双参数MRI联合临床指标构建预测模型对PSA 4～10 ng/mL临床显著性前列腺癌的诊断价值研究OA
Diagnostic value of nomogram model constructed by biparametric MRI combined with clinical indicators for clinically significant prostate cancer in patients with PSA 4-10 ng/mL
目的 探讨基于前列腺影像报告和数据系统版本2.1(PI-RADS v2.1)的双参数磁共振成像(bpMRI)联合临床指标构建预测模型对前列腺特异性抗原(PSA)4～10 ng/mL临床显著性前列腺癌(csPCa)的诊断价值.方法 回顾性分析2017年6月至2024年8月在浙江中医药大学附属杭州市中医院266例接受bpMRI检查和前列腺穿刺活检患者的资料,按照7∶3的比例随机分为训练组186例和验证组80例.经单因素和多因素logistic回归分析筛选独立危险因素,构建预测模型并绘制列线图.采用ROC曲线、校准曲线及决策曲线分析评估模型的诊断效能.结果 单因素分析显示,csPCa组与非csPCa组患者年龄、游离与总前列腺特异性抗原比值(f/tPSA)、前列腺体积(PV)、前列腺特异性抗原密度(PSAD)、年龄体积比(AVR)、前列腺特异性抗原-年龄-体积(PSA-AV)及bpMRI比较差异均有统计学意义(均P＜0.001),多因素logistic回归分析结果显示年龄、PSA-AV、bpMRI是PSA 4～10 ng/mL csPCa的独立危险因素,基于上述3个因素构建的预测模型在训练组和验证组中都显示良好的预测效能,其AUC分别0.92和0.81,明显优于bpMRI(AUC分别为0.87和0.75)、PSA-AV(AUC分别为0.71和0.68)和年龄(AUC分别为0.65和0.64)的预测效能.校准曲线显示预测概率与实际概率高度一致.决策曲线表明模型在0～0.75阈值范围内具有较高的临床净获益.结论 基于bpMRI和临床指标构建的预测模型对PSA 4～10 ng/mL csPCa具有较高的预测价值,有助于临床医师选择最佳的管理策略.
Objective To investigate the diagnostic value of a prediction model based on biparametric magnetic resonance imaging(bpMRI)according to Prostate Imaging Report and Data System version 2.1(PI-RADS v2.1)combined with clinical indicators for clinically significant prostate cancer(csPCa)in patients with prostate specific antigen(PSA)4-10 ng/mL.Methods Clinical data of 266 patients who underwent bpMRI and prostate biopsy at Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University from June 2017 to August 2024 were retrospectively analyzed.The patients were randomly divided into a training cohort(n=186)and a validation cohort(n=80)at a ratio of 7∶3.Independent risk factors were screened by univariate and multivariate logistic regression analyses,and a prediction model was established and presented as a nomogram.The diagnostic performance of the model was evaluated using ROC curve,calibration curve and decision curve analysis.Results Univariate analysis showed that there were statistically significant differences between the csPCa group and non-csPCa group in age,free/total PSA ratio(f/tPSA),prostate volume(PV),PSA density(PSAD),age-to-volume ratio(AVR),PSA-age volume(PSA-AV)and bpMRI(all P＜0.001).Multivariate logistic regression analysis revealed that age,PSA-AV and bpMRI were independent risk factors for csPCa in patients with PSA 4-10 ng/mL.The prediction model based on the above three factors showed favorable predictive efficacy in both the training and validation cohorts,with AUC of 0.92 and 0.81,respectively,which was significantly higher than that of bpMRI(AUC:0.87 and 0.75,respectively),PSA-AV(AUC:0.71 and 0.68,respectively)and age(AUC:0.65 and 0.64,respectively).The calibration curve showed high consistency between the predicted probability and the actual probability.Decision curve analysis indicated that the model achieved a high net clinical benefit within the threshold range of 0-0.75.Conclusion The prediction model constructed by bpMRI combined with clinical indicators has high predictive value for csPCa in patients with PSA 4-10 ng/mL,which is helpful for clinicians to select the optimal management strategy.
徐辉景;颜丹;张永胜;李志平;杨丽勤;洪璐威;崔凤
310007 浙江中医药大学附属杭州市中医院放射科杭州师范大学附属萧山医院超声科310007 浙江中医药大学附属杭州市中医院放射科310007 浙江中医药大学附属杭州市中医院放射科310007 浙江中医药大学附属杭州市中医院放射科310007 浙江中医药大学附属杭州市中医院放射科310007 浙江中医药大学附属杭州市中医院放射科
前列腺癌前列腺特异性抗原前列腺影像报告和数据评分系统双参数磁共振成像
Prostate cancerProstate specific antigenProstate Imaging Report and Data SystemBiparametric magnetic resonance imaging
《浙江医学》 2026 (5)
464-470,7
浙江省医药卫生科技计划项目(2024KY1386、2025KY1217、2025KY1160、2025KY1161)浙江省中医药科技计划项目(2024ZL668)杭州市卫生科技计划项目(A20230086)萧山区科技计划引导项目(2022338)
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