Details

Autor(en) / Beteiligte

• Lin, Si‐Ying
• Li, Meng‐Yao
• Zhou, Chi‐Peng
• Ao, Wei
• Huang, Wen‐Yu
• Wang, Si‐Si
• Yu, Jia‐Fan
• Tang, Zi‐Han
• Abdelhamid Ahmed, Amr H.
• Wang, Ting‐Yi
• Wang, Zhi‐hong
• Hua, Surong
• Randolph, Gregory W.
• Zhao, Wen‐Xin
• Wang, Bo

Titel

Accurate preoperative prediction of nodal metastasis in papillary thyroid microcarcinoma: Towards optimal management of patients

Ist Teil von

• Head & neck, 2024-05, Vol.46 (5), p.1009-1019

Ort / Verlag

Hoboken, USA: John Wiley & Sons, Inc

Erscheinungsjahr

2024

Quelle

Wiley-Blackwell Journals

Beschreibungen/Notizen

• Objective To enhance the accuracy in predicting lymph node metastasis (LNM) preoperatively in patients with papillary thyroid microcarcinoma (PTMC), refining the “low‐risk” classification for tailored treatment strategies. Methods This study involves the development and validation of a predictive model using a cohort of 1004 patients with PTMC undergoing thyroidectomy along with central neck dissection. The data was divided into a training cohort (n = 702) and a validation cohort (n = 302). Multivariate logistic regression identified independent LNM predictors in PTMC, leading to the construction of a predictive nomogram model. The model's performance was assessed through ROC analysis, calibration curve analysis, and decision curve analysis. Results Identified LNM predictors in PTMC included age, tumor maximum diameter, nodule‐capsule distance, capsular contact length, bilateral suspicious lesions, absence of the lymphatic hilum, microcalcification, and sex. Especially, tumors larger than 7 mm, nodules closer to the capsule (less than 3 mm), and longer capsular contact lengths (more than 1 mm) showed higher LNM rates. The model exhibited AUCs of 0.733 and 0.771 in the training and validation cohorts respectively, alongside superior calibration and clinical utility. Conclusion This study proposes and substantiates a preoperative predictive model for LNM in patients with PTMC, honing the precision of “low‐risk” categorization. This model furnishes clinicians with an invaluable tool for individualized treatment approach, ensuring better management of patients who might be proposed observation or ablative options in the absence of such predictive information.