Intra-perinodular textural transition (Ipris): A 3D descriptor for nodule diagnosis on lung CT

Abstract

This paper presents Ipris (Intra-perinodular textural transition), a new radiomic method, to automatically distinguish between benign and malignant nodules on routine lung CT scans. Ipris represents a minimal set of quantitative measurements which attempt to capture the transition in textural appearance going from the inside to the outside of the nodule. Briefly the approach involves partitioning the 3D volume and interface of the nodule into K nested shells. Then, a set of 48 Ipris features from 2D slices of the shells are extracted. The features pertain to the spiculations, intensity and gradient sharpness obtained from intensity differences between inner and outer voxels of an interface voxel. The Ipris features were used to train a support vector machine classifier in order to distinguish between benign (granulomas) from malignant (adenocarcinomas) nodules on non-contrast CT scans. We used CT scans of 290 patients from multiple institutions, one cohort for training (N = 145) and the other (N = 145) for independent validation. Independent validation of the Ipris approach yielded an AUC of 0.83 whereas, the established textural and shape radiomic features yielded a corresponding AUC of 0.75, while the AUCs for two human experts (1 pulmonologist, 1 radiologist) yielded corresponding AUCs of 0.69 and 0.73.