Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants
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...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Cancer Reports |
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| Online Access: | https://doi.org/10.1002/cnr2.70071 |
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| author | Sakhr Alshwayyat Haya Kamal Owais Ghammaz Tala Abdulsalam Alshwayyat Mustafa Alshwayyat Ramez M. Odat Hamdah Hanifa Wafa Asha Nesreen A. Saadeh |
| author_facet | Sakhr Alshwayyat Haya Kamal Owais Ghammaz Tala Abdulsalam Alshwayyat Mustafa Alshwayyat Ramez M. Odat Hamdah Hanifa Wafa Asha Nesreen A. Saadeh |
| author_sort | Sakhr Alshwayyat |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d672e2452381403083c90843c30fb02a |
| institution | Kabale University |
| issn | 2573-8348 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Cancer Reports |
| spelling | doaj-art-d672e2452381403083c90843c30fb02a2024-12-30T06:30:32ZengWileyCancer Reports2573-83482024-12-01712n/an/a10.1002/cnr2.70071Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma VariantsSakhr Alshwayyat0Haya Kamal1Owais Ghammaz2Tala Abdulsalam Alshwayyat3Mustafa Alshwayyat4Ramez M. Odat5Hamdah Hanifa6Wafa Asha7Nesreen A. Saadeh8Research Associate King Hussein Cancer Center Amman JordanFaculty of Medicine Jordan University of Science & Technology Irbid JordanFaculty of Medicine Jordan University of Science & Technology Irbid JordanFaculty of Medicine Jordan University of Science & Technology Irbid JordanFaculty of Medicine Jordan University of Science & Technology Irbid JordanFaculty of Medicine Jordan University of Science & Technology Irbid JordanFaculty of Medicine University of Kalamoon Al‐Nabk SyriaDepartment of Radiation Oncology King Hussein Cancer Center Amman JordanInternal Medicine Department Jordan University of Science and Technology Irbid JordanABSTRACT 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.https://doi.org/10.1002/cnr2.70071clinical decision‐makingmachine learningpapillary thyroid cancerprognosissurvival analysistreatment outcome |
| spellingShingle | Sakhr Alshwayyat Haya Kamal Owais Ghammaz Tala Abdulsalam Alshwayyat Mustafa Alshwayyat Ramez M. Odat Hamdah Hanifa Wafa Asha Nesreen A. Saadeh Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants Cancer Reports clinical decision‐making machine learning papillary thyroid cancer prognosis survival analysis treatment outcome |
| title | Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants |
| title_full | Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants |
| title_fullStr | Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants |
| title_full_unstemmed | Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants |
| title_short | Prognostic Models Using Machine Learning Algorithms and Treatment Outcomes of Papillary Thyroid Carcinoma Variants |
| title_sort | prognostic models using machine learning algorithms and treatment outcomes of papillary thyroid carcinoma variants |
| topic | clinical decision‐making machine learning papillary thyroid cancer prognosis survival analysis treatment outcome |
| url | https://doi.org/10.1002/cnr2.70071 |
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