Department of Surgical Oncology, Shaanxi Provincial People's Hospital, Xi'an, 710068, Shaanxi, China.
Langenbecks Arch Surg. 2024 Oct 23;409(1):321. doi: 10.1007/s00423-024-03503-9.
Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice.
Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation.
A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability.
In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis.
甲状腺乳头状癌（PTC）的病理亚型是甲状腺癌的重要因素。一些罕见的亚型表现出广泛的淋巴结转移。这些病理亚型在临床实践中应得到更多关注。
从 SEER 数据库中选择不同病理亚型的 PTC 患者。使用逻辑回归、随机森林和装袋（bagging）方法筛选训练队列中与颈部淋巴结转移相关的危险因素。基于曲线下面积（AUC）最大的模型建立列线图，并通过校准曲线进行评估。决策曲线分析（DCA）用于评估对患者的临床获益。通过 200 次 10 倍交叉验证对列线图进行深度验证。
共纳入 7882 例患者，其中 5516 例在训练组，2366 例在测试组。逻辑回归模型的 AUC 最高，为 0.7396。性别、年龄、种族、延伸（甲状腺外延伸）、病理类型和原发肿瘤大小被确定为颈部淋巴结转移的独立危险因素（p<0.05）。校准曲线表明该模型具有良好的校准度。DCA 表明列线图模型具有良好的临床实用性。
在临床实践中，考虑 PTC 的病理亚型很重要。建立的列线图可作为评估颈部淋巴结转移的预测工具。
