前列腺癌周围神经侵犯预测模型的构建及验证-现代医学

前列腺癌周围神经侵犯预测模型的构建及验证

单位：1. 苏州大学附属第四医院泌尿外科, 江苏苏州 215000;
2. 苏州大学附属第一医院泌尿外科, 江苏苏州 215000

分类号：R737.25

出版年·卷·期（页码）：2025·44·第六期（905-913）

摘要：

目的： 筛选前列腺癌周围神经侵犯(PNI)的独立危险因素，构建预测模型，为前列腺癌PNI提供个体化预测方案。方法： 收集2022年9月至2024年3月于苏州大学附属第一医院泌尿外科治疗的前列腺癌患者的临床资料。共纳入患者318例，依据根治性前列腺切除术后病理分为PNI阳性组(n=202)和PNI阴性组(n=116)。将总体病例随机分为训练集(n=223)和验证集(n=95)，划分比例为7∶3。通过LASSO回归筛选出训练集中最具价值的预测变量，并对预测变量进行多因素Logistic回归分析，绘制列线图，并进一步在验证集中验证。所有统计分析均使用R软件(v 4.4.0)进行。结果： 通过LASSO回归筛选出4个具有预测价值的变量，包括前列腺特异性抗原密度(PSAD)、穿刺阳性针数百分比、前列腺报告和数据评分系统(PI-RADS)评分、血清白蛋白。多因素Logistic回归分析显示，PSAD (OR=6.788，95%CI 1.909～28.697)、穿刺阳性针数百分比(OR=1.033，95%CI 1.015～1.054)、白蛋白<40 g·L^-1(OR=2.655，95%CI 1.340～5.425)、PI-RADS评分4分(OR=3.146，95%CI 1.289～8.165)、PI-RADS评分5分(OR=3.633，95%CI 1.331～10.399)是前列腺癌PNI的独立危险因素，并使用以上变量进行预测模型构建。结果显示，训练集受试者工作特征曲线(ROC)曲线下面积(AUC)为0.814(95%CI 0.755～0.874)，验证集ROC的AUC为0.772(95%CI 0.674～0.871)。拟合优度检验显示训练集P=0.21，验证集P=0.60，具有较好的拟合度。临床决策曲线(DCA)及临床影响曲线(CIC)结果表明此模型在临床应用中有一定的指导意义。结论： 本研究以PSAD、穿刺阳性针数百分比、PI-RADS评分、血清白蛋白共4项指标建立了前列腺癌PNI预测模型，结果显示该模型具有较好的临床应用价值，可用于预测前列腺癌PNI的发生风险。

Objective: To identify independent risk factors for perineural invasion(PNI) in prostate cancer and develop a predictive model for individualized risk assessment. Methods: We analyzed clinical data from 318 patients with prostate cancer treated at the First Affiliated Hospital of Soochow University between September 2022 and March 2024. Patients were categorized into PNI-positive(n=202) and PNI-negative(n=116) groups based on postoperative pathological results. The cases were randomly divided into training(n=223) and validation(n=95) sets with a 7∶3 ratio. LASSO regression identified significant predictors, followed by multivariate Logistic regression to construct a nomogram, validated in both sets using R software(v4.4.0). Results: Four predictors were identified: prostate-specific antigen density(PSAD)(OR=6.788, 95%CI 1.909-28.697), percentage of positive biopsy cores(OR=1.033, 95%CI 1.015-1.054), serum albumin ≤40 g·L^-1(OR=2.655, 95%CI 1.340-5.425), and PI-RADS score(4: OR=3.146, 95%CI 1.289-8.165; 5: OR=3.633, 95%CI 1.331-10.399). The model demonstrated discriminative ability with AUCs of 0.814(95%CI 0.755-0.874) and 0.772(95%CI 0.674-0.871) in training and validation sets, respectively. Goodness-of-fit tests showed satisfactory calibration(training set P=0.21; validation set P=0.60). Decision curve analysis and clinical impact curves confirmed clinical utility across probability thresholds. Conclusion: Our nomogram incorporating PSAD, percentage of positive biopsy cores, PI-RADS score, and serum albumin effectively predicts PNI risk in prostate cancer, demonstrating good clinical applicability for preoperative risk stratification.

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