Machine learning approaches for prediction of thyroid cancer recurrence using thyroglobulin level, whole body scan

Background- Although thyroid cancer generally has a good prognosis, some patients are prone to recurrence. Multiple factors influence recurrence risk. Machine learning (ML) algorithms offer potential for more accurate and precise prediction models. The aim of the present study is to evaluate recurrence-related factors in thyroid cancer patients using ML algorithms. Methods- This retrospective cohort study included patients with differentiated thyroid cancer referred to a specialized endocrinology clinic, between 2013 and 2023. Demographic data, tumor characteristics, and treatment details were extracted from medical records. Six ML algorithm were employed including logistic regression, Naïve Bayes classifier, decision tree, random forest, XGBoost and LightGBM. Results- A total 355 patients were included (mean age: 41.6914.04 years, 84.22% female). Among ML algorithms, LightGBM demonstrated superior predictive performance, achieving an accuracy of 95.41%, precision of 88.84%, recall of 84.25%, specificity of 97.89%, and an area under the curve of 97.28%. The top five predictors were first-year thyroglobulin level, first response to treatment, age, primary tumor characteristics, and regional lymph nodes involvement, respectively. Conclusion- This study demonstrated that ML algorithms had strong capability to identify thyroid cancer patients at risk of recurrence.