Lung cancer is the leading cause of cancer death worldwide with early
detection being the key to a positive patient prognosis. Although a multitude
of studies have demonstrated that machine learning, and particularly deep
learning, techniques are effective at automatically diagnosing lung cancer,
these techniques have yet to be clinically approved and adopted by the medical
community. Most research in this field is focused on the narrow task of nodule
detection to provide an artificial radiological second reading. We instead
focus on extracting, from chest X-ray images, a wider range of pathologies
associated with lung cancer using a computer vision model trained on a large
dataset. We then find the set of best fit decision trees against an
independent, smaller dataset for which lung cancer malignancy metadata is
provided. For this small inferencing dataset, our best model achieves
sensitivity and specificity of 85% and 75% respectively with a positive
predictive value of 85% which is comparable to the performance of human
radiologists. Furthermore, the decision trees created by this method may be
considered as a starting point for refinement by medical experts into
clinically usable multi-variate lung cancer scoring and diagnostic models