TY - JOUR
T1 - Computed Tomography-Based Texture Analysis to Determine Human Papillomavirus Status of Oropharyngeal Squamous Cell Carcinoma
AU - Ranjbar, Sara
AU - Ning, Shuluo
AU - Zwart, Christine M.
AU - Wood, Christopher P.
AU - Weindling, Steven M.
AU - Wu, Teresa
AU - Mitchell, Joseph Ross
AU - Li, Jing
AU - Hoxworth, Joseph M.
PY - 2018/3/1
Y1 - 2018/3/1
N2 - 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.
AB - 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.
KW - human papillomavirus
KW - machine learning
KW - oropharyngeal cancer
KW - oropharynx
KW - radiomics
KW - squamous cell carcinoma
KW - texture analysis
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U2 - 10.1097/RCT.0000000000000682
DO - 10.1097/RCT.0000000000000682
M3 - Article
C2 - 29189396
AN - SCOPUS:85044400483
VL - 42
SP - 299
EP - 305
JO - Journal of Computer Assisted Tomography