甲状腺癌是近年来全球发病率增长最快的恶性肿瘤，已上升为我国女性恶性肿瘤发病率第五位，其中50%以上为1cm以下的甲状腺微小乳头状癌（PTMC）。文献报告甲状腺微小癌颈部淋巴结的转移率10%～60%，主要转移至气管旁中央区淋巴结，因气管遮挡，作为首选的超声术前诊断敏感性仅为10.9%～42.0%。由于甲状腺微小癌淋巴结转移目前尚无有效的检查手段，因此多数患者被动接受了甲状腺全切或大部切除加颈部淋巴结清扫的扩大手术，因此2%-4%的患者导致了甲状旁腺或喉返神经损伤而严重影响了生活质量。本课题拟运用多模态超声技术结合计算机人工智能辅助成像定量分析技术，与组织病理和分子病理对照研究，探索有或无淋巴结转移的甲状腺微小癌的超声多模态综合影像特征及其分子机制，建立人工智能辅助多模态超声对甲状腺微小癌淋巴结转移的预警系统，以提高超声诊断的客观性和准确性，使部分患者免于扩大手术的风险。

Thyroid cancer is one of the fastest growing malignant tumors in recent years. It has become the fifth most common malignant tumor in China, which is mainly caused by papillary thyroid carcinoma (PTC). Due to the improvement of high frequency ultrasound resolution, more and more thyroid cancer less than or equal to 1 cm could be found, which has been more than 50% of the thyroid carcinoma.Reports of papillary thyroid carcinoma cervical lymph node metastasis rate is as high as 10% to 60%, mainly for central lymph node metastasis, preoperative diagnostic sensitivity is only 10.9% ~ 42.0%.There is no warning and clear check means to predict early lymph node metastasis , so the majority of patients with passive accepted a broaden operation, leading to parathyroid and laryngeal nerve injury and seriously affect the quality of life of patients. Therefore this topic intends to use multimodal ultrasound combined with artificial intelligence based computer assisted imaging quantitative analysis technology, and with tissue pathology and molecular pathology, explore the ultrasonographic features of papillary thyroid microcarcinoma with or without lymph node metastasis, establish artificial intelligence based computer aided detected method using ultrasound imaging of thyroid microcarcinoma to predict early lymph node metastasis, improve the objectivity and accuracy of ultrasound diagnosis, make patients avoid from excessive surgical treatment , and the patients who have high risk of thyroid cancer metastasis get early diagnosis and treatment.