Abstract: Active surveillance(AS) has become one of the management strategies for low-risk papillary thyroid carcinoma(T_1aN₀M₀ papillary thyroid carcinoma,low-risk PTC).The key is to identify patients who are suitable for AS.Conventional Ultrasonography(CUS) is the main evaluation method at present,but it has limitations in detecting indicators such as occult lymph node metastasis.Ultrasonic Elastography(UE) provides a new method for risk assessment by quantifying tissue stiffness,although it still has limitations when used alone.Artificial Intelligence(AI) has shown high diagnostic performance in identifying pathological features including lymph node metastasis.However,most models are established based on high-risk surgical cohorts,leading to low generalizability when applied to AS decision-making for low-risk PTC.Future research should focus on establishing and validating a multi-technique prediction model combining conventional ultrasonography,elastography and AI.Meanwhile,standardized data collection and model transparency can further improve the clinical applicability and generalizability of the model,thus contributing to more accurate identification of low-risk PTC.

Key words: low-risk papillary thyroid carcinoma; active surveillance; ultrasonic elastography; artificial intelligence