Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence

Abstract

Abstract This study analyzed the prognostic significance of clinico-pathologic factors, including the number of metastatic lymph nodes (LNs) and lymph node ratio (LNR), in patients with papillary thyroid carcinoma (PTC), and attempted to construct a disease recurrence prediction model using machine learning techniques. We retrospectively analyzed clinico-pathologic data from 1040 patients diagnosed with PTC between 2003 and 2009. We analyzed clinico-pathologic factors related to recurrence through logistic regression analysis. Among the factors that we included, only sex and tumor size were significantly correlated with disease recurrence. Parameters such as age, sex, tumor size, tumor multiplicity, ETE, ENE, pT, pN, ipsilateral central LN metastasis, contralateral central LNs metastasis, number of metastatic LNs, and LNR were input for construction of a machine learning prediction model. The performance of five machine learning models related to recurrence prediction was compared based on accuracy. The Decision Tree model showed the best accuracy at 95%, and the lightGBM and stacking model together showed 93% accuracy. Among those factors mentioned above, LNR and contralateral LN metastasis were used as important features in all machine learning prediction models. We confirmed that all machine learning prediction models showed an accuracy of 90% or more for predicting disease recurrence in PTC. LNR and contralateral LN metastasis were used as important features for constructing a robust machine learning prediction model. In the future, we have a plan to perform large-scale multicenter clinical studies to improve the performance of our prediction models and verify their clinical effectiveness.