| 延怡迪,石岩玉,路惠萍,等.ⅢC1r期宫颈癌患者盆腔淋巴结转移预测模型的建立及评价[J].肿瘤学杂志,2026,32(1):54-59. |
| ⅢC1r期宫颈癌患者盆腔淋巴结转移预测模型的建立及评价 |
| Establishment and Evaluation of A Nomogram for Pelvic Lymph Node Metastasis in Stage ⅢC1r Cervical Cancer Patients |
| 投稿时间：2025-06-18 |
| DOI：10.11735/j.issn.1671-170X.2026.01.B009 |
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| 中文关键词: 宫颈肿瘤 淋巴结转移 危险因素 预测模型 |
| 英文关键词:cervical neoplasms lymph node metastasis risk factors prediction model |
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| 中文摘要: |
| 摘 要： ［目的］ 基于基线临床数据、血清肿瘤标志物水平和系统性炎症免疫指标构建ⅢC1r期宫颈癌患者盆腔淋巴结转移的预测模型。［方法］ 选取郑州大学第三附属医院2018年1月至2024年12月收治的254例术前影像学提示淋巴结转移且最大径<20 mm的ⅢC1r期宫颈癌患者，患者接受了根治性子宫切除术及盆腔淋巴结清扫术，部分患者同时行腹主动脉旁淋巴结清扫术。术后病理显示盆腔淋巴结转移134例（52.76%），无盆腔淋巴结转移120例（47.24%）。研究采用R4.4.3软件将254例研究对象通过随机抽样法按7∶3比例拆分为训练集（n=178）和验证集（n=76）。采用多因素Logistic回归分析影响ⅢC1r期宫颈癌患者盆腔淋巴结转移的独立危险因素，据此构建列线图预测模型，采用受试者工作特征（receiver operating characteristic，ROC）曲线及校准曲线对模型进行验证。［结果］ ①多因素 Logistic回归分析发现鳞状细胞癌抗原（squamous cell carcinoma antigen，SCC-Ag）≥3.42 ng/mL（OR=3.517，P=0.004）、系统性免疫炎症指数（systemic immune-inflammation index，SII）≥367.38 （OR=3.695，P=0.004）、肿瘤直径≥32.95 mm（OR=3.151，P=0.009）、淋巴结最大径≥11.25 mm（OR=10.898，P=0.000）是ⅢC1r期宫颈癌患者盆腔淋巴结转移的独立危险因素。②基于以上4项独立危险因素构建了盆腔淋巴结转移风险预测模型。在模型效能验证中，训练集和验证集的ROC曲线下面积分别达0.828（95%CI：0.768~0.883）和0.833（95%CI：0.737~0.919），经验证校准曲线接近理想曲线。［结论］ 基于SCC-Ag、SII、肿瘤直径和淋巴结最大径构建的预测模型用于预测ⅢC1r期宫颈癌患者的盆腔淋巴结转移效能较佳，为 ⅢC1r期宫颈癌患者个体化诊疗提供科学的参考依据。 |
| 英文摘要: |
| Abstract：［Objective］ To develop and validate a Nomogram for predicting pelvic lymph node metastasis (PLNM) in patients with stage ⅢC1r cervical cancer based on baseline clinical characteristics， serum tumor markers， and systemic inflammatory-immune indices. ［Methods］ This study included 254 patients with stage ⅢC1r cervical cancer (preoperative imaging suggesting lymph node metastasis with a maximum diameter <20 mm) from The Third Affiliated Hospital of Zhengzhou University between January 2018 and December 2024. All patients underwent radical hysterectomy and pelvic lymphadenectomy， with some additionally undergoing para-aortic lymphadenectomy. Postoperative pathology confirmed PLNM in 134 cases (52.76%) and no PLNM in 120 cases (47.24%). Patients were randomly divided into a training set (n=178) and a validation set (n=76) in a 7∶3 ratio using R software (version 4.4.3). Multivariate Logistic regression analysis identified independent risk factors for PLNM， which were used to construct a Nomogram. The model’s performance was assessed using receiver operating characteristic (ROC) curves and calibration curves. ［Results］ Multivariate analysis identified four independent risk factors for PLNM： squamous cell carcinoma antigen (SCC-Ag) ≥3.42 ng/mL (OR=3.517， P=0.004)， systemic immune-inflammation index (SII) ≥367.38 (OR=3.695， P=0.004)， tumor diameter ≥32.95 mm (OR=3.151， P=0.009)， and maximum lymph node diameter ≥11.25 mm (OR=10.898， P=0.000). A Nomogram incorporating these factors was developed. The area under the ROC curve (AUC) was 0.828 (95%CI： 0.768~0.883) for the training set and 0.833 (95%CI： 0.737~0.919) for the validation set. Calibration curves demonstrated good agreement between predicted and observed probabilities. ［Conclusion］ The Nomogram based on SCC-Ag， SII， tumor diameter， and maximum lymph node diameter showed good predictive performance for PLNM in stage ⅢC1r cervical cancer， and may serve as a valuable tool for guiding individualized treatment strategies. |
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