Development and Validation of a Nomogram for Preoperative Prediction of Central Compartment Lymph Node Metastasis in Patients with Papillary Thyroid Carcinoma and Type 2 Diabetes Mellitus.

purposeTo develop and validate a nomogram to predict central compartment lymph node metastasis in PTC patients with Type 2 Diabetes. PATIENTS AND

methodsThe 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.

resultsROC 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).

conclusionA 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.