Accelerating the Radiology Learning Curve: How the Parameter-Based I-TIRADS Lexicon Enhances Interpretive Reliability and Learning Curves Across Resident Experience Levels—A Dynamic Video-Based Study

Background/Objectives: Standardizing thyroid nodule assessment remains a major challenge in clinical practice and radiology education. Traditional categorical risk stratification systems (RSSs) often yield high interobserver variability, particularly among trainees. This study evaluated the reproducibility, educational utility, and experience-dependent performance of the parameter-based International Thyroid Imaging Reporting and Data System (I-TIRADS) lexicon compared to established categorical RSSs (ACR, EU, and K-TIRADS). Methods: In this prospective validation study, 105 thyroid nodules stratified across Bethesda categories were evaluated by seven radiologists: one senior expert (as reference standard) and six residents categorized into three distinct experience levels (Junior, Intermediate, Senior). To simulate real-time clinical practice and eliminate 2D static image bias, comprehensive dynamic video clips were utilized for all cases. Interobserver agreement and intergroup consistency were quantified using Fleiss’ and Cohen’s kappa (κ) statistics. Results: Parameter-based I-TIRADS descriptors—specifically composition (κ = 0.510) and direction of growth (κ = 0.498)—demonstrated significantly higher overall reliability compared to final categorical RSS classifications (ACR: κ = 0.220, EU: κ = 0.215, K-TIRADS: κ = 0.196). Crucially, the reproducibility advantage of the I-TIRADS lexicon became markedly more pronounced with advancing clinical experience: senior observers achieved substantial to near-perfect consensus for core descriptors (up to κ = 0.826), whereas agreement for traditional RSS categories remained limited. I-TIRADS facilitated a steeper learning curve, allowing even junior trainees to achieve reliable consensus on objective sonographic parameters. Conclusions: The parameter-based I-TIRADS provides superior interobserver consistency compared to traditional categorical RSSs by reducing subjective evaluative drift, accelerating the radiologic learning curve, and offering a highly reproducible framework for both clinical practice and residency education.