Wang Lirong, Zhang Lin, Wang Dan, Chen Jiawen, Su Wenxiu, Sun Lei, Jiang Jue, Wang Juan, Zhou Qi
Department of Ultrasound, the Second Affiliated Hospital of Xi 'an Jiaotong University, Xi 'an, Shannxi, China.
Department of Otolaryngology-Head and Neck Surgery, the Second Affiliated Hospital of Xi 'an Jiaotong University, Xi 'an, Shannxi, China.
PeerJ. 2024 Apr 19;12:e17108. doi: 10.7717/peerj.17108. eCollection 2024.
In papillary thyroid carcinoma (PTC) patients with Hashimoto's thyroiditis (HT), preoperative ultrasonography frequently reveals the presence of enlarged lymph nodes in the central neck region. These nodes pose a diagnostic challenge due to their potential resemblance to metastatic lymph nodes, thereby impacting the surgical decision-making process for clinicians in terms of determining the appropriate surgical extent.
Logistic regression analysis was conducted to identify independent risk factors associated with central lymph node metastasis (CLNM) in PTC patients with HT. Then a prediction model was developed and visualized using a nomogram. The stability of the model was assessed using ten-fold cross-validation. The performance of the model was further evaluated through the use of ROC curve, calibration curve, and decision curve analysis.
A total of 376 HT PTC patients were included in this study, comprising 162 patients with CLNM and 214 patients without CLNM. The results of the multivariate logistic regression analysis revealed that age, Tg-Ab level, tumor size, punctate echogenic foci, and blood flow grade were identified as independent risk factors associated with the development of CLNM in HT PTC. The area under the curve (AUC) of this model was 0.76 (95% CI [0.71-0.80]). The sensitivity, specificity, accuracy, and positive predictive value of the model were determined to be 88%, 51%, 67%, and 57%, respectively.
The proposed clinic-ultrasound-based nomogram in this study demonstrated a favorable performance in predicting CLNM in HT PTCs. This predictive tool has the potential to assist clinicians in making well-informed decisions regarding the appropriate extent of surgical intervention for patients.
在患有桥本甲状腺炎(HT)的乳头状甲状腺癌(PTC)患者中,术前超声检查经常显示颈部中央区域存在肿大淋巴结。由于这些淋巴结可能与转移性淋巴结相似,给诊断带来挑战,从而在确定合适的手术范围方面影响临床医生的手术决策过程。
进行逻辑回归分析以确定HT合并PTC患者中央淋巴结转移(CLNM)的独立危险因素。然后使用列线图开发并可视化预测模型。使用十折交叉验证评估模型的稳定性。通过ROC曲线、校准曲线和决策曲线分析进一步评估模型的性能。
本研究共纳入376例HT合并PTC患者,其中162例有CLNM,214例无CLNM。多因素逻辑回归分析结果显示,年龄、Tg-Ab水平、肿瘤大小、点状强回声灶和血流分级被确定为HT合并PTC患者发生CLNM的独立危险因素。该模型的曲线下面积(AUC)为0.76(95%CI[0.71-0.80])。该模型的敏感性、特异性、准确性和阳性预测值分别确定为88%、51%、67%和57%。
本研究中提出的基于临床-超声的列线图在预测HT合并PTC患者的CLNM方面表现良好。这种预测工具有可能帮助临床医生就患者适当的手术干预范围做出明智的决策。