@davecg It would be hard to get a working/stable nodule detector without lung segmentation. Potential adenopathy and metastasis could be independently interesting to improve final probability. But usually in lung cancer screening we need to find small nodules to detect early treatable T1 cancers to save lives. There is usually no diagnostic dilemna about the dominant nodule/mass when we observe a N+ and/or M+ cancer. They present as large spiculated nodules or masses.
As an adjunct to the radiologist, this algorithm could be interesting in borderline, not clearly suspect, not clearly benign, nodules (6-10 mm) to plan the workup (follow-up vs biopsy vs PET-CT vs surgery). Or of course, if nodule sensibility/specificity suprahuman-expertise is validated, to apply it as a fully automated ct lung screening tool with the nice responsability challenge of dealing with unrelated potentially significant observations (giant aorta aneurysm, lymphoma, thyroid cancer, pneumonia, tuberculosis, and many more) in the images. As I suggested in the screening mammography thread , it would be interesting to apply this code on a new external dataset with cross-validation from multiple radiologists to compare expert and machine ROCs as Google did for its Lancet diabetic retinopathy article. @jeremy probably has some interesting ideas to improve even more the result.