摘要
Abstract
Thyroid cancer,the most common malignancy of the endocrine system,is undergoing a profound transformation in precision diagnosis and treatment driven jointly by artificial intelligence(AI)and multi-omics technologies.Confronted with longstanding challenges,including the strong subjectivity of ultrasound interpretation,diagnostic uncertainty in cytology,difficulty in preoperative differentiation of follicular neoplasms,and the lack of precise indicators for recurrence risk assessment,this field is gradually shifting from a traditional paradigm reliant on a single information dimension and empirical judgment toward a data-driven,multidimensional integrative approach to precision diagnosis and treatment.First,leveraging the advantages of deep learning and computer vision,AI technologies have significantly improved the accuracy and efficiency of intelligent ultrasound image interpretation,shifting nodule malignancy differentiation from experience-dominated practice toward AI-assisted decision-making.Second,multi-omics technologies such as genomics and proteomics have systematically revealed key driver genes and molecular biomarkers associated with thyroid nodules,providing important supplementary information for the precise classification of indeterminate nodules.Meanwhile,AI models integrating multilevel information from genomics,proteomics,and other omics layers have demonstrated distinct advantages in diagnostic performance and recurrence risk prediction,offering new insights for individualized treatment decision-making and continuous patient monitoring.Overall,research on AI-driven multi-omics approaches in thyroid nodules has progressed from isolated technical exploration toward multidimensional integration aimed at clinical translation.关键词
人工智能/多组学/甲状腺结节/精准诊疗Key words
Artificial intelligence(AI)/Multi-omics/Thyroid nodule/Precision diagnosis and treatment分类
医药卫生
