Application and Thinking of Deep Learning in Predicting Lateral Cervical Lymph Node Metastasis of Papillary Thyroid Cancer

Abstract

Papillary thyroid carcinoma (PTC) can exhibit lateral neck lymph node metastasis at an early stage. Lateral neck lymph node metastasis is a crucial factor affecting the prognosis of PTC and is an absolute indication for neck lymph node dissection surgery. Additionally, it is a relative contraindication of endoscopic surgery for most medical centers. Therefore, the preoperative identification of lateral neck lymph node metastasis is vital for surgical decision-making and prognosis assessment. Ultrasound, CT, cytology, and clinical features can provide some information on lateral neck lymph node metastasis, but their accuracy does not fully meet clinical needs. Deep learning is a primary method for medical image recognition or feature extraction. In recent years, deep learning-based ultrasound, CT, cytology, conventional clinical parameters, or multimodal models combining these data have been developed and are expected to achieve routine clinical application. With the establishment and sharing of large datasets, automated annotation, algorithm optimization, and resolution of data security issues, deep learning is expected to accurately predict lateral neck lymph node metastasis in PTC. Furthermore, it can be integrated into electronic medical record systems for automated real-time analysis and assist clinical decision-making.

Keywords

• deep learning
• papillary thyroid carcinoma
• lymph node metastasis
• multimodal data