Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer

Abstract

Objectives This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly diagnosed thyroid cancer (TC). Methods Demographic and clinicopathologic variables of TC patients in the Surveillance, Epidemiology, and End Results database from 2010 to 2016 were retrospectively analyzed. On this basis, we developed a random forest (RF) algorithm model based on machine‐learning. The area under receiver operating characteristic curve (AUC), accuracy score, recall rate, and specificity are used to evaluate and compare the prediction performance of the RF model and the other model. Results A total of 17,138 patients were included in the study, with 166 (0.97%) developed bone metastases. Grade, T stage, histology, race, sex, age, and N stage were the important prediction features of BM. The RF model has better predictive performance than the other model (AUC: 0.917, accuracy: 0.904, recall rate: 0.833, and specificity: 0.905). Conclusions The RF model constructed in this study could accurately predict bone metastases in TC patients, which may provide clinicians with more personalized clinical decision‐making recommendations. Machine learning technology has the potential to improve the development of BM prediction models in TC patients.

Application of Machine Learning Techniques to Predict Bone Metastasis in Patients with Prostate Cancer

“… 13 , 27 Studies have already used machine learning technology to predict the development of diseases. 12 , 28 In this study, several widely used machine learning algorithms were developed and validated to predict the risk of BM in PCa patients. After the comparison of algorithms with several evaluation indicators, the XGB algorithm-based prediction model showed the best performance among these models.…”

“…And there are already studies that have been conducted in this area with good results. [12][13][14] Therefore, in this study, we aim to build a prediction model to evaluate the risk of BM in PCa patients based on machine learning techniques, and develop a web-based predictor that can be easily manipulated by physicians and patients. This study may provide some help for clinicians to make personalized decisions for the treatment of patients with PCa BM.…”

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“…Currently, considering the complexity and hugeness of medical data, machine learning algorithms have critical application value in assisting disease diagnosis and predicting clinical outcomes ( 18 , 19 ). Liu et al established an RF model using machine learning to accurately predict the risk of bone metastasis in thyroid cancer patients ( 20 ). Using machine learning and comparing six machine learning algorithms, Zhu et al finally established an XGBoost model with the best performance in predicting the occurrence of central lymph node metastasis for papillary thyroid cancer patients, helping patients better determine the scope of surgery ( 21 ).…”