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Minerva Endocrinology 2021 Sep 16

DOI: 10.23736/S2724-6507.21.03393-9

Copyright © 2021 EDIZIONI MINERVA MEDICA

language: English

A predictive model and survival analysis for local recurrence in differentiated thyroid carcinoma

PeiPei YANG 1, JiuPing HUANG 1, 2, ZhenDong WANG 3, LinXue QIAN 1

1 Department of Ultrasound, Beijing Friendship Hospital, Capital Medical University, Beijing, China; 2 Department of Ultrasound, Peking University Third Hospital, Haidian District, Beijing, China; 3 Department of Interventional Ultrasound, First Medical Center of Chinese People's Liberation Army, General Hospital, Beijing, China



BACKGROUND: Local recurrence (LR) is associated with poor outcome in patients with differentiated thyroid carcinoma (DTC). The aim of this study was to explore potential risk factors for LR and build a predictive model.
METHODS: The medical data of patients who were diagnosed with DTC after initial surgery in three medical centers (2000-2018) were reviewed. Detailed clinicopathologic characteristics of all cases were identified.
RESULTS: Multiple factors, including extrathyroidal extension (ETE), histology, symptoms, multifocality, and tumor diameter, were significantly different between the LR and no evidence of disease groups in univariate and multivariate analysis (P ˂ 0.05). Tumor diameter, symptoms, and ETE made the greatest contributions to prognosis according to decision tree analysis and random forest algorithm. The predictive model constructed from these data achieved 98.7% accuracy of classification. A five-fold cross-validation confirmed that the model has 84.7%-89.7% accuracy of classification. Additionally, symptoms and ETE were independent predictors on survival analysis (P ˂ 0.05).
CONCLUSIONS: This study optimized the weight of risk factors, including tumor diameter, symptoms, ETE, and multifocality, in predicting LR in patients with DTC. Our predictive model provides a strong tool to distinguish between high-risk and low-risk DTC.


KEY WORDS: Recurrence; Thyroid carcinoma; Thyroid nodule; Survival analysis; Decision trees

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