Thyroid carcinoma is the most common endocrine carcinoma, constituting approximately 3% of all carcinoma cases diagnosed worldwide as of 2020. [1] In particular, the papillary thyroid carcinoma (PTC) accounts for 84-88% of all thyroid carcinoma cases. [2][3][4][5][6] According to the surveillance, epidemiology, and end results (SEER) program, [1,6,7] the incidence and recurrence rates of PTC have steadily increased in the past decade. Some studies reported that recurrence occurred in approximately 8-28% of PTC patients, [3,8,9] which indicates that PTC frequently invades the perithyroid tissues and cervical lymph nodes (LNs). [3,6,8,9] For this reason, PTC patients require additional examinations and operations, which results in extensive morbidity and medical costs compared to those of the first operation. Therefore, early prediction of PTC recurrence can prevent the spread of serious carcinoma and reduce the socioeconomic burden on patients. [5] In the medical field, various clinical indicators have been suggested as predictors of PTC recurrence. According to the risk of structural disease recurrence of the 2015 American Thyroid Association (ATA) guidelines, the histological types of tumor, tumor size, tumor multiplicity, extrathyroidal extension (ETE), extranodal extension (ENE), and mutation of genes (BRAF, TERT) are crucial for estimating PTC recurrence. [10] Moreover, some studies considered additional features such as the demographics, thyroid function tests (TFTs), type of surgery, and I-131 radioactive therapy. [9][10][11][12][13][14][15] However, their goal was to individually analyze the association between PTC recurrence and each feature. Since no unified predictor of PTC recurrence exists, [16][17][18][19] it is necessary to develop an artificial intelligence (AI) model that can analyze these features in an integrated manner, similar to clinicians who make decisions based on diverse examinations.Recently, research on predicting thyroid carcinoma recurrence using machine learning (ML) and deep learning (DL) has been increasing. Zhu et al. [20] developed an online recurrence risk calculator based on the extreme gradient boosting (XGBoost) algorithm. Park et al. [21] categorized the number of metastatic LNs and lymph node ratio (LNR) by considering the prognostic significance of clinicopathologic features, and predicted PTC recurrence through ML algorithms using these features. To predict thyroid carcinoma recurrence using computed tomography
