Machine Learning for Predicting Distant Metastasis of Medullary Thyroid Carcinoma Using the SEER Database

Zhen-Tian Guo; Kun Tian; Xi-Yuan Xie; Yu-Hang Zhang; De-Bao Fang

doi:10.1155/2023/9965578

Machine Learning for Predicting Distant Metastasis of Medullary Thyroid Carcinoma Using the SEER Database

Int J Endocrinol. 2023 Dec 30:2023:9965578. doi: 10.1155/2023/9965578. eCollection 2023.

Authors

Zhen-Tian Guo¹, Kun Tian¹, Xi-Yuan Xie², Yu-Hang Zhang³, De-Bao Fang⁴

Affiliations

¹ Department of General Surgery, Beijing Electric Power Hospital, State Grid Corporation China, Capital Medical University, Beijing 100073, China.
² Fujian Provincial Hospital, Fuzhou, Fujian 350001, China.
³ Mudanjiang Medical University, Mudanjiang, Heilongjiang 157000, China.
⁴ Hefei National Laboratory for Physical Sciences at Microscale, School of Basic Medical Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei 230027, Anhui, China.

Abstract

Objectives: We aimed to establish an effective machine learning (ML) model for predicting the risk of distant metastasis (DM) in medullary thyroid carcinoma (MTC).

Methods: Demographic data of MTC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Institutes of Health between 2004 and 2015 to develop six ML algorithm models. Models were evaluated based on accuracy, precision, recall rate, F1-score, and area under the receiver operating characteristic curve (AUC). The association between clinicopathological characteristics and target variables was interpreted. Analyses were performed using traditional logistic regression (LR).

Results: In total, 2049 patients were included and 138 developed DM. Multivariable LR showed that age, sex, tumor size, extrathyroidal extension, and lymph node metastasis were predictive features for DM in MTC. Among the six ML models, the random forest (RF) had the best predictability in assessing the risk of DM in MTC, with an accuracy, precision, recall rate, F1-score, and AUC higher than those of the traditional binary LR model.

Conclusion: RF was superior to traditional LR in predicting the risk of DM in MTC and can provide a valuable reference for clinicians in decision-making.