Clinical target volume (CTV) delineation from radiotherapy computed tomography (RTCT) images is used to define the treatment areas containing the gross tumor volume (GTV) and/or sub-clinical malignant disease for radiotherapy (RT). High intra-and inter-user variability makes this a particularly difficult task for esophageal cancer. This motivates automated solutions, which is the aim of our work. Because CTV delineation is highly context-dependent-it must encompass the GTV and regional lymph nodes (LNs) while also avoiding excessive exposure to the organs at risk (OARs)-we formulate it as a deep contextual appearance-based problem using encoded spatial contexts of these anatomical structures. This allows the deep network to better learn from and emulate the margin-and appearance-based delineation performed by human physicians. Additionally, we develop domain-specific data augmentation to inject robustness to our system. Finally, we show that a simple 3D progressive holistically nested network (PHNN), which avoids computationally heavy decoding paths while still aggregating features at different levels of context, can outperform more complicated networks. Cross-validated experiments on a dataset of 135 esophageal cancer patients demonstrate that our encoded spatial context approach can produce concrete performance improvements, with an average Dice score of 83.9 ± 5.4% and an average surface distance of 4.2 ± 2.7 mm, representing improvements of 3.8% and 2.4mm, respectively, over the state-of-the-art approach.