甲状腺乳头状癌颈淋巴结转移预测模型。
Prediction Model of Cervical Lymph Node Metastasis in Papillary Thyroid Carcinoma.
机构信息
Department of Ultrasound Imaging, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Department of Ultrasound Imaging, National Regional Medical Center, Binhai Campus of the First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
出版信息
Cancer Control. 2024 Jan-Dec;31:10732748241295347. doi: 10.1177/10732748241295347.
BACKGROUND
The objective of this study is to develop a predictive model for the assessment of cervical lymph node metastasis risk in papillary thyroid carcinoma (PTC).
METHODS
A retrospective study was conducted on 212 patients with PTC who underwent initial surgical treatment from August 2022 to April 2023 in 2 hospitals. Data were randomly split into 7:3 training-validation sets. Logistic regression was used for feature selection and predictive model creation. Model performance was assessed using receiver operating characteristic (ROC) and calibration curves. Clinical utility was determined using decision curves.
RESULTS
Among the 212 patients with PTC, 104 cases (49.1%) exhibited cervical lymph node metastasis, while 108 cases (50.9%) did not. Multivariate logistic regression analysis revealed that age (OR = 0.95), FT3 (OR = 0.41), tumor maximum diameter ≥0.9 cm (OR = 1.85), intratumoral microcalcifications (OR = 1.84), and suspicious lymph node on ultrasound (OR = 2.96) were independent risk factors for lymph node metastasis in PTC patients ( < 0.05). The constructed model for predicting the risk of cervical lymph node metastasis demonstrated a training set ROC curve area under the curve (AUC) of 0.742 (95% CI: 0.664 - 0.821), with a cut-off value of 0.615, specificity of 87.8%, and sensitivity of 51.4%. The validation set exhibited an AUC of 0.648 (95% CI: 0.501 - 0.788), with a cut-off value of 0.644, specificity of 91.2%, and sensitivity of 43.3%. Including the BRAF V600 E mutation did not improve the model's diagnostic performance significantly. Decision curve analysis indicated clinical feasibility of the model.
CONCLUSION
The predictive model developed in this study effectively predicts lymph node metastasis risk in PTC patients by incorporating ultrasound features, demographic characteristics, and serum parameters. However, including the BRAF V600 E mutation does not significantly improve the model's diagnostic performance.
背景
本研究旨在建立一个预测甲状腺乳头状癌（PTC）颈部淋巴结转移风险的模型。
方法
对 2022 年 8 月至 2023 年 4 月在 2 家医院接受初始手术治疗的 212 例 PTC 患者进行回顾性研究。数据随机分为 7:3 的训练-验证集。使用逻辑回归进行特征选择和预测模型创建。使用接收者操作特征（ROC）和校准曲线评估模型性能。使用决策曲线确定临床实用性。
结果
在 212 例 PTC 患者中，104 例（49.1%）出现颈部淋巴结转移，108 例（50.9%）未出现。多变量逻辑回归分析显示，年龄（OR=0.95）、FT3（OR=0.41）、肿瘤最大直径≥0.9cm（OR=1.85）、肿瘤内微钙化（OR=1.84）和超声可疑淋巴结（OR=2.96）是 PTC 患者颈部淋巴结转移的独立危险因素（<0.05）。构建的预测颈部淋巴结转移风险的模型在训练集的 ROC 曲线下面积（AUC）为 0.742（95%CI：0.664-0.821），截断值为 0.615，特异性为 87.8%，敏感性为 51.4%。验证集的 AUC 为 0.648（95%CI：0.501-0.788），截断值为 0.644，特异性为 91.2%，敏感性为 43.3%。纳入 BRAF V600E 突变并未显著提高模型的诊断性能。决策曲线分析表明该模型具有临床可行性。
结论
该研究建立的预测模型通过纳入超声特征、人口统计学特征和血清参数，有效预测 PTC 患者的淋巴结转移风险。然而，纳入 BRAF V600E 突变并未显著提高模型的诊断性能。
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