Predictive value of bpMRI for pelvic lymph node metastasis in prostate cancer patients with PSA ≤20 μg/L
DONG Lai
SHI Rong-jie
SHANG Jin-wei
SHEN Zhi-yi
ZHANG Kai-yu
ZHANG Cheng-long
YANG Bin
HUANG Tian-bao
WANG Ya-min
ZHAO Rui-zhe
XIA Wei
WANG Shang-qian
CHENG Gong
HUA Li-xin
Abstract：Objective:The aim of this study is to explore the predictive value of biparametric magnetic resonance imaging(bpMRI)for pelvic lymph node metastasis in prostate cancer patients with PSA≤20 μg/L and establish a nomogram.Methods:The imaging data and clinical data of 363 patients undergoing radical prostatectomy and pelvic lymph node dissection in the First Affiliated Hospital of Nanjing Medical University from July 2018 to December 2023 were retrospectively analyzed.Univariate analysis and multi-variate logistic regression were used to screen independent risk factors for pelvic lymph node metastasis in prostate cancer,and a nomo-gram of the clinical prediction model was established.Calibration curves were drawn to evaluate the accuracy of the model.Results:Multivariate logistic regression analysis showed extrocapusular extension(OR=8.08,95％CI=2.62-24.97,P＜0.01),enlarge-ment of pelvic lymph nodes(OR=4.45,95％CI=1.16-17.11,P=0.030),and biopsy ISUP grade(OR=1.97,95％CI=1.12-3.46,P=0.018)were independent risk factors for pelvic lymph node metastasis.The C-index of the prediction model was 0.834,which indicated that the model had a good prediction ability.The actual value of the model calibration curve and the prediction proba-bility of the model fitted well,indicating that the model had a good accuracy.Further analysis of DCA curve showed that the model had good clinical application value when the risk threshold ranged from 0.05 to 0.70.Conclusion:For prostate cancer patients with PSA≤20 μg/L,bpMRI has a good predictive value for the pelvic lymph node metastasis of prostate cancer with extrocapusular extension,enlargement of pelvic lymph nodes and ISUP grade ≥ 4.
Keywords：prostate cancerlymph node metastasisMRIprostate specific antigennomogram
Publication Date：2025-05-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 426-431 )