Enhanced Lung Nodule Malignancy Suspicion Classifier using Biomarkers, Radiomics and Image Features

Abstract

Lung cancer is the leading cause of all cancer-related deaths. An early detection of lung cancer by inspecting computed tomography (CT) tends to improve survival rates significantly without the need for invasive procedures. This work aims to strengthen standard image-based deep learning lung nodule malignancy classification models by combining user defined features like biomarkers, nodule size, shape and radiomic features with deep features and obtain a meaningful representation of lung nodules. The National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) IDRI dataset is used to train and evaluate the classification task. We combine 3D deep image features of the nodules with biomarkers, nodule diameter, volume and its radiomic features and train an ensemble model that classifies nodules as malignant or benign. As a result, we aim to reduce false-positive rates among patients and compare our results with current state of the art lung nodule malignancy classification models. This work employs a 3D Convolutional Neural Network as well as a Random Forest to combine the nodule features. Our results show that a combination of deep features and user-defined features outperform image classification with CNN'salone in predicting suspicion levels of lung nodule malignancy using LIDC-IDRI.