Purpose/Objective(s): Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate effectiveness of 2.5D approaches (i.e, using 2D network with multi-slice inputs) by leveraging pretrained 2D model weights, and show improved accuracy as compared to 2D or 3D detectors. However, slice-based 2.5D detectors do not explicitly model inter-slice consistency for LN as a 3D object, requiring heuristic post-merging steps to generate final 3D LN instances. In this study, we formulate 3D LN detection as a tracking task and propose LNTracker, a novel LN tracking transformer, that can effectively tackle the challenging LN detection task across major body sections.