Prediction of central lymph node metastasis in papillary thyroid microcarcinoma according to clinicopathologic factors and thyroid nodule sonographic features: a case-control study

• Published in: Cancer management and research Vol. 10; pp. 3237 - 3243
• Main Authors: Jin, Wen-Xu, Ye, Dan-Rong, Sun, Yi-Han, Zhou, Xiao-Fen, Wang, Ou-Chen, Zhang, Xiao-Hua, Cai, Ye-Feng
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2018; Taylor & Francis Ltd; Dove Medical Press
• Subjects: Biopsy; Cancer metastasis; Care and treatment; Central lymph node; Complications and side effects; Deoxyribonucleic acid; Diagnosis; Displays (Marketing); DNA; Gene mutation; Lobectomy; Lymphatic metastasis; Lymphatic system; Medical prognosis; Medical research; Metastasis; Methods; Mutation; Original Research; Papillary thyroid microcarcinoma; Predictive factor; Preoperative care; Risk factors; Support vector machine; Surgery; Systematic review; Thyroid cancer; Thyroid diseases; Tumors; China; central lymph node; support vector machine; papillary thyroid microcarcinoma; predictive factor
• ISSN: 1179-1322; 1179-1322
• DOI: 10.2147/cmar.s169741

Preoperative diagnosis of central lymph node metastasis (CLNM) poses to be a challenge in clinical node-negative papillary thyroid microcarcinoma (PTMC). This research work aims at investigating the association existing between BRAF mutation, clinicopathological factors, ultrasound characteristics, and CLNM, in addition to establishing a predictive model for CLNM in PTMC. The study included 673 PTMC patients, already undergone total thyroidectomy or lobectomy with prophylactic central lymph node dissection. The predictor factors were identified through univariate and multivariate analyses. The support vector machine was put to use to develop statistical models, which could predict CLNM on the basis of independent predictors. Tumor size (>5 mm), lower location, no well-defined margin, contact of >25% with the adjacent capsule, display of enlarged lymph nodes, and BRAF mutation were independent predictors of CLNM. Through the use of the predictive model, 79.6% of the patients were classified accurately, the sensitivity and specificity amounted to be 85.1% and 75.8%, respectively, and the positive predictive value and negative predictive value stood at 71.6% and 87.6%, respectively. We established a predictive model in order to predict CLNM preoperatively in PTMC when preoperative diagnosis of CLNM was not clear.