The First Clinical School of Shanxi Medical University, Taiyuan, China.
Department of Thyroid Surgery, the First Hospital of Shanxi Medical University, Taiyuan, China.
Medicine (Baltimore). 2023 Aug 11;102(32):e34581. doi: 10.1097/MD.0000000000034581.
In this study, a nomogram was established and validated by assessing the risk factors for the development of pulmonary metastases in patients with non-papillary thyroid carcinoma (NPTC) and was used to predict the risk of developing pulmonary metastases. Demographic and clinicopathological variables of patients with NPTC from 2010 to 2015 in the Surveillance, Epidemiology, and End Results database were retrospectively analyzed, and independent risk factors were identified using χ2 tests and full subset regression analysis. Based on this, a nomogram was developed and validated for predicting the risk of pulmonary metastasis in patients with NPTC. The predictive performance of the nomogram was calculated using the consistency index, and the clinical application value of the nomogram was evaluated using calibration curve and decision curve analyses. In addition, risk stratification of patients with NPTC based on these results was performed to facilitate early diagnosis and treatment of patients with pulmonary metastases in the clinic. Data from 1435 patients with NPTC were used for the analysis based on the inclusion and exclusion criteria. Statistical analysis yielded a high risk of pulmonary metastasis in patients with older age, high T-stage, poorly differentiated, undifferentiated thyroid carcinoma, follicular thyroid carcinoma (NOS), and the presence of other distant metastases. We further developed a nomogram with a consistency index of 0.898 (95% confidence interval: 0.880-0.920) in the training cohort and 0.895 (95% confidence interval: 0.862-0.927) in the validation cohort. The calibration curve and decision curve analyses also demonstrated the strong reliability and accuracy of this clinical prediction model. In this study, a nomogram was constructed to accurately identify patients with NPTC at a high risk of pulmonary metastasis, which will help clinicians in personalized decision-making.
在这项研究中，我们通过评估非乳头状甲状腺癌（NPTC）患者发生肺转移的风险因素来建立并验证了一个列线图，以预测发生肺转移的风险。我们回顾性分析了 2010 年至 2015 年监测、流行病学和最终结果数据库中 NPTC 患者的人口统计学和临床病理学变量，并使用卡方检验和全子集回归分析确定了独立的风险因素。在此基础上，我们为预测 NPTC 患者发生肺转移的风险开发并验证了一个列线图。通过一致性指数计算了该列线图的预测性能，并通过校准曲线和决策曲线分析评估了该列线图的临床应用价值。此外，基于这些结果对 NPTC 患者进行风险分层，以便在临床中早期诊断和治疗发生肺转移的患者。根据纳入和排除标准，共纳入 1435 例 NPTC 患者进行数据分析。统计分析显示，年龄较大、T 分期较高、分化差、未分化甲状腺癌、滤泡状甲状腺癌（NOS）和存在其他远处转移的患者发生肺转移的风险较高。我们进一步在训练队列中开发了一个一致性指数为 0.898（95%置信区间：0.880-0.920），在验证队列中开发了一个一致性指数为 0.895（95%置信区间：0.862-0.927）的列线图。校准曲线和决策曲线分析也表明，该临床预测模型具有较强的可靠性和准确性。在这项研究中，我们构建了一个列线图，可以准确识别 NPTC 患者中发生肺转移风险较高的患者，这将有助于临床医生进行个性化决策。
