World journal of surgery · 2025
A Pathology-Based Risk Stratification Model for Predicting Occult Lesions in Papillary Thyroid Carcinoma Patients: Validation Using 5-Year Thermal Ablation Outcome Data.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhao Han-Xiao, Niu Yun, Wei Ying, Zhao Zhen-Long, Hao Ying, Xu Chun-Yan, Peng Li-Li, Li Yan, Wu Jie, Cao Shi-Liang, Yu Na, Yu Ming-An
Аннотация
BACKGROUND: Thermal ablation (TA) is a minimally invasive alternative for patients with papillary thyroid carcinoma (PTC). However, sonographically occult lesions may lead to new tumor development during follow-up monitoring. This study involved developing and validating a prediction model for identifying patients at high risk of new tumors after receiving TA.
METHODS: This study involved the retrospective analysis of the pathological results of patients who received total thyroidectomy between January and June 2023 to identify risk factors for occult lesions. A prediction model was developed on the basis of these risk factors, with patients stratified into risk groups. The model was externally validated in patients who received TA with more than 5 years of follow-up monitoring.
RESULTS: The training cohort included 377 patients (median age 46 years, 76.1% female). Two independent risk factors for occult thyroid lesions were identified: tumor stage (T1a vs. T1b: OR 3.502, p < 0.001; T1a vs. T2: OR 4.124, p = 0.029) and tumor multifocality (OR 4.435, p < 0.001). Patients were stratified into low-risk (< 0.09), medium-risk (0.09-0.29), and high-risk (> 0.29) groups. The prediction model demonstrated good discrimination, with an AUC of 0.717, which remained stable after internal validation (bias-corrected AUC: 0.769). External validation of 290 patients receiving TA (median age 43 years, 72.8% female) with more than 5 years of follow-up monitoring confirmed the effectiveness of the model in predicting new tumor development (AUC: 0.755).
CONCLUSIONS: This risk-stratified prediction model for occult lesions provides an evidence-based tool for clinicians to estimate new tumor risk after receiving TA and to guide individualized follow-up strategies.
METHODS: This study involved the retrospective analysis of the pathological results of patients who received total thyroidectomy between January and June 2023 to identify risk factors for occult lesions. A prediction model was developed on the basis of these risk factors, with patients stratified into risk groups. The model was externally validated in patients who received TA with more than 5 years of follow-up monitoring.
RESULTS: The training cohort included 377 patients (median age 46 years, 76.1% female). Two independent risk factors for occult thyroid lesions were identified: tumor stage (T1a vs. T1b: OR 3.502, p < 0.001; T1a vs. T2: OR 4.124, p = 0.029) and tumor multifocality (OR 4.435, p < 0.001). Patients were stratified into low-risk (< 0.09), medium-risk (0.09-0.29), and high-risk (> 0.29) groups. The prediction model demonstrated good discrimination, with an AUC of 0.717, which remained stable after internal validation (bias-corrected AUC: 0.769). External validation of 290 patients receiving TA (median age 43 years, 72.8% female) with more than 5 years of follow-up monitoring confirmed the effectiveness of the model in predicting new tumor development (AUC: 0.755).
CONCLUSIONS: This risk-stratified prediction model for occult lesions provides an evidence-based tool for clinicians to estimate new tumor risk after receiving TA and to guide individualized follow-up strategies.