Artificial intelligence; Machine learning; Recurrence; Thyroid cancer; Otorhinolaryngology; General Medicine
Abstract :
[en] [en] PURPOSE: The objective of this study was to train machine learning models for predicting the likelihood of recurrence in patients diagnosed with well-differentiated thyroid cancer. While thyroid cancer mortality remains low, the risk of recurrence is a significant concern. Identifying individual patient recurrence risk is crucial for guiding subsequent management and follow-ups.
METHODS: In this prospective study, a cohort of 383 patients was observed for a minimum duration of 10 years within a 15-year timeframe. Thirteen clinicopathologic features were assessed to predict recurrence potential. Classic (K-nearest neighbors, support vector machines (SVM), tree-based models) and artificial neural networks (ANN) were trained on three distinct combinations of features: a data set with all features excluding American Thyroid Association (ATA) risk score (12 features), another with ATA risk alone, and a third with all features combined (13 features). 283 patients were allocated for the training process, and 100 patients were reserved for the validation of stage.
RESULTS: The patients' mean age was 40.87 ± 15.13 years, with a majority being female (81%). When using the full data set for training, the models showed the following sensitivity, specificity and AUC, respectively: SVM (99.33%, 97.14%, 99.71), K-nearest neighbors (83%, 97.14%, 98.44), Decision Tree (87%, 100%, 99.35), Random Forest (99.66%, 94.28%, 99.38), ANN (96.6%, 95.71%, 99.64). Eliminating ATA risk data increased models specificity but decreased sensitivity. Conversely, training exclusively on ATA risk data had the opposite effect.
CONCLUSIONS: Machine learning models, including classical and neural networks, efficiently stratify the risk of recurrence in patients with well-differentiated thyroid cancer. This can aid in tailoring treatment intensity and determining appropriate follow-up intervals.
Disciplines :
Otolaryngology
Author, co-author :
Borzooei, Shiva; Department of Endocrinology, Faculty of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran
Briganti, Giovanni ; Université de Liège - ULiège > Département des sciences cliniques > Santé digitale ; Chair of AI and Digital Medicine, Faculty of Medicine, University of Mons, Mons, France
Golparian, Mitra; Hamadan University of Medical Sciences, Pajoohesh Blvd., Hamadan, Iran
Lechien, Jerome R; Department of Otolaryngology-Head Neck Surgery, Elsan Hospital, Paris, France
Tarokhian, Aidin ; Hamadan University of Medical Sciences, Pajoohesh Blvd., Hamadan, Iran. tarokhianaidin@gmail.com
Language :
English
Title :
Machine learning for risk stratification of thyroid cancer patients: a 15-year cohort study.
Publication date :
30 October 2023
Journal title :
European Archives of Oto-Rhino-Laryngology
ISSN :
0937-4477
eISSN :
1434-4726
Publisher :
Springer Science and Business Media Deutschland GmbH, Germany
Powers AE, Marcadis AR, Lee M, Morris LGT, Marti JL (2019) Changes in trends in thyroid cancer incidence in the United States, 1992 to 2016. JAMA 322(24):2440–2441. 10.1001/jama.2019.18528 DOI: 10.1001/jama.2019.18528
Aschebrook-Kilfoy B, Kaplan EL, Chiu BC-H, Angelos P, Grogan RH (2013) The acceleration in papillary thyroid cancer incidence rates is similar among racial and ethnic groups in the United States. Ann Surg Oncol 20:2746–2753 DOI: 10.1245/s10434-013-2892-y
Li M, Brito JP, Vaccarella S (2020) Long-term declines of thyroid cancer mortality: an international age–period–cohort analysis. Thyroid 30(6):838–846 DOI: 10.1089/thy.2019.0684
Tuttle RM, Alzahrani AS (2019) Risk stratification in differentiated thyroid cancer: from detection to final follow-up. J Clin Endocrinol Metab 104(9):4087–4100 DOI: 10.1210/jc.2019-00177
Luster M, Clarke S, Dietlein M, Lassmann M, Lind P, Oyen W et al (2008) Guidelines for radioiodine therapy of differentiated thyroid cancer. Eur J Nucl Med Mol Imaging 35:1941–1959 DOI: 10.1007/s00259-008-0883-1
Lee J, Lee SG, Kim K, Yim SH, Ryu H, Lee CR et al (2019) Clinical value of lymph node ratio integration with the 8th edition of the UICC TNM classification and 2015 ATA risk stratification systems for recurrence prediction in papillary thyroid cancer. Sci Rep 9(1):13361 DOI: 10.1038/s41598-019-50069-4
