Ultrasound Risk Stratification of Autonomously Functioning Thyroid Nodules: Cine Loop Video Sequences Versus Static Image Captures.

Background/Objectives: Autonomously functioning thyroid nodules (AFTNs) are most frequently diagnosed as benign. However, they show high ratings in ultrasound (US) risk stratification systems (RSSs) that utilize the current clinical standard methodology of conventional static image capture (SIC) documentation. The objective of this study was to evaluate the RSS ratings and respective fine needle cytology (FNC) recommendations of cine loop (CL) video sequences in comparison to SIC. Methods: 407 patients with 424 AFTNs were enrolled in this unicentric, retrospective study between 11/2015 and 11/2023. Recorded US CL and SIC were analyzed lesion-wise and compared regarding US features, Kwak and ACR TIRADS, ACR FNC recommendations, as well as assessment difficulties and artifacts. Statistical analyses were conducted using the Chi² test and Spearman's correlation coefficient in SPSS software. p-values < 0.05 were considered significant. Results: Strong to very strong correlations were observed for all US features, RSS ratings, and ACR FNC recommendations (Spearman's correlation: each p < 0.001), comparing CL and SIC. For >60% of the AFTNs, ACR FNC recommendation was given. Kwak TIRADS were more consistent with the benign nature of AFTNs than the ACR ratings. CL captured significantly more "echogenic foci" than SIC (Chi²: p < 0.001). Artifacts (poor image quality, acoustic shadowing, sagittal incompletely displayed AFTN) were significantly more common on CL, affecting ~40% of AFTNs, compared to ~15% on SIC (Chi²: each p < 0.05). Weak correlation was observed for assessment confidence between CL and SIC, with SIC outperforming CL (Spearman's correlation: each p < 0.001). Conclusions: A strong correlation was identified between CL and SIC in terms of RSS ratings and ACR FNC recommendations. Kwak is a superior representative of the benign character of AFTNs than ACR. However, CL provided more detailed information while being associated with decreased observer confidence and more artifacts. Specific operator training and technical improvements, including AI implementation, could improve image quality in future.