Open AccessMachine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer
Author(s) -
Liu WenCai,
Li ZhiQiang,
Luo ZhiWen,
Liao WeiJie,
Liu ZhiLi,
Liu JiaMing
Publication year - 2021
Publication title -
cancer medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.403
H-Index - 53
ISSN - 2045-7634
DOI - 10.1002/cam4.3776
Subject(s) - medicine , machine learning , receiver operating characteristic , predictive modelling , bone metastasis , random forest , artificial intelligence , stage (stratigraphy) , recall rate , precision and recall , area under curve , metastasis , recall , oncology , cancer , computer science , paleontology , pharmacokinetics , biology , linguistics , philosophy
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.