Purpose/Objective(s): Automatic lymph node (LN) delineation models face challenges due to the anatomical and pathological variability of LN across different regions and disease states. Traditional methods require large, annotated datasets to account for all variations. Recently, foundational models have shown promise in developing high-performing models with fewer samples. However, models not specifically designed for LN delineation often yield poor results due to the unique characteristics of LNs. This highlights the need for a specialized LN delineation model to effectively tackle this clinically important and technically complex task.