Ouyang F-s, Guo B-l, Ouyang L-z, Liu Z-w, Lin S-j, Meng W et al (2019) Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules. Eur J Radiol 113:251–257 DOI: 10.1016/j.ejrad.2019.02.029
Li L-R, Du B, Liu H-Q, Chen C (2021) Artificial intelligence for personalized medicine in thyroid cancer: current status and future perspectives. Front Oncol 10:604051 DOI: 10.3389/fonc.2020.604051
Verburg F, Reiners C (2019) Sonographic diagnosis of thyroid cancer with support of AI. Nat Rev Endocrinol 15(6):319–321 DOI: 10.1038/s41574-019-0204-8
Yoon J, Lee E, Koo JS, Yoon JH, Nam K-H, Lee J et al (2020) Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer. PLoS ONE 15(11):e0242806 DOI: 10.1371/journal.pone.0242806
Schlumberger M, Leboulleux S (2021) Current practice in patients with differentiated thyroid cancer. Nat Rev Endocrinol 17(3):176–188 DOI: 10.1038/s41574-020-00448-z
Bisong E, Bisong E (2019) Introduction to Scikit-learn. Building machine learning and deep learning models on Google cloud platform: a comprehensive guide for beginners, 1st edn. Apress, Ottawa, pp 215–29
Imambi S, Prakash KB, Kanagachidambaresan G (2021) PyTorch. Programming with TensorFlow: solution for edge computing applications, Springer, Cham, pp 87–104
Bisong E, Bisong E (2019) Matplotlib and Seaborn. Building machine learning and deep learning models on google cloud platform: a comprehensive guide for beginners, 1st edn. Apress, Ottawa, pp 151-167
Yu L, Zhou R, Chen R, Lai KK (2022) Missing data preprocessing in credit classification: One-hot encoding or imputation? Emerg Mark Financ Trade 58(2):472–482 DOI: 10.1080/1540496X.2020.1825935
Yue S, Li P, Hao P (2003) SVM classification: Its contents and challenges. Applied Mathematics-A Journal of Chinese Universities 18:332–342 DOI: 10.1007/s11766-003-0059-5
Clark LA, Pregibon D (2017) Tree-based models. In: Statistical models in S. Routledge, pp 377–419.
Taunk K, De S, Verma S, Swetapadma A (2019) A brief review of nearest neighbor algorithm for learning and classification. In: 2019 International Conference on intelligent computing and control systems (ICCS): IEEE; pp 1255–60
Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380(14):1347–1358 DOI: 10.1056/NEJMra1814259
Tuttle RM, Tala H, Shah J, Leboeuf R, Ghossein R, Gonen M et al (2010) Estimating risk of recurrence in differentiated thyroid cancer after total thyroidectomy and radioactive iodine remnant ablation: using response to therapy variables to modify the initial risk estimates predicted by the new American Thyroid Association staging system. Thyroid 20(12):1341–1349 DOI: 10.1089/thy.2010.0178
Halevy A, Norvig P, Pereira F (2009) The unreasonable effectiveness of data. IEEE Intell Syst 24(2):8–12 DOI: 10.1109/MIS.2009.36
Anguita D, Ghio A, Greco N, Oneto L, Ridella S (2010) Model selection for support vector machines: advantages and disadvantages of the machine learning theory. In: The 2010 International Joint Conference on neural networks (IJCNN): IEEE, pp 1–8
El Haji H, Souadka A, Patel BN, Sbihi N, Ramasamy G, Patel BK et al (2023) Evolution of breast cancer recurrence risk prediction: a systematic review of statistical and machine learning-based models. JCO Clin Cancer Inform 7:e2300049 DOI: 10.1200/CCI.23.00049
Mazaki J, Katsumata K, Ohno Y, Udo R, Tago T, Kasahara K et al (2021) A novel prediction model for colon cancer recurrence using auto-artificial intelligence. Anticancer Res 41(9):4629–4636 DOI: 10.21873/anticanres.15276
Lim H, Devesa SS, Sosa JA, Check D, Kitahara CM (2017) Trends in thyroid cancer incidence and mortality in the United States, 1974–2013. JAMA 317(13):1338–1348. 10.1001/jama.2017.2719 DOI: 10.1001/jama.2017.2719
Seib CD, Sosa JA (2019) Evolving understanding of the epidemiology of thyroid cancer. Endocrinol Metab Clin N Am 48(1):23–35. 10.1016/j.ecl.2018.10.002 DOI: 10.1016/j.ecl.2018.10.002
Kelly A, Barres B, Kwiatkowski F, Batisse-Lignier M, Aubert B, Valla C et al (2019) Age, thyroglobulin levels and ATA risk stratification predict 10-year survival rate of differentiated thyroid cancer patients. PLoS ONE 14(8):e0221298. 10.1371/journal.pone.0221298 DOI: 10.1371/journal.pone.0221298
Wu J, Hu XY, Ghaznavi S, Kinnear S, Symonds CJ, Grundy P et al (2022) The prospective implementation of the 2015 ATA guidelines and modified ATA recurrence risk stratification system for treatment of differentiated thyroid cancer in a canadian tertiary care referral setting. Thyroid 32(12):1509–1518. 10.1089/thy.2022.0055 DOI: 10.1089/thy.2022.0055
