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Lung cancer is the leading cause of cancer death and morbidity worldwide.Many studies have shown machine learning models to be effective at detectinglung nodules from chest X-ray images. However, these techniques have yet to beembraced by the medical community due to several practical, ethical, andregulatory constraints stemming from the black-box nature of deep learningmodels. Additionally, most lung nodules visible on chest X-ray are benign;therefore, the narrow task of computer vision-based lung nodule detectioncannot be equated to automated lung cancer detection. Addressing both concerns,this study introduces a novel hybrid deep learning and decision tree-basedcomputer vision model which presents lung cancer malignancy predictions asinterpretable decision trees. The deep learning component of this process istrained using a large publicly available dataset on pathological biomarkersassociated with lung cancer. These models are then used to inference biomarkerscores for chest X-ray images from two, independent data sets for whichmalignancy metadata is available. We mine multi-variate predictive models byfitting shallow decision trees to the malignancy stratified datasets andinterrogate a range of metrics to determine the best model. Our best decisiontree model achieves sensitivity and specificity of 86.7% and 80.0% respectivelywith a positive predictive value of 92.9%. Decision trees mined using thismethod may be considered as a starting point for refinement into clinicallyuseful multi-variate lung cancer malignancy models for implementation as aworkflow augmentation tool to improve the efficiency of human radiologists.

Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images