【摘要】 目的 基于术前血脂比率、cM0(i+)分期及其他临床特征，构建并评估肾透明细胞癌(ccRCC)的预后模型，旨在为临床预后评估提供精准工具。方法 回顾性分析2014年5月—2023年5月于西安交通大学第二附属医院泌尿外科接受肾部分切除术的215例ccRCC患者的临床资料，通过患者术前血脂数据和术后循环肿瘤细胞(CTCs)检测数据进行血脂比率的计算和cM0(i+)分期的评估。根据受试者工作特征(ROC)曲线和X-tile方法选择最佳血脂比率及其最佳截断值。根据低密度脂蛋白胆固醇与非低密度脂蛋白胆固醇的比值(LNLR)水平与cM0(i+)分期将患者分为三组：A组[LNLR>1.68且诊断为cM0(i+)]、B组[仅LNLR>1.68或仅诊断为cM0(i+)]、C组(LNLR≤1.68且诊断为cM0)。采用Kaplan-Meier生存分析绘制A、B、C组患者的生存曲线，采用log-rank检验比较组间无复发生存期(RFS)的差异，采用多因素Cox回归分析确定影响RFS的独立危险因素，并基于该结果构建列线图预测模型。通过ROC曲线、校准曲线和决策曲线验证模型预测效能。结果 绘制血脂指标的ROC曲线，筛选得到LNLR对RFS的预测价值最高，其最佳截断值为1.68;A组的ccRCC患者术后RFS显著缩短；多因素Cox回归分析显示，术前LNLR、cM0(i+)分期、病理分级和分期是影响患者RFS的独立危险因素，基于上述危险因素构建列线图模型。患者1、3、5年RFS的ROC曲线下面积(AUC)分别为0.896(95%CI:0.8121～0.9627)、0.890(95%CI:0.7879～0.9641)、0.870(95%CI:0.7697～0.9526),表明模型具有良好的区分度及预测效能；校准图显示预测结果与实际结果有良好的一致性；临床决策曲线分析显示临床净收益高。结论 术前LNLR水平是ccRCC患者RFS的独立危险因素，基于LNLR、cM0(i+)分期、患者病理分级和分期建立的预后预测模型对患者的RFS具有良好的预测效能和潜在的临床应用价值。更多还原

【Abstract】 Objective To construct and evaluate a prognostic model for clear cell renal cell carcinoma(ccRCC) based on preoperative lipid ratios, cM0(i+) staging, and other clinical characteristics, so as to provide a precise tool for clinical prognosis assessment. Methods A retrospective analysis was conducted on the clinical data of 215 ccRCC patients treated in our hospital during May 2014 and May 2023. Lipid ratios were calculated using preoperative lipid data, and patients were divided into cM0(i+) stage or cM0 stage according to postoperative circulating tumor cells(CTCs) test results. The optimal lipid ratio and cutoff value were selected using receiver operating characteristic(ROC) curves and the X-tile method. Patients were subdivided into three groups based on the level of the low-density lipoprotein cholesterol to non-low-density lipoprotein cholesterol ratio(LNLR) and cM0(i+) staging: Group A [LNLR >1.68 and diagnosed with cM0(i+)],Group B [LNLR >1.68 or diagnosed with cM0(i+)],and Group C(LNLR ≤1.68 and diagnosed with cM0). Kaplan-Meier survival analysis was used to plot survival curves for patients in different groups. The log-rank test was employed to compare differences in recurrence-free survival(RFS) among the subgroups. Multivariate Cox regression analysis was conducted to identify the independent risk factors influencing RFS,and a nomogram prediction model was constructed based on these results. The predictive performance of the model was validated using ROC curves, calibration curves, and decision curves. Results ROC curves were plotted for lipid markers, and LNLR was identified as the most predictive for RFS. Its optimal cutoff point was 1.68. Patients in Group A experienced a significantly shorter postoperative RFS. Multivariate Cox regression analysis identified preoperative LNLR,cM0(i+) staging, pathological grade, and stage as independent risk factors for RFS. A nomogram model was constructed based on these risk factors. The area under the ROC curve(AUC) for 1-,3-,and 5-year RFS was 0.896(95%CI:0.8121-0.9627),0.890(95%CI:0.7879-0.9641),and 0.870(95%CI:0.7697-0.9526),indicating good discriminatory ability and predictive performance. Calibration plots demonstrated good agreement between predicted and actual outcomes. Clinical decision curve analysis showed high clinical net benefit. Conclusion Preoperative LNLR level is an independent risk factor for RFS in ccRCC patients. The prognostic prediction model based on LNLR,cM0(i+) staging, patient pathological grade, and staging demonstrates good predictive performance for RFS and holds potential clinical application value.更多还原