Radiomic-Based Lung Nodule Classification in Low-Dose Computed Tomography

Radiomics is a systematic approach to characterize objects in terms of their radiological appearance. We used radiomic features of 5027 objects of 6 classes and trained a binary classifier with 79% accuracy. Features were obtained by using our novel preprocessing pipeline for object segmentation from the lung tissue in a low-dose Computed Tomography (LDCT) imaging technique. Our results show that radiomic features prove effective in distinguishing between suspicious and benign objects located in the lung tissue. Our data shows that there is vast space for improvement from both model- as well as a data-centric approach to developing Computer-aided detection (CAD) systems based on radiomics for early lung cancer detection. We show our results in the paper.