@article{9f76976d1b9548fcb6067919e1a5e6a8,
title = "Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma",
abstract = "Objective To determine whether machine learning can accurately classify human papillomavirus (HPV) status of oropharyngeal squamous cell carcinoma (OPSCC) using computed tomography (CT)-based texture analysis. Methods Texture analyses were retrospectively applied to regions of interest from OPSCC primary tumors on contrast-enhanced neck CT, and machine learning was used to create a model that classified HPV status with the highest accuracy. Results were compared against the blinded review of 2 neuroradiologists. Results The HPV-positive (n = 92) and-negative (n = 15) cohorts were well matched clinically. Neuroradiologist classification accuracies for HPV status (44.9%, 55.1%) were not significantly different (P = 0.13), and there was a lack of agreement between the 2 neuroradiologists (κ =-0.145). The best machine learning model had an accuracy of 75.7%, which was greater than either neuroradiologist (P < 0.001, P = 0.002). Conclusions Useful diagnostic information regarding HPV infection can be extracted from the CT appearance of OPSCC beyond what is apparent to the trained human eye.",
keywords = "human papillomavirus, machine learning, oropharyngeal cancer, oropharynx, radiomics, squamous cell carcinoma, texture analysis",
author = "Sara Ranjbar and Shuluo Ning and Zwart, {Christine M.} and Wood, {Christopher P.} and Weindling, {Steven M.} and Teresa Wu and {Ross Mitchell}, J. and Jing Li and Hoxworth, {Joseph M.}",
note = "Funding Information: This work was generously supported by a US $40,000 grant from the Arizona State University/Mayo Clinic Seed Grant Program (jointly awarded to J.L. and J.M.H.). Funding Information: From the *Department of Biomedical Informatics, †School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe; ‡Department of Radiology, Mayo Clinic, Phoenix, AZ; §Department of Radiology, Mayo Clinic, Rochester, MN; ||Department of Radiology, Mayo Clinic, Jacksonville, FL; and ¶Department of Research, Mayo Clinic, Scottsdale, AZ. Received for publication June 21, 2017; accepted August 29, 2017. Correspondence to: Joseph M. Hoxworth, MD, 5777 E Mayo Blvd, Phoenix, AZ 85054 (e‐mail: hoxworth.joseph@mayo.edu). This work was generously supported by a US $40,000 grant from the Arizona State University/Mayo Clinic Seed Grant Program (jointly awarded to J.L. and J.M.H.). J.R.M. has submitted intellectual property disclosures regarding texture analysis of medical imaging data to Mayo Clinic Ventures, which is exploring provisional patents. No other potential conflicts of interest exist for this body of work. Copyright {\textcopyright} 2017 Wolters Kluwer Health, Inc. All rights reserved. DOI: 10.1097/RCT.0000000000000682 Publisher Copyright: {\textcopyright} 2017 Wolters Kluwer Health, Inc. All rights reserved.",
year = "2018",
month = mar,
day = "1",
doi = "10.1097/RCT.0000000000000682",
language = "English (US)",
volume = "42",
pages = "299--305",
journal = "Journal of Computer Assisted Tomography",
issn = "0363-8715",
publisher = "Lippincott Williams and Wilkins",
number = "2",
}
