Intra-perinodular Textural Transition (Ipris): A 3D Descriptor for Nodule Diagnosis on Lung CT

  • Mehdi AlilouEmail author
  • Mahdi Orooji
  • Anant Madabhushi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)


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.


Radiomic Features Radiomics Method Malignant Nodules Interface Voxels Characterizing Lung Nodules 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers 1U24CA199374-01, R01CA202752-01A1 R01CA208236-01A1 R21CA179327-01; R21CA195152-01 the National Institute of Diabetes and Digestive and Kidney Diseases under award number R01DK098503-02, National Center for Research Resources under award number 1 C06 RR12463-01 the DOD Prostate Cancer Synergistic Idea Development Award (PC120857); the DOD Lung Cancer Idea Development New Investigator Award (LC130463), the DOD Prostate Cancer Idea Development Award; the DOD Peer Reviewed Cancer Research Program W81XWH-16-1-0329, W81XWH-15-1-0613, the Case Comprehensive Cancer Center Pilot Grant VelaSano Grant from the Cleveland Clinic the Wallace H. Coulter Foundation Program in the Department of Biomedical Engineering at Case Western Reserve University. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Computational Imaging and Personalized DiagnosticsCase Western Reserve UniversityClevelandUSA

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