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甲状腺球蛋白和抗体定量预测分化型甲状腺癌早期淋巴结转移的列线图模型

A nomogram model for early lymph node metastasis of differentiated thyroid carcinoma on basis of thyroglobulin and antibody quantification

    摘要
  • 摘要:
      目的  探讨甲状腺球蛋白（Tg）和抗体定量与分化型甲状腺癌（DTC）早期淋巴结转移的关系并构建定量列线图模型。
      方法  选取86例甲状腺癌根治术患者作为研究对象，术前超声检查发现颈区可疑淋巴结转移113枚，术后病理证实阳性74枚（57例患者，纳入阳性淋巴结组）、阴性39枚（29例患者，纳入阴性淋巴结组）。比较2组超声定量评分、细针穿刺细胞学检查（FNA-C）结果和穿刺针洗脱液中甲状腺球蛋白（FNA-Tg）、BRAF基因突变、血清Tg、甲状腺球蛋白抗体（TgAb）、甲状腺激素情况。
      结果  2组性别、年龄、肿瘤分期、分化级别、病理类型、超声定量评分、BRAF基因突变阳性率、血清Tg水平、甲状腺激素水平比较，差异无统计学意义（P>0.05）。阳性淋巴结组FNA-C阳性率、FNA-Tg水平、血清TgAb水平高于阴性淋巴结组，差异有统计学意义（P < 0.05）。多因素Logistic回归分析显示，FNA-C阳性、FNA-Tg≥25 μg/L和血清TgAb≥500 U/mL是淋巴结转移阳性的预测因子（P < 0.05）。受试者工作特征曲线显示，超声定量评分、FNA-C、FNA-Tg和列线图模型预测淋巴结转移阳性的曲线下面积分别为0.689、0.754、0.795和0.856（P < 0.05）。
      结论  FNA-C和FNA-Tg是术前诊断DTC淋巴结转移的重要依据。通过大样本数据构建的列线图模型对淋巴结转移阳性有较好的预测效能，可帮助医生术前准确快速地判断淋巴结转移情况，且克服了单独FNA-C或FNA-Tg的诊断不足，具有较强的实践性。

     

    Abstract:
      Objective  To investigate the relationships between the quantification of thyroglobulin(Tg), quantitative antibody and early lymph node metastasis in differentiated thyroid carcinoma (DTC), and construct a quantitative nomogram model.
      Methods  A total of 86 patients who underwent radical thyroidectomy were chosen as research objects, 113 lymph nodes with suspicious metastases in the neck area were detected by ultrasonography before operation, including 74 positive(57 patients, positive lymph node group) nodes and 39 negative(29 patients, negative lymph node group) nodes confirmed by pathology after operation. The ultrasonic quantitative score of lymph nodes, fine needle aspiration cytology (FNA-C), fine needle aspiration thyroglobulin (FNA-Tg) and BRAF gene mutation in puncture needle eluent, serum Tg, thyroglobulin antibody (TgAb) and thyroid hormones were compared.
      Results  There were no differences of gender, age, tumor stage, differentiation grade and pathological type, quantitative ultrasound score, positive rate of BRAF gene mutation, serum Tg and thyroid hormone levels (P>0.05). FNA-C positive rate, FNA-Tg and serum TgAb levels in the positive lymph node group were significantly higher than those in the negative lymph node group (P < 0.05). Multivariate Logistic regression analysis showed that positive for FNA-C, FNA-Tg≥25 μg/L and serum TgAb≥500 U/mL were predictors of positive lymph node metastasis (P < 0.05). Receiver operating curve showed that the area under the curve of ultrasound quantitative score, FNA-C, FNA-Tg and nomogram model for predicting positive lymph node metastasis were 0.689, 0.754, 0.795 and 0.856, respectively (P < 0.05).
      Conclusion  FNA-C and FNA-Tg are important basis in diagnosing DTC lymph node metastasis before operation. The nomogram model constructed by large sample data has good predictive efficiency for positive lymph node metastasis. The nomogram model can help doctors accurately and quickly judge metastatic lymph nodes before operation, and overcome the diagnosis deficiency of FNA-C or FNA-Tg alone, and has strong practicability.

     

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