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Challenges, in the context of medical imaging, are valuable in that they allow for a direct comparison of different algorithms designed for a specific radiologic task, with all algorithms abiding by the same set of rules, operating on a common set of images, and being evaluated with a uniform performance assessment paradigm. The variability of system performance based on database composition and subtlety, definition of “truth,” and scoring metric is well-known;1–3 challenges serve to level the differences across these various dimensions. The medical imaging community has hosted a number of successful thoracic imaging challenges that have spanned a wide range of tasks,4,5 including lung nodule detection,6 lung nodule change, vessel segmentation,7 and vessel tree extraction.8 Each challenge presents its own unique set of circumstances and considerations; however, important common themes exist. Future challenge organizers (and participants) could benefit from an open discussion of successes achieved, pitfalls encountered, and lessons learned from each completed challenge.

Guest Editorial: LUNGx Challenge for computerized lung nodule classification: reflections and lessons learned