;
Haoke ZHENG
1
;
Jin TAO
1
;
Yafeng FAN
1
;
Xuepei ZHANG
1
Author Information
1. 郑州大学第一附属医院泌尿外科,郑州 450000
- Publication Type:Journal Article
- Keywords:
Prostatic neoplasms;
Lymphatic metastasis;
Multiparametric magnetic resonance imaging;
Biopsy
- From:
Journal of Peking University(Health Sciences)
2025;57(4):684-691
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To analyze the clinical features associated with pelvic lymph node metastasis(PLNM)in prostate cancer and to construct a preoperative prediction model for PLNM,thereby reducing unnecessary extended pelvic lymph node dissection(ePLND).Methods:Based on predefined inclusion and exclusion criteria,344 patients who underwent radical prostatectomy and ePLND at the First Affilia-ted Hospital of Zhengzhou University between 2014 and 2024 were retrospectively enrolled,among whom,77 patients(22.4％)were pathologically confirmed to have lymph node-positive disease.The clinical characteristics,MRI reports,and pathological results were collected.The data were then randomly divi-ded into a training cohort(241 cases,70％)and a validation cohort(103 cases,30％).Univariate and multivariate Logistic regression analysis were employed to construct a preoperative prediction model for PLNM.Results:Univariate Logistic regression analysis revealed that total prostate specific antigen(tPSA)(P=0.021),free prostate specific antigen(fPSA)(P=0.002),fPSA to tPSA ratio(fPSA/tPSA)(P=0.011),percentage of positive biopsy cores(P＜0.001),prostate imaging reporting and data system(PI-RADS)score(P=0.004),biopsy Gleason score ≥8(P=0.005),clinical T stage(P＜0.001),and MRI-indicated lymph node involvement(MRI-LNI)(P＜0.001)were significant predictors of PLNM.Multivariate Logistic regression analysis demonstrated that the percentage of positive biopsy cores(OR=91.24,95％CI:13.34-968.68),PI-RADS score(OR=7.64,95％ CI:1.78-138.06),and MRI-LNI(OR=4.67,95％ CI:1.74-13.24)were independent risk factors for PLNM.And a novel nomogram for predicting PLNM was developed by integrating all these three variables.Com-pared with the individual predictors:percentage of positive biopsy cores[area under curve(AUC)=0.806],PI-RADS score(AUC=0.679),and MRI-LNI(AUC=0.768),the multivariate model incor-porating all three variables demonstrated significantly superior predictive performance(AUC=0.883).Consistently,calibration curves and decision curve analyses confirmed that the multivariable model had high predictive accuracy and provided significant net clinical benefit relative to single-variable models.And using a cutoff of 6％,the multiparameter model missed only approximately 5.2％of PLNM cases(4/77),while reducing approximately 53％of ePLND procedures(139/267),demonstrating favorable predictive efficacy.Conclusion:Percentage of positive biopsy cores,PI-RADS score and MRI-LNI are independent risk factors for PLNM.The constructed multivariate model significantly improves predictive efficacy,offering a valuable tool to guide clinical decisions on ePLND.
