Abstract
Thyroid cancer, one of the most prevalent endocrine malignancies, presents diverse treatment responses among patients, underscoring the necessity for personalized treatment strategies. This study aims to develop and evaluate AI models that can accurately predict the initial treatment response among patients with well-differentiated thyroid cancer. We trained and validated various machine learning and deep learning models using a dataset that comprises demographic and clinicopathological features. This dataset was collected from a retrospective cohort of 383 patients diagnosed with thyroid cancer at a single medical center. Models in all experiments achieved an average accuracy of 72.2%, average precision of 76.2%, average recall of 68%, average F1 score of 70.6%, and average AUC of 80.5%. Multi-layer Perceptron achieved the highest accuracy (85.7%), recall (75%), and F1 score (81.7%) in this study when it was used for predicting binary treatment response using all features, excluding those weakly correlated with the treatment response. However, Naive Bayes attained the highest precision (95.1%) and AUC (88.7%) in this study when it was used for predicting binary treatment response using all features. Models used for predicting binary treatment responses outperformed those used for predicting multi-class treatment responses. AI demonstrates satisfactory performance in predicting the response to thyroid cancer treatment, yet there is room for optimization. Healthcare providers should not solely rely on our models; combining them with other tools is advised until further studies validate their optimal performance. Future research should enhance predictive capabilities by including additional features, exploring alternative models, and utilizing larger and balanced datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nguyen, Q.T., Lee, E.J., Huang, M.G., Park, Y.I., Khullar, A., Plodkowski, R.A.: Diagnosis and treatment of patients with thyroid cancer. Am. Health Drug Benefits 8(1), 30–40 (2015). PMID: 25964831
Pizzato, M., Li, M., Vignat, J., Laversanne, M., Singh, D., La Vecchia, C., et al.: The epidemiological landscape of thyroid cancer worldwide: GLOBOCAN estimates for incidence and mortality rates in 2020. The Lancet Diabetes & Endocrinology. 2022 2022/04/01/, 10(4), 264–72. https://doi.org/10.1016/S2213-8587(22)00035-3
Haugen, B.R., Alexander, E.K., Bible, K.C., Doherty, G.M., Mandel, S.J., Nikiforov, Y.E., et al.: 2015 American Thyroid Association Management Guidelines for Adult Patients with Thyroid Nodules and Differentiated Thyroid Cancer: The American Thyroid Association Guidelines Task Force on Thyroid Nodules and Differentiated Thyroid Cancer. Thyroid : official journal of the American Thyroid Association. 2016 Jan, 26(1):1–133. PMID: 26462967. https://doi.org/10.1089/thy.2015.0020
Ma, X., Xi, B., Zhang, Y., Zhu, L., Sui, X., Tian, G., et al.: A machine learning-based diagnosis of thyroid cancer using thyroid nodules ultrasound images. Current Bioinform. 15(4), 349–58 (2020). https://doi.org/10.2174/1574893614666191017091959
Bellantuono, L., Tommasi, R., Pantaleo, E., Verri, M., Amoroso, N., Crucitti, P., et al.: An eXplainable Artificial Intelligence analysis of Raman spectra for thyroid cancer diagnosis. Sci. Rep. 13(1), 16590 (2023). https://doi.org/10.1038/s41598-023-43856-7
Zhu, Y.-C., Du, H., Jiang, Q., Zhang, T., Huang, X.-J., Zhang, Y., et al.: Machine Learning Assisted Doppler Features for Enhancing Thyroid Cancer Diagnosis. 41(8), 1961–1974 (2022). https://doi.org/10.1002/jum.15873
Borzooei, S., Briganti, G., Golparian, M., Lechien, J.R., Tarokhian, A.: Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study. European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS): affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery. 2023 Oct 30. PMID: 37902840. https://doi.org/10.1007/s00405-023-08299-w
Park, Y.M., Lee, B.-J.: Machine learning-based prediction model using clinico-pathologic factors for papillary thyroid carcinoma recurrence. Sci. Rep. 11(1), 4948 (2021). https://doi.org/10.1038/s41598-021-84504-2
Kil, J., Kim, K.G., Kim, Y.J., Koo, H.R., Park, J.S.: Deep learning in thyroid ultrasonography to predict tumor recurrence in thyroid cancers. Taehan Yongsang Uihakhoe chi. 81(5):1164–74. PMID, 36238043 (2020). https://doi.org/10.3348/jksr.2019.0147
Li, Y., Wu, F., Ge, W., Zhang, Y., Hu, Y., Zhao, L., et al.: Risk stratification of papillary thyroid cancers using multidimensional machine learning. Int. J. Surg. 110(1) (2024)
Grani, G., Gentili, M., Siciliano, F., Albano, D., Zilioli, V., Morelli, S., et al.: A data-driven approach to refine predictions of differentiated thyroid cancer outcomes: a prospective multicenter study. J. Clin. Endocrinol. Metabolism 108(8), 1921–8 (2023). PMID: 36795619. https://doi.org/10.1210/clinem/dgad075
Lubin, D.J., Tsetse, C., Khorasani, M.S., Allahyari, M., McGrath, M.: Clinical predictors of I-131 therapy failure in differentiated thyroid cancer by machine learning: a single-center experience. World J. Nuclear Med. 20(3), 253–9 (2021). PMID: 34703393. https://doi.org/10.4103/wjnm.WJNM_104_20
Sa, R., Yang, T., Zhang, Z., Guan, F.: Random Forest for Predicting Treatment Response to Radioiodine and Thyrotropin Suppression Therapy in Patients With Differentiated Thyroid Cancer But Without Structural Disease. The Oncologist 29(1), e68-e80. PMID: 37669005 (2024). https://doi.org/10.1093/oncolo/oyad252
Borzooei, S., Tarokhian, A.: Differentiated Thyroid Cancer Recurrence. 2023, 09 Mar 2023. https://archive.ics.uci.edu/dataset/915/differentiated+thyroid+cancer+recurrence
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors have no competing interests to declare that are relevant to the content of this article.
Appendix
The appendix can be accessed through the following link: https://github.com/DrAlaaalzoubi/Artificial-Intelligence-for-Predicting-Responses-to-Thyroid-Cancer-Treatment/tree/main.
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Abd-Alrazaq, A. et al. (2024). Artificial Intelligence for Predicting Responses to Thyroid Cancer Treatment. In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14976. Springer, Cham. https://doi.org/10.1007/978-3-031-67285-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-67285-9_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-67284-2
Online ISBN: 978-3-031-67285-9
eBook Packages: Computer ScienceComputer Science (R0)