Application of Machine Learning to Predict Cervical Lymph Node Metastasis from Micropapillary Thyroid Carcinoma with Ultrasound

Abstract

Background This study aims to determine the prediction performance of a machine learning-based clinical model for cervical lymph node metastasis (CLNM) in micropapillary thyroid carcinoma (MPTC) with ultrasound (US).Methods Patients with MPTC who underwent total or hemithyroidectomy with unilateral or bilateral prophylactic central neck dissection were included (n = 692). Nodal status was pathologically determined. Clinical and US features and thyroid function markers were extracted to build a random forest model. A nomogram with the significant predictive risk factors from multivariable logistic regression analysis was built to visualize hazard rates. Finally, the predictive performances of the models were compared.Results Overall, 332 patients (47.98%) had CLNM. In multiple logistic regression, the strong predictive risk factors for CLNM were younger age, larger anteroposterior diameter, lower anteroposterior/transverse diameter (A/T) ratio, and higher thyroglobulin (TG) concentration (P < 0.05). The random forest and nomogram models showed good predictive performance with the area under the curves (AUCs) of 0.836 and 0.780, respectively, which were significantly higher than those without A/T ratio in the models (AUCs: 0.807 vs. 0.722, all P < 0.05). The AUC of the A/T ratio as a single feature for predicting CLNM was 0.744, while A/T ratio (≤ 0.828) combined with anteroposterior diameter (≥ 10 mm) yielded a higher AUC of 0.754 for predicting CLNM.Conclusions The machine learning-based clinical model with US had a good predictive performance for CLNM in MPTC patients. This clinical model may facilitate surgical decision-making for MPTC, especially regarding whether cervical lymph node dissection is warranted.