Development and validation of a nomogram for preoperative prediction of cervical lymph node involvement in thyroid microcarcinoma

Abstract

Cervical regional lymph node involvement (CRLNI) is common in papillary thyroid microcarcinoma (PTMC), but the way to deal with cervical lymph node involvement of clinically negative PTMC is controversial. We studied data of patients histologically confirmed PTMC in the Surveillance, Epidemiology, and End Results (SEER) Program and Department of Surgical Oncology in Hangzhou First People's Hospital (China). We screened 6 variables of demographic and clinicopathological characteristics as potential predictors and further constructed a lymph node involvement model based on the independent predictors including age, race, sex, extension, multifocality and tumor size. The model was validated by both the internal and the external testing sets, and the visual expression of the model was displayed by a nomogram. As a result, the C-index of this predictive model in the training set was 0.766, and the internal and external testing sets through cross-validation were 0.753 and 0.668, respectively. The area under the receiver operating characteristic curve (AUC) was 0.766 for the training set. We also performed a Decision Curve Analysis (DCA), which showed that predicting the cervical lymph node involvement risk applying this nomogram would be better than having all patients or none patients use this nomogram.

Keywords: SEER; cervical regional lymph node involvement; model; nomogram; papillary thyroid microcarcinoma.

Figure 1

Demographic and clinicopathological characteristics screening applying the LASSO logistic regression model. Final predictors include age, race, sex, extension, multifocality, tumor size. (A) Suitable parameter (λ) selection in the LASSO model used 5-fold cross-validation via minimum criteria [–40]. We plotted the partial likelihood deviance (binomial deviance) curve versus log (λ). 2 dotted vertical lines were drawn at the optimal values applying the minimum criteria and the 1 standard error of the minimum criteria (the 1-SE criteria). (B) LASSO coefficient profiles of the 6 variables. We produced a coefficient profile plot against the log (λ) sequence. A suitable λ was chosen when log (λ)= -5 and resulted in 6 variables with nonzero coefficients. LASSO=least absolute shrinkage and selection operator, SE=standard error.

Figure 2

Nomogram for predicting CRLNI in PTMC patients. CRLNI=cervical regional lymph node involvement.

Figure 3

Calibration curves of the nomogram for predicting CRLNI in PTMC patients. (A) Calibration curve of the nomogram for training set. (B) Calibration curve of the nomogram for internal testing set. (C) Calibration curve of the nomogram for external testing set. The x-axis represents the predicted CRLNI. The y-axis represents the actual CRLNI. The diagonal dotted line stands for a perfect prediction using an ideal model. We drew the solid line to represent the performance of the nomogram, of which the closer fit to the diagonal dotted line represents the better prediction of the nomogram.

Figure 4

ROC curve analysis to predict CRLNI in PTMC patients. (A) ROC curve for the training set. (B) ROC curve for the internal testing set. (C) ROC curve for the external testing set. AUC=area under ROC curve; ROC= receiver operating characteristic.

Figure 5

Decision curve analysis for CRLNI in PTMC patients. The y-axis represents the net benefit. The red line represents the nomogram of CRLNI. The grey line displays the assumption that all patients have CRLNI. The black line represents the assumption that no patients have CRLNI. The decision curve showed that predicting the CRLNI risk applying this nomogram would be better than having all patients or none patients treated by this nomogram with a range of the threshold probability between >1% and <75.