首页|基于多因素logistic回归模型预测前列腺特异性抗原4～20ng/mL时移行带前列腺癌

基于多因素logistic回归模型预测前列腺特异性抗原4～20ng/mL时移行带前列腺癌

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  • NETL
  • NSTL
  • 万方数据
目的 基于前列腺影像报告和数据系统(PI-RADS)v2。1与前列腺特异性抗原(PSA)及其衍生指标建立多因素logistic回归模型，探讨PSA 4～20 ng/m L时对移行带前列腺癌的预测价值。方法 回顾性选取PSA 在4～20 ng/m L的前列腺疾病患者104例，均进行穿刺活检术或根治性前列腺切除术。依据病理结果将患者分为前列腺癌组(30例)和非前列腺癌(前列腺增生或前列腺炎)组(74例)。比较2组患者总前列腺特异性抗原(t PSA)、游离前列腺特异性抗原(f PSA)、f PSA与t PSA比值(f PSA/t PSA)、前列腺特异性抗原密度(PSAD)、PI-RADS v2。1评分等指标的差别。建立多因素logistic回归模型，分析患癌的危险因素，使用受试者工作特征(ROC)曲线评价诊断效能。结果 2组患者的t PSA、f PSA/t PSA、PSAD及PI-RADS v2。1评分间差异均有统计学意义(P<0。05)。logistic回归分析显示，PSAD、PI-RADS v2。1评分是预测移行带前列腺癌的独立危险因素。模型的曲线下面积(AUC)为0。938，明显高于任一指标，其敏感度为90。00%，特异度为86。49%，准确性为86。54%。结论 基于PI-RADS v2。1与PSAD的logistic回归模型可以提升移行带前列腺癌的诊断效能，为可疑前列腺癌患者的穿刺活检提供更有力的证据，避免过度诊断以及不必要的活检。
Predicting transitional zone prostate cancer with prostate specific antigen of 4-20 ng/m L based on multivariate logistic regression model
Objective To establish a multivariate logistic regression model based on prostate imaging reporting and data system(PI-RADS)v2.1 and prostate specific antigen(PSA)and its derived indicators,and to investigate the predictive value of PSA 4-20 ng/m L for transitional zone prostate cancer.Methods A total of 104 patients with prostate disease and PSA of 4-20 ng/m L were retospec-tively selected.All patients underwent puncture biopsy or radical prostatectomy.In accordance with the pathological findings,patients were divided into prostate cancer group(30 cases)and non-prostate cancer(prostatic hyperplasia or prostatitis)group(74 cases).The differences of total prostate specific antigen(t PSA),free prostate specific antigen(f PSA),f PSA/t PSA,prostate specific antigen density(PSAD)and PI-RADS v2.1 scores were compared between the two groups.A multivariate logistic regression model was con-structed to analyze the risk factors of cancer.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic efficiency.Results There were statistically differences in t PSA,f PSA/t PSA,PSAD,and PI-RADS v2.1 scores between the two groups(P<0.05).Logistic regression analysis indicated that PSAD and PI-RADS v2.1 scores were independent risk factors for pre-dicting transitional zone prostate cancer.The area under the curve(AUC)of the model was 0.938,which was significantly higher than any individual index,with sensitivity of 90.00%,specificity of 86.49%,and accuracy of 86.54%.Conclusion The logistic regression model based on PI-RADS v2.1 and PSAD can enhance the diagnostic efficiency for transitional zone prostate cancer.It provides stron-ger evidence for the needle biopsy of patients with suspicious prostate cancer and helps to avoid overdiagnosis and unnecessary biopsy.

prostate cancerprostate imaging reporting and data systemprostate specific antigenlogistic regression

聂洋平、李云

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郑州市第七人民医院MR科,河南 郑州 450000

前列腺癌 前列腺影像报告和数据系统 前列腺特异性抗原 logistic回归

2025

10.3969/j.issn.1002-1671.2025.01.018

实用放射学杂志
西安市医学科学研究所

实用放射学杂志

北大核心
影响因子：1.141
ISSN：1002-1671
年,卷(期)：2025.41(1)