ⅠB～ⅡA期宫颈癌患者阴道侵犯列线图模型的构建及验证OA
Development and Validation of A Nomogram Model for Predicting Vaginal Invasion in Patients with Stage ⅠB-ⅡA Cervical Cancer
目的 探讨ⅠB～ⅡA期宫颈癌患者阴道侵犯的危险因素,构建列线图模型,并验证该模型对阴道侵犯的预测性能.方法 回顾性选取2021年9月至2024年9月苏州大学附属张家港医院收治的宫颈癌患者380例,按照7∶3比例将患者随机分为建模队列(266例)和内部验证队列(114例),另回顾性选取同期张家港市中医医院收治的150例宫颈癌患者作为外部验证队列.收集患者临床资料,采用LASSO回归分析筛选关键变量纳入多因素分析,基于多因素分析筛选出的独立影响因素构建列线图模型,采用受试者工作特征曲线和决策曲线分析验证模型预测效能.结果 ⅠB～ⅡA期宫颈癌患者阴道侵犯发生率为17.37%(66/380).LASSO回归分析共筛选出关键变量7个,多因素分析结果显示,年龄大(OR=1.173,95%CI:1.097～1.254)、磁共振成像诊断阴道侵犯阳性(OR=3.004,95%CI:1.185～7.616)、全身炎症反应指数高(OR=1.327,95%CI:1.002～1.757)是ⅠB～ⅡA期宫颈癌患者阴道侵犯的独立危险因素,而预后营养指数高(OR=0.912,95%CI:0.859～0.968)则是其独立保护因素(P<0.05).受试者工作特征曲线分析结果显示,建模队列曲线下面积(AUC)为0.866(95%CI:0.803～0.930),内部验证队列AUC为0.828(95%CI:0.761～0.896),外部验证队列AUC为0.834(95%CI:0.774～0.897).决策曲线分析结果显示,0～0.6高风险阈值范围内,依照列线图模型对患者进行干预,相比所有患者均干预或均不干预,可获得更高的标准化净收益.结论 本研究构建的列线图模型能较准确地预测ⅠB～ⅡA期宫颈癌患者阴道侵犯发生风险,可为此类患者的肿瘤分期评估及治疗方案制定提供参考依据.
Objective To explore the risk factors for vaginal invasion in patients with stage ⅠB-ⅡA cervical cancer,construct a nomogram model,and validate the predictive performance of the model for vaginal invasion.Methods A total of 380 cervical cancer patients admitted to the Affiliated Zhangjiagang Hospital of Soochow University from September 2021 to September 2024 were retrospectively collected.The patients were randomly divided into a modeling cohort(n=266)and an internal validation cohort(n=114)in a 7:3 ratio.Additionally,150 cervical cancer patients admitted to Zhangjiagang Hospital of Traditional Chinese Medicine were retrospectively collected as an external validation cohort.Clinical data of the patients were collected.LASSO regression was used to screen key variables for inclusion in multivariate analysis.A nomogram model was constructed based on the independent factors identified by multivariate analysis.The predictive performance of the model was validated using receiver operating characteristic(ROC)curve and decision curve analysis(DCA).Results The incidence of vaginal invasion in patients with stage ⅠB-ⅡA cervical cancer in this study was 17.37％(66/380).LASSO regression analysis screened 7 key variables.Multivariate analysis showed that older age(OR=1.173,95％CI:1.097-1.254),positive MRI diagnosis of vaginal invasion(OR=3.004,95％CI:1.185-7.616),and a high systemic inflammatory response index(OR=1.327,95％CI:1.002-1.757)were independent risk factors for vaginal invasion in stage ⅠB-ⅡA cervical cancer patients,while a high prognostic nutritional index(OR=0.912,95％CI:0.859-0.968)was an independent protective factor(P<0.05).ROC curve analysis showed that the area under the curve was 0.866(95％CI:0.803-0.930)for the modeling cohort,0.828(95％CI:0.761-0.896)for the internal validation cohort,and 0.834(95％CI:0.774-0.897)for the external validation cohort.DCA results showed that within the high-risk threshold range of 0-0.6,intervening based on the nomogram model provided a higher standardized net benefit compared to intervening in all patients or in none.Conclusion The nomogram model constructed in this study can relatively accurately predict the risk of vaginal invasion in patients with stage ⅠB-ⅡA cervical cancer,providing a reference for tumor staging assessment and treatment planning for such patients.
季慧;刘晓丽;瞿佳龙
苏州大学附属张家港医院(张家港市第一人民医院)妇科,江苏张家港 215600苏州大学附属张家港医院(张家港市第一人民医院)妇科,江苏张家港 215600张家港市中医医院妇产科,江苏张家港 215600
宫颈癌阴道侵犯国际妇产科联盟分期列线图
cervical cancervaginal invasionFIGO stagenomogram
《转化医学杂志》 2026 (3)
379-385,7
2021年度江苏省妇幼健康科研项目(F202167)
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