Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor

• Type of Material: Article
• Authors: Prateek Prasanna, Case Western Reserve University; Pallavi Tiwari, Case Western Reserve University; Anant Madabhushi, Emory University
• Language: English
• Date: 2016-11-22
• Publisher: NATURE PORTFOLIO
• Publication Version: Final Published Version
• Copyright Statement: © 2016, The Author(s)
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/srep37241
• Title of Journal or Parent Work: SCIENTIFIC REPORTS
• Volume: 6
• Start Page: 37241
• End Page: 37241
• Grant/Funding Information: Research reported in this publication was supported by the National Cancer Institute of the National Institutes of Health under award numbers R01CA136535-01, R01CA140772-01, R21CA167811-01, R21CA179327-01; R21CA195152-01 the National Institute of Diabetes and Digestive and Kidney Diseases under award number R01DK098503-02, the DOD Prostate Cancer Synergistic Idea Development Award (PC120857); the DOD Lung Cancer Idea Development New Investigator Award (LC130463); the Ohio Third Frontier Technology development Grant, the CTSC Coulter Annual Pilot Grant, the Case Comprehensive Cancer Center Pilot Grant, VelaSano Grant from the Cleveland Clinic, and 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.
• Keywords: Multidisciplinary Sciences; Science & Technology - Other Topics; RECURRENCE; MOLECULAR SUBTYPES; BREAST-CANCER; Science & Technology; IMAGES
• Research Categories: Health Sciences, Oncology; Health Sciences, Obstetrics and Gynecology

Abstract

In this paper, we introduce a new radiomic descriptor, Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe) for capturing subtle differences between benign and pathologic phenotypes which may be visually indistinguishable on routine anatomic imaging. CoLlAGe seeks to capture and exploit local anisotropic differences in voxel-level gradient orientations to distinguish similar appearing phenotypes. CoLlAGe involves assigning every image voxel an entropy value associated with the co-occurrence matrix of gradient orientations computed around every voxel. The hypothesis behind CoLlAGe is that benign and pathologic phenotypes even though they may appear similar on anatomic imaging, will differ in their local entropy patterns, in turn reflecting subtle local differences in tissue microarchitecture. We demonstrate CoLlAGe's utility in three clinically challenging classification problems: distinguishing (1) radiation necrosis, a benign yet confounding effect of radiation treatment, from recurrent tumors on T1-w MRI in 42 brain tumor patients, (2) different molecular sub-types of breast cancer on DCE-MRI in 65 studies and (3) non-small cell lung cancer (adenocarcinomas) from benign fungal infection (granulomas) on 120 non-contrast CT studies. For each of these classification problems, CoLlAGE in conjunction with a random forest classifier outperformed state of the art radiomic descriptors (Haralick, Gabor, Histogram of Gradient Orientations).