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

TL;DR

This paper presents a hybrid deep learning and decision tree approach to predict lung cancer malignancy from chest X-ray images, emphasizing interpretability and clinical utility, with promising sensitivity and specificity results.

Contribution

Introduces a novel hybrid model combining deep learning and decision trees for interpretable lung cancer malignancy prediction from X-ray images.

Findings

Achieved 86.7% sensitivity in malignancy detection

Attained 80.0% specificity in model performance

Decision trees provide interpretable malignancy stratification

Abstract

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature of deep learning models. Additionally, most lung nodules visible on chest X-ray are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two, independent data sets for which malignancy metadata is available. We mine multi-variate predictive models by fitting shallow decision trees to the malignancy stratified datasets and interrogate a range of metrics to determine the best model. Our best decision tree model achieves sensitivity and specificity of 86.7% and 80.0% respectively with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.