He Chao, Lu Yiqiao, Wang Binqi, He Jie, Liu Haiguang, Zhang Xiaohua
Department of Surgical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, People's Republic of China.
The Second Clinical Medicine Faculty, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People's Republic of China.
Cancer Manag Res. 2021 Mar 17;13:2499-2513. doi: 10.2147/CMAR.S300264. eCollection 2021.
To develop and validate a nomogram to predict central compartment lymph node metastasis in PTC patients with Type 2 Diabetes.
The total number of enrolled patients was 456. The optimal cut-off values of continuous variables were obtained by ROC curve analysis. Significant risk factors in univariate analysis were further identified to be independent variables in multivariable logistic regression analysis, which were then incorporated and presented in a nomogram. The ROC curve analysis was performed to evaluate the discrimination of the nomogram, calibration curves and Hosmer-Lemeshow test were used to visualize and quantify the consistency. Decision curve analysis (DCA) was performed to evaluate the net clinical benefit patients could get by applying this nomogram.
ROC curve analysis showed the optimal cutoff values of NLR, PLR, and tumor size were 2.9204, 154.7003, and 0.95 (cm), respectively. Multivariate logistic regression analysis indicated that age, multifocality, largest tumor size, and neutrophil-to-lymphocyte ratio were independent prognostic factors of CLNM. The C-index of this nomogram in the training data set was 0.728, and 0.618 in the external validation data set. When we defined the predicted possibility (>0.5273) as high-risk of CLNM, we could get a sensitivity of 0.535, a specificity of 0.797, a PPV(%) of 67.7, and an NPV(%) of 68.7. Great consistencies were represented in the calibration curves. DCA showed that applying this nomogram will help patients get more clinical net benefit than having all of the patients or none of the patients treated with central compartment lymph node dissection (CLND).
A high level of preoperative NLR was an independent predictor for CLNM in PTC patients with T2DM. And the verified optimal cutoff value of NLR in this study was 2.9204. Applying this nomogram will help stratify high-risk CLNM patients, consequently enabling these patients to be treated with appropriate measures. What is more, we hope to find more sensitive indicators in the near future to further improve the sensitivity and specificity of our nomogram.
开发并验证一种列线图，以预测2型糖尿病甲状腺乳头状癌（PTC）患者中央区淋巴结转移情况。
共纳入456例患者。通过ROC曲线分析获得连续变量的最佳截断值。单因素分析中的显著危险因素在多因素逻辑回归分析中进一步确定为自变量，然后纳入列线图并呈现。进行ROC曲线分析以评估列线图的辨别力，使用校准曲线和Hosmer-Lemeshow检验来可视化和量化一致性。进行决策曲线分析（DCA）以评估应用该列线图患者可获得的净临床获益。
ROC曲线分析显示中性粒细胞与淋巴细胞比值（NLR）、血小板与淋巴细胞比值（PLR）和肿瘤大小的最佳截断值分别为2.9204、154.7003和0.95（cm）。多因素逻辑回归分析表明，年龄、多灶性、最大肿瘤大小和中性粒细胞与淋巴细胞比值是中央区淋巴结转移（CLNM）的独立预后因素。该列线图在训练数据集的C指数为0.728，在外部验证数据集的C指数为0.618。当我们将预测可能性（>0.5273）定义为CLNM高风险时，敏感性为0.535，特异性为0.797，阳性预测值（PPV，%）为67.7，阴性预测值（NPV，%）为68.7。校准曲线显示出良好的一致性。DCA表明，应用该列线图比所有患者均接受或均不接受中央区淋巴结清扫术（CLND）能为患者带来更多的临床净获益。
术前高水平的NLR是T2DM的PTC患者CLNM的独立预测因素。本研究中验证的NLR最佳截断值为2.9204。应用该列线图有助于对CLNM高风险患者进行分层，从而使这些患者能够得到适当的治疗措施。此外，我们希望在不久的将来找到更敏感的指标，以进一步提高我们列线图的敏感性和特异性。
