Machine learning approaches for predicting differentiated thyroid cancer recurrence using thyroglobulin levels and whole-body scans: a retrospective cohort study

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. The aim of the present study was to evaluate recurrence related factors in thyroid cancer patients using ML algorithms. This retrospective cohort study included patients with differentiated thyroid cancer who, after undergoing total thyroidectomy and ablation with radioactive iodine (RAI) were followed up over a ten-year period. Demographic data, tumor characteristics, and treatment details were extracted from medical records. Six ML algorithms were employed including logistic regression, Naïve Bayes classifier, decision tree, random forest, XGBoost and LightGBM. A total of 355 patients were included (mean age: 41.69 $$\:\pm\:$$ 14.04 years, 84.22% female). Among ML algorithms, XGBoost demonstrated superior predictive performance, achieving an accuracy of 97.66(± 2.34) % and an area under the curve of 0.99. The top predictors were the number of recurrences, first-year stimulated thyroglobulin level, regional node involvement, and first response to treatment, respectively. This study also proposed the use of whole-body scans only for high-risk patients.