Factors Influencing Cervical Lymph Node Metastasis in Pediatric Differentiated Thyroid Cancers

Lymph node metastasis is a considerable variable influencing postoperative American Thyroid Association (ATA) risk stratification in pediatric differentiated thyroid cancer (DTC). The primary aim of this study was to ascertain the factors predicting nodal metastasis and describe the outcomes in relation to the ATA risk. Patients 18 years or younger operated between December 2005 and December 2019 were analyzed. Demographic, clinicopathological, treatment, and outcome data were recorded. Factors associated with nodal metastasis were assessed by univariate and multivariate regression analysis. Patients were stratified into low-, intermediate-, and high-risk as per the pediatric ATA guidelines. A total of 86 patients (43% male; median [IQR] age, 12 (10-14) years) underwent surgery during the study period. Lymph node metastases were present in 70 (82.4%) patients involving the lateral (8%) and central compartment (4.7%) alone and both (88.6%) compartments. Extrathyroid extension (ETE) was present in 65%; 35%, minimal; and 30%, extensive. On univariate analysis, nodal metastasis was more frequent in male patients, multifocal tumor, lymphovascular invasion, and ETE. On multivariate analysis, only ETE was predictive of nodal disease with an odds ratio of 8. Minimal and extensive ETEs were both significantly associated with lymph node metastases when compared to the absence of ETE. The 5-year disease-free survival was 100%, 95.7%, and 66% in the low-, intermediate-, and high-risk groups respectively (p < 0.0001). Pediatric DTCs have an exceptionally high incidence of lymph node metastasis. ETE is the single most important predictor of nodal disease. The ATA pediatric risk stratification is useful in predicting clinical outcomes.

Automated summary

Pediatric differentiated thyroid cancer has a high incidence of lymph node metastasis, with extrathyroid extension being the most important predictor of nodal disease. The ATA pediatric risk stratification is valuable in predicting clinical outcomes.