摘要
Abstract
Objective:To develop a nomogram model using apparent diffusion coefficient(ADC)histogram features to predict clinically significant prostate cancer(CSPCa)in the transition zone.Materials and Methods:A retrospective analysis was conducted on 283 patients with suspicious prostate cancer admitted to the urology department of our hospital from January 2019 to June 2024.The patients were randomly divided into a development set(70%,198 cases)and an internal validation set(30%,85 cases).The least absolute shrinkage and selection operator(LASSO)algorithm was applied to screen for key features:ADC_min(apparent diffusion coefficient minimum),ADC_CoeffOfVar(coefficient of variation of apparent diffusion coefficient),ADC_kurtosis(apparent diffusion coefficient kurtosis)and ADC_entropy(apparent diffusion coefficient entropy).Furthermore,univariate and multivariate logistic regression analyses were performed to select variables and construct a predictive model.Diagnostic performance was evaluated using area under the curve(AUC)of the receiver operating characteristic(ROC),sensitivity,specificity,positive predictive value,negative predictive value,and accuracy.Decision curve analysis(DCA)was also employed to assess clinical net benefit.Results:ADC_CoeffOfVar[odds ratio(OR)=1.01,P=0.034]and ADC_entropy(OR=1.00,P<0.001)were independent predictors of CSPCa.The nomogram model constructed based on these factors demonstrated good predictive performance in both the development set(AUC=0.844)and the internal validation set(AUC=0.765).Calibration curve analysis showed a high degree of agreement between model predictions and actual observations,and decision curve analysis further confirmed the net benefit of the model in clinical decision-making.Conclusions:The nomogram model constructed based on ADC histogram features not only provides a non-invasive tool for preoperative risk assessment but also holds practical clinical application potential.关键词
前列腺肿瘤/临床显著性前列腺癌/磁共振成像/列线图Key words
prostatic neoplasms/clinically significant prostate cancer/magnetic resonance imaging/nomogram分类
临床医学
