Department of Thyroid Surgery, The China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, China.
Front Endocrinol (Lausanne). 2022 Oct 7;13:1025739. doi: 10.3389/fendo.2022.1025739. eCollection 2022.
The preoperative risk stratification for patients with papillary thyroid carcinoma (PTC) plays a crucial role in guiding individualized treatment. We aim to construct a predictive model that aids in distinguishing between patients with low-risk and high-risk PTC based on preoperative clinical and ultrasound characteristics.
Patients who underwent open surgery and were diagnosed with PTC a postoperative pathological report between January 2020 and December 2020 were retrospectively reviewed. Data including basic information, preoperative ultrasound characteristics, thyroid function, and postoperative pathology characteristics were obtained. Univariate logistic regression analysis and least absolute shrinkage and selection operator regression analysis were performed to screen candidate variables. Finally, the preoperative predictive model for PTC was established based on the results of the multivariate logistic regression analysis.
A total of 1,875 patients with PTC were enrolled. Eight variables (sex, age, number of foci, maximum tumor diameter on ultrasound, calcification, capsule, lymph node status on ultrasound, and thyroid peroxidase (TPO) antibody level) significantly associated with risk stratification were included in the predictive model. A nomogram was constructed for clinical utility. The model showed good discrimination, and the area under the curve was 0.777 [95% confidence interval (CI): 0.752-0.803] and 0.769 (95% CI: 0.729-0.809) in the training set and validation set, respectively. The calibration curve exhibited a rather good consistency with the perfect prediction. Furthermore, decision curve analysis and clinical impact curve showed that the model had good efficacy in predicting the prognostic risk of PTC.
The nomogram model based on preoperative indicators for predicting the prognostic stratification of PTC showed a good predictive value. This could aid surgeons in deciding on individualized precision treatments.
术前对甲状腺乳头状癌（PTC）患者进行风险分层对指导个体化治疗至关重要。我们旨在构建一个预测模型，根据术前临床和超声特征，帮助区分低危和高危 PTC 患者。
回顾性分析 2020 年 1 月至 2020 年 12 月期间接受开放手术并经术后病理报告诊断为 PTC 的患者。收集患者的基本信息、术前超声特征、甲状腺功能和术后病理特征等数据。采用单因素逻辑回归分析和最小绝对收缩和选择算子回归分析筛选候选变量。最后，基于多因素逻辑回归分析的结果，建立 PTC 的术前预测模型。
共纳入 1875 例 PTC 患者。有 8 个变量（性别、年龄、病灶数量、超声最大肿瘤直径、钙化、包膜、超声淋巴结状态和甲状腺过氧化物酶（TPO）抗体水平）与风险分层显著相关，纳入预测模型。为临床应用构建了一个列线图。该模型具有良好的判别能力，在训练集和验证集中，曲线下面积分别为 0.777[95%置信区间（CI）：0.752-0.803]和 0.769（95%CI：0.729-0.809）。校准曲线与完美预测具有较好的一致性。此外，决策曲线分析和临床影响曲线表明，该模型在预测 PTC 预后风险方面具有良好的疗效。
基于术前指标预测 PTC 预后分层的列线图模型具有良好的预测价值，有助于外科医生制定个体化的精准治疗方案。
