Ensemble artificial neural network lung nodule classification utilizing nodular and peri-nodular radiomics

Lung cancer accounts for 22% of all cancer deaths, necessitating screening with Low Dose Computed Tomography (LDCT) to improve early detection. Approximately 97% of early detected nodules are benign. Mathematical Prediction Models (MPMs) based on demographic, clinical, and radiologist interpretated data have been developed to provide insight into lung cancer risk at the time of nodule detection. However, MPMs incorporate subjective data (patient reported and interpretation). We hypothesized that a machine learning approach that utilizes only quantitative features extracted from the LDCT data, focusing on the detected nodule and surrounding lung parenchyma, can outperform existing calibrated MPMs. Our approach explores predictive performance of ensembles of Artificial Neural Networks trained on important radiomic features, showing improved specificity and AUC-pr when compared to current MPMs.