Predicting overall survival in anaplastic thyroid cancer using machine learning approaches.

PURPOSE

Anaplastic thyroid carcinoma (ATC) is a highly aggressive and lethal thyroid cancer subtype with a poor prognosis. Recent advancements in machine learning (ML) have the potential to improve survival predictions. This study aimed to develop and validate ML models using the SEER database to predict 3-month, 6-month, and 12-month (overall survival) OS in ATC patients.

METHODS

Clinical and demographic data for patients with ATC from the SEER database (2004-2015) were utilized. Five ML algorithms-AdaBoost, support vector machines, gradient boosting classifiers, random forests, and naive Bayes-were evaluated. The data were split into training and testing sets (7:3 ratio), and the models were tuned using fivefold cross-validation. Model performance was assessed using the concordance index (C-index) and Brier score, with 95% confidence intervals reported.

RESULTS

The gradient boosting model achieved the greatest performance for 3-month survival (C-index: 0.8197, 95% CI 0.7682-0.8689; Brier score: 0.1802), and the AdaBoost model achieved the greatest performance in 6-month survival (C-index: 0.8473, 95% CI 0.7979-0.8933; Brier score: 0.1775). The SVC model showed superior performance for 12-month survival (C-index: 0.8347, 95% CI 0.7866-0.8816; Brier score: 0.1476). Using SHAP with a gradient boosting model, the top five features affecting 6-month OS were identified: surgery, the presence of stage IVC, radiation, chemotherapy, and tumor size. Treatment improved survival, while higher stages reduced survival, with smaller tumors generally linked to better outcomes.

CONCLUSION

ML algorithms can accurately predict short-term survival in ATC patients. These models can potentially guide clinical decision-making and individualized treatment strategies.