Efficient computed tomography-based image segmentation for predicting lateral cervical lymph node metastasis in papillary thyroid carcinoma

Purpose: Papillary thyroid carcinoma (PTC) is a common thyroid cancer, and accurate preoperative assessment of lateral cervical lymph node metastasis is critical for surgical planning. Current methods are often subjective and prone to misdiagnosis. This study aims to improve the accuracy of metastasis evaluation using a deep learning-based segmentation method on enhanced computed tomography (CT) images. Approach: We propose a YOLOv8-based deep learning model integrated with a deformable self-attention module to enhance metastatic lymph node segmentation. The model was trained on a large dataset of pathology-confirmed CT images from PTC patients. Results: The model demonstrated diagnostic performance comparable to experienced physicians, with high precision in identifying metastatic nodes. The deformable self-attention module improved segmentation accuracy, with strong sensitivity and specificity. Conclusion: This deep learning approach improves the accuracy of preoperative assessment for lateral cervical lymph node metastasis in PTC patients, aiding surgical planning, reducing misdiagnosis, and lowering medical costs. It shows promise for enhancing patient outcomes in PTC management.