The influence of extraprostatic extension grade on the detection of pelvic lymph node metastasis in prostate cancer - Wang, Jun-guang; Ying, Ling-ling; He, Pei-pei (Frontiers in Oncology, 2025)
IntroductionThis study was conducted to evaluate any association between extraprostatic extension (EPE) grade with the risk of pelvic lymph node metastasis (PLNM) of prostate cancer (PCa).MethodsMagnetic resonance imaging data, as well as clinical and pathological data were collected for 317 patients undergoing radical prostatectomy (RP) along with pelvic lymph node dissection (PLND) at Ningbo Yinzhou No. 2 Hospital from January 2019 to January 2024. The collected magnetic resonance images were scored employing the EPE grade. The factors associated with PLNM were analyzed through Chi-square test and independent sample T-test. Independent risk factors associated with PLNM were identified through Multivariate analyses. The area under the curve (AUC) was calculated and the diagnostic performance of the model was assessed by analyzing the receiver operating characteristic (ROC) curve. The clinical net benefit of EPE grade, biopsy positive rate, and the combined model were examined using clinical decision curves.ResultsAmong 317 patients, 33had PLNM. Multifactor analysis demonstrated EPE grade and biopsy positive rate as independent risk factors for PLNM of PCa. The AUC of EPE grade and biopsy positive rate was, respectively, 0.879 and 0.877, and the diagnostic efficiency of PLNM between the two was not statistically significant (P > 0.05). However, when the two approaches were combined, the diagnostic efficiency improved significantly, and the AUC increased to 0.921 (P < 0.05). The analysis of the clinical decision curve revealed a significantly higher clinical net benefit of the combined model than that of the EPE grade and biopsy positive rate.ConclusionsThe EPE grade and biopsy positive rate exhibit an independent correlation with PLNM of PCa. In addition, the combination of the two can significantly enhance the accuracy of predicting PLNM of PCa.