Machine learning algorithms for identifying contralateral central lymph node metastasis in unilateral cN0 papillary thyroid cancer

• Published in: Frontiers in endocrinology (Lausanne) 2024, Vol.15, p.1385324-1385324
• Main Authors: Ren, Anwen, Zhu, Jiaqing, Wu, Zhenghao, Ming, Jie, Ruan, Shengnan, Xu, Ming, Huang, Tao
• Format: Article
• Language: English
• Subjects: Adult; Algorithms; contralateral central lymph node metastasis; Female; Humans; Lymph Nodes - pathology; Lymph Nodes - surgery; Lymphatic Metastasis - pathology; Machine Learning; Male; Middle Aged; papillary thyroid carcinoma; prediction model; Retrospective Studies; risk factors; Thyroid Cancer, Papillary - pathology; Thyroid Cancer, Papillary - surgery; Thyroid Neoplasms - pathology; Thyroid Neoplasms - surgery
• ISSN: 1664-2392; 1664-2392
• DOI: 10.3389/fendo.2024.1385324

The incidence of thyroid cancer is growing fast and surgery is the most significant treatment of it. For patients with unilateral cN0 papillary thyroid cancer whether to dissect contralateral central lymph node is still under debating. Here, we aim to provide a machine learning based prediction model of contralateral central lymph node metastasis using demographic and clinical data. 2225 patients with unilateral cN0 papillary thyroid cancer from Wuhan Union Hospital were retrospectively studied. Clinical and pathological features were compared between patients with contralateral central lymph node metastasis and without. Six machine learning models were constructed based on these patients and compared using accuracy, sensitivity, specificity, area under the receiver operating characteristic and decision curve analysis. The selected models were then verified using data from Differentiated Thyroid Cancer in China study. All statistical analysis and model construction were performed by R software. Male, maximum diameter larger than 1cm, multifocality, ipsilateral central lymph node metastasis and younger than 50 years were independent risk factors of contralateral central lymph node metastasis. Random forest model performed better than others, and were verified in external validation cohort. A web calculator was constructed. Gender, maximum diameter, multifocality, ipsilateral central lymph node metastasis and age should be considered for contralateral central lymph node dissection. The web calculator based on random forest model may be helpful in clinical decision.