Cancer reports (Hoboken, N.J.) · 2024
Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants.
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Alshwayyat Sakhr, Kamal Haya, Ghammaz Owais, Alshwayyat Tala Abdulsalam, Alshwayyat Mustafa, Odat Ramez M, Hanifa Hamdah, Asha Wafa, Saadeh Nesreen A
Аннотация
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.
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.