Abstract
Simple SummaryThyroid cancer is one of the most common cancers worldwide, and papillary thyroid carcinoma (PTC) comprises over 80% of all thyroid cancers. About 30% of patients with PTC have multifocality, which is useful for recurrence prediction. However, recent studies have suggested that the number of tumors, total tumor diameter, and bilaterality are more powerful predictors of recurrence than multifocality. Herein, we evaluated the effect of these factors on the predictability of recurrence in patients with PTC. Our study of 1288 patients confirmed that the number of tumors, total tumor diameter, and bilaterality could be independent predictors of recurrence even though they did not offer better predictability of recurrence than the prediction model using multifocality. Therefore, a simpler, multifocality-based prediction model would be sufficient for predicting recurrence in patients with PTC.Multifocality in papillary thyroid carcinoma (PTC) increases the risk of recurrence. Some recent studies have suggested that multifocality-related parameters, such as the number of tumor foci, total tumor diameter (TTD), and bilaterality, are more useful for predicting recurrence than multifocality. However, it is still unclear if these factors can improve the accuracy of the recurrence prediction model. Between 2012 and 2019, 1288 patients with PTC underwent total thyroidectomy at Ewha Womans University Medical Center. The 5-year disease-free survival rate was 91.2% in patients with >3 tumor foci, 95.1% with 3 foci, and 97.6% with 2 foci; conversely, those with a unifocal tumor showed a 5-year recurrence-free survival rate of 98.0%. Cox proportional hazards analysis indicated that the number of tumor foci (HR for >3 foci, 3.214; HR for 3 foci, 2.473), bilaterality (HR, 2.530), or TTD (HR for >3 cm, 5.359; HR for 2–3 cm, 3.584) could be an independent predictor of recurrence. However, models using the number of tumor foci, bilaterality, and TTD did not show better overall predictability of recurrence than models based on multifocality. In conclusion, a simpler prediction model based on multifocality may be sufficient.
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