Department of General Surgery, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Wuhan, P.R. China.
Department of Thyroid and Breast Surgery, Maternal and Child Health Hospital of Hubei Province, Wuhan, P.R. China.
J Pediatr Endocrinol Metab. 2024 Feb 9;37(3):250-259. doi: 10.1515/jpem-2023-0425. Print 2024 Mar 25.
The objective of this study was to develop and evaluate the efficacy of a nomogram for predicting lung metastasis in pediatric differentiated thyroid cancer.
The SEER database was utilized to collect a dataset consisting of 1,590 patients who were diagnosed between January 2000 and December 2019. This dataset was subsequently utilized for the purpose of constructing a predictive model. The model was constructed utilizing a multivariate logistic regression analysis, incorporating a combination of least absolute shrinkage feature selection and selection operator regression models. The differentiation and calibration of the model were assessed using the C-index, calibration plot, and ROC curve analysis, respectively. Internal validation was performed using a bootstrap validation technique.
The results of the study revealed that the nomogram incorporated several predictive variables, namely age, T staging, and positive nodes. The C-index had an excellent calibration value of 0.911 (95 % confidence interval: 0.876-0.946), and a notable C-index value of 0.884 was achieved during interval validation. The area under the ROC curve was determined to be 0.890, indicating its practicality and usefulness in this context.
This study has successfully developed a novel nomogram for predicting lung metastasis in children and adolescent patients diagnosed with thyroid cancer. Clinical decision-making can be enhanced by assessing clinicopathological variables that have a significant predictive value for the probability of lung metastasis in this particular population.
本研究旨在开发并评估一种列线图在预测儿童分化型甲状腺癌肺转移中的疗效。
利用 SEER 数据库收集了 2000 年 1 月至 2019 年 12 月期间诊断的 1590 例患者的数据集，用于构建预测模型。该模型采用多变量逻辑回归分析，结合最小绝对收缩和选择算子回归模型进行构建。采用 C 指数、校准图和 ROC 曲线分析分别评估模型的区分度和校准度。采用自举验证技术进行内部验证。
研究结果表明，该列线图纳入了年龄、T 分期和阳性淋巴结等预测变量。C 指数的校准值为 0.911（95%置信区间：0.876-0.946），区间验证时的 C 指数值为 0.884，表现出良好的校准性能。ROC 曲线下面积为 0.890，表明其在该人群中的实用性和有效性。
本研究成功开发了一种用于预测儿童和青少年甲状腺癌患者肺转移的新型列线图。通过评估对该特定人群肺转移概率具有显著预测价值的临床病理变量，可辅助临床决策。
