Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants

Abstract

ABSTRACT Background Hürthle cell (HCC) and columnar cell variants (CCV) are rare subtypes of thyroid cancer. Aims This study used machine learning (ML) to evaluate treatment effectiveness and develop prognostic models. Methods Chi‐square tests, Kaplan–Meier curves, log‐rank tests, and Cox regression were used. Five ML algorithms constructed prognostic models predicting 5‐year survival, validated using the AUC of the ROC curve. Results Among 3690 patients, 3180 had CCV and 510 had HCC. ML models showed metastasis, surgery + RT, and age were significant factors for HCC, while the N component of TNM, metastasis, and tumor size were significant for CCV. Conclusion This study offers a comprehensive approach for treating and assessing prognosis in PTC variants. The ML models developed offer practical tools for personalized clinical decision‐making.

Keywords

• clinical decision‐making
• machine learning
• papillary thyroid cancer
• prognosis
• survival analysis
• treatment outcome