A Pathology‐Based Risk Stratification Model for Predicting Occult Lesions in Papillary Thyroid Carcinoma Patients: Validation Using 5‐Year Thermal Ablation Outcome Data

医学 神秘的 危险分层 腹部外科 血管外科 心胸外科 放射科 心脏外科 甲状腺癌 烧蚀 病理 普通外科 外科 内科学 甲状腺 替代医学
作者
Han‐xiao Zhao,Yun Niu,Ying Wei,Zhenlong Zhao,Ying Hao,Chunyan Xu,Li-Li Peng,Yan Li,Jie Wu,Shi-Liang Cao,Na Yu,Ming-An Yu
出处
期刊:World Journal of Surgery [Springer Science+Business Media]
卷期号:49 (9): 2484-2492
标识
DOI:10.1002/wjs.70046
摘要

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.
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