收稿日期: 2025-02-24
网络出版日期: 2025-08-11
基金资助
湖南省重点领域研发计划（国际与区域合作）(2021WK2003);南通市卫生健康委员会科研课题专项(MS2024032);南通大学临床医学专项科研基金项目(2024LY006);南通大学临床医学专项科研基金项目(2024LQ019)
Establishment of a nomogram model for predicting pelvic lymph node metastasis in prostate cancer based on systemic immune-infiltration inflammation index
Received date: 2025-02-24
Online published: 2025-08-11
目的 旨在建立并验证一个基于全身炎症标志物的列线图模型，用于预测前列腺癌患者的盆腔淋巴结转移（PLNM）。 方法 回顾性收集前列腺癌患者的临床数据和术前全身炎症标志物水平，包括中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）、系统性免疫炎症指数（SII）以及单核细胞与淋巴细胞比值（MLR）等。通过单因素和多因素logistic回归分析筛选与PLNM显著相关的指标，基于多因素分析结果构建预测前列腺癌PLNM风险的列线图模型。采用受试者工作特征曲线（ROC）和校准曲线对模型的预测效能进行验证与评价。 结果 研究共纳入334例前列腺癌患者，其中107例存在PLNM。单因素分析显示，PLNM组与非盆腔淋巴结转移（NPLNM）组间的fPSA、Gleason评分、NLR、PLR、MLR和SII存在组间差异（P < 0.05），多因素logistic回归分析结果表明fPSA、Gleason评分及SII是PLNM的独立预测因子（P < 0.05）。基于上述变量构建SII联合多变量的列线图模型具有良好的区分能力，ROC曲线的曲线下面积值为0.79（95%CI：0.73~0.84），校准曲线显示模型预测值与实际观察值高度一致。 结论 本研究构建了一种基于SII的列线图模型，用于术前预测前列腺癌患者的PLNM风险。该模型操作简便、预测准确，有望作为现有影像学评估的有效补充，为前列腺癌的个体化诊疗提供科学依据。
刘俊志 , 邱磊 , 徐坤 , 刘建炜 , 胡德华 , 朱华 , 沈城 , 陆明 , 陈建刚 . 系统性免疫炎症指数预测前列腺癌盆腔淋巴结转移列线图模型的建立[J]. 实用医学杂志, 2025 , 41(15) : 2349 -2354 . DOI: 10.3969/j.issn.1006-5725.2025.15.009
Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis （PLNM） in prostate cancer patients prior to surgery. Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil?to?lymphocyte ratio （NLR）， platelet?to?lymphocyte ratio （PLR）， systemic immune?inflammation index （SII）， and monocyte?to?lymphocyte ratio （MLR）—of patients diagnosed with prostate cancer. Univariate and multivariate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM. Based on the results of the multivariate analysis， a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves （ROC） and calibration plots. Results Among the 334 enrolled patients with prostate cancer， 107 were identified with PLNM. Univariate analysis revealed statistically significant differences in free prostate?specific antigen （fPSA）， Gleason score， NLR， PLR， MLR， and SII between the PLNM and non?pelvic lymph node metastasis （NPLNM） groups （P < 0.05）. Multivariate analysis confirmed that fPSA， Gleason score， and SII were independent predictors of PLNM （P < 0.05）. A nomogram incorporating these predictors exhibited strong discriminative ability， with an area under the ROC curve （AUC） of 0.79 （95%CI： 0.73 ~ 0.84）. Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM. Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer. Owing to its user?friendly design and high predictive accuracy， this tool may serve as a valuable complementary method to conventional imaging techniques， thereby supporting personalized treatment decision?making.
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