Construction and clinical validation of a machine learning-based nomogram model for predicting lymphatic leakage following radical prostatectomy
YANG Xiudong
LIU Xing
LIU Xin
JIANG Yan
WANG Wei
HE Zongbin
HUANG Sha
WEN Meihong
LIU Yazhen
Abstract：Objective To identify risk factors associated with lymphatic leakage after laparoscopic radical prostatectomy(LRP)and to develop a machine learning-based nomogram for predicting such outcomes to support clinical prevention strategies.Methods We retrospectively analyzed perioperative data from 248 patients who underwent radical prostatectomy for prostate cancer between January 2020 and January 2024.Independent risk factors were identified through univariate and multivariate logistic regression analyses.A predictive model was developed,and its diagnostic performance was assessed by the area under the receiver operating characteristic curve(AUC).Five-fold cross-validation was performed to evaluate the model's generalizability.A nomogram was subsequently constructed to facilitate individualized risk quantification.Results Among the 248 patients,89(35.9%)developed lymphatic leakage,while 159(64.1%)did not.Independent risk factors for lymphatic leakage included intraopera-tive lymph node dissection(OR=5.415,95%CI:2.167～13.532,P<0.001),intraoperative plasma transfusion(OR=2.952,95%CI:1.524～5.718,P=0.001),and postoperative fasting duration of≥2 days(OR=1.412,95%CI:1.089～1.829,P=0.009).The predictive model showed good discrimination and calibration(AUC=0.711,95%CI:0.647～0.776,P<0.001;sensitivity:0.764;specificity:0.597).Model robustness was confirmed through five-fold cross-validation(training set AUC=0.822;test set AUC=0.829).The nomogram provided a clinically useful tool for quantifying individual risk of lymphatic leakage.Conclusions Intraoperative lymph node dissection,plasma transfusion,and postoperative fasting lasting≥2 days are independent risk factors for lymphatic leakage following radical prostatectomy.The validated predictive model demonstrates favorable clinical utility.
Keywords：machine learningradical prostatectomylymphatic leakagepredictive modelrisk factors
Publication Date：2025-11-10
Online Publishing Date：2025-11-20(First online date of this platform, not the publication date of the document)
Pages：7( 3378-3384 )