Integrated gene profiling of fine‐needle aspiration sample improves lymph node metastasis risk stratification for thyroid cancer

Abstract

Abstract Background Lymph node metastasis risk stratification is crucial for the surgical decision‐making of thyroid cancer. This study investigated whether the integrated gene profiling (combining expression, SNV, fusion) of Fine‐Needle Aspiration (FNA) samples can improve the prediction of lymph node metastasis in patients with papillary thyroid cancer. Methods In this retrospective cohort study, patients with papillary thyroid cancer who went through thyroidectomy and central lymph node dissection were included. Multi‐omics data of FNA samples were assessed by an integrated array. To predict lymph node metastasis, we built models using gene expressions or mutations (SNV and fusion) only and an Integrated Risk Stratification (IRS) model combining genetic and clinical information. Blinded histopathology served as the reference standard. ROC curve and decision curve analysis was applied to evaluate the predictive models. Results One hundred and thirty two patients with pathologically confirmed papillary thyroid cancer were included between 2016–2017. The IRS model demonstrated greater performance [AUC = 0.87 (0.80–0.94)] than either expression classifier [AUC = 0.67 (0.61–0.74)], mutation classifier [AUC = 0.61 (0.55–0.67)] or TIRADS score [AUC = 0.68 (0.62–0.74)] with statistical significance (p 1 cm, AUC = 0.88 (0.79–0.97)] and small nodule [≤1 cm, AUC = 0.84 (0.74–0.93)] subgroups. The genetic risk factor showed independent predictive value (OR = 10.3, 95% CI:1.1–105.3) of lymph node metastasis in addition to the preoperative clinical information, including TIRADS grade, age, and nodule size. Conclusion The integrated gene profiling of FNA samples and the IRS model developed by the machine‐learning method significantly improve the risk stratification of thyroid cancer, thus helping make wise decisions and reducing unnecessary extensive surgeries.

Keywords

• biomarker
• fine‐needle aspiration
• machine learning
• multi‐omics
• risk stratification
• thyroid cancer