Lymph node (LN) assessment is a critical yet very challenging task in the routine clinical workflow of radiology and oncology. Accurate LN analysis is essential for cancer diagnosis, staging and treatment planning. Finding scatteredly distributed, low-contrast clinically relevant LNs in 3D CT is difficult even for experienced physicians under high inter-observer variations. Previous automatic LN detection typically yields limited recall and high false positives (FPs) due to adjacent anatomies with similar image intensities, shapes or textures (vessels, muscles, esophagus, etc.). In this work, we propose a new LN DEtection TRansformer, named LN-DETR, with location debiased query selection and contrastive query learning to enhance the representation ability of LN queries, important to increase the detection sensitivity and reduce FPs or duplicates. We also enhance LN-DETR by adapting an efficient multi-scale 2.5D fusion scheme to incorporate the 3D context. Trained and tested on 3D CT scans of 1067 patients (with 10, 000+ labeled LNs) via combining seven LN datasets from different body parts (neck, chest, and abdomen) and pathologies/cancers, our method significantly improves the performance of previous leading methods by >4 similar to 5% average recall at the same FP rates in both internal and external testing. We further evaluate on the universal lesion detection task using DeepLesion benchmark, and our method achieves the top performance of 88.46% averaged recall, compared with other leading reported results.