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CN 11-3017/P
何俊林, 陈文静, 胡莉莉, 等. 基于影像-血清学-临床多参数术前预测单灶性甲状腺乳头状癌颈部淋巴结转移的研究[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.112.
引用本文: 何俊林, 陈文静, 胡莉莉, 等. 基于影像-血清学-临床多参数术前预测单灶性甲状腺乳头状癌颈部淋巴结转移的研究[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.112.
HE J L, CHEN W J, HU L L, et al. Predicting Cervical Lymph Node Metastasis Using Preoperative Multiparameter Data Based on Imaging, Serology, and Clinical Features in Unifocal Papillary Thyroid Carcinoma[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.112. (in Chinese).
Citation: HE J L, CHEN W J, HU L L, et al. Predicting Cervical Lymph Node Metastasis Using Preoperative Multiparameter Data Based on Imaging, Serology, and Clinical Features in Unifocal Papillary Thyroid Carcinoma[J]. CT Theory and Applications, xxxx, x(x): 1-9. DOI: 10.15953/j.ctta.2024.112. (in Chinese).

基于影像-血清学-临床多参数术前预测单灶性甲状腺乳头状癌颈部淋巴结转移的研究

Predicting Cervical Lymph Node Metastasis Using Preoperative Multiparameter Data Based on Imaging, Serology, and Clinical Features in Unifocal Papillary Thyroid Carcinoma

  • 摘要: 目的:评价超声、CT、临床、血清学多参数术前预测单灶性甲状腺乳头状癌(PTC)颈部中央区淋巴结转移(CLNM)的价值,开发一个列线图用于术前预测CLNM。对象和方法:连续收集2019年1月至2023年12月在我院经手术病理证实的340例单灶性PTC患者的超声、CT、临床、甲状腺功能检查的术前多参数资料。相关专业不同年资的医师评价和记录各个参数的特征后,单因素分析各个特征与CLNM的相关性,多因素Logistic回归分析(向前法)筛选CLNM的独立危险因素(IRF)。340例患者以7∶3随机分成训练组(n=237)和验证组(n=103),预测列线图由IRFs在训练组构建,并在验证组中验证。评价列线图预测性能的指标包括受试者工作特性曲线(ROC)、校准曲线(calibration)以及决策曲线分析(DCA)。结果:单灶性PTC的多参数特征中,2个临床特征、4个CT影像特征、7个超声影像特征以及甲状腺功能检查的7项指标被纳入研究。年龄≤55岁、男性性别、肿瘤最大径>10 mm、被膜接触>0和肿瘤边缘不规则是CLNM的IRFs。列线图的预测性能在训练组和验证组的ROC曲线下面积分别是0.815(95%CI:0.761~0.870)和0.747(95%CI:0.646~0.848),calibration曲线和DCA显示了列线图的预测准确性和临床实用性。结论:单灶性PTC的术前多参数特征能够有效地预测CLNM,构建的预测列线图能够辅助临床决策,提升患者受益。

     

    Abstract: Objective: To evaluate the predictive performance of preoperative multiparameter data, including computed tomography (CT), ultrasound, clinical features, and serology, for central cervical lymph node metastasis (CLNM) in unifocal papillary thyroid carcinoma (PTC) and to develop a nomogram model for predicting CLNM preoperatively. Methods: Data were collected from 340 consecutive patients with pathologically confirmed unifocal PTC who underwent radical thyroidectomy between January 2019 and December 2023. Radiologists with varying levels of experience analyzed the multiparameter data, including clinical features, CT, ultrasound, and thyroid function tests. The correlation between these characteristics and CLNM was assessed using univariate analysis, and independent risk factors (IRFs) were identified via forward selection in multivariate logistic regression analysis. The patients were randomly divided into training and validation cohorts at a 7:3 ratio. A nomogram model for predicting CLNM was established using IRFs from the training cohort and tested on the validation cohort. The predictive performance of the nomogram was evaluated using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results: A total of 20 characteristics, including two clinical features, four CT features, seven ultrasound features, and seven thyroid hormone levels, were recorded and analyzed. Age ≤55 years, male sex, maximum lesion diameter >10 mm (ultrasound), capsular contact >0 (CT), and irregular tumor margin (ultrasound) were identified as IRFs for CLNM. The area under the curve of the nomogram ROCs in the training and validation cohorts was 0.815 (95% CI: 0.761–0.870) and 0.747 (95% CI: 0.646–0.848), respectively. The calibration curve and DCA showed excellent utility of the nomogram for predicting CLNM. Conclusions: The multiparameter characteristics of unifocal PTC can effectively predict CLNM. The constructed nomogram can assist in clinical decision-making and benefit patients with PTC.

     

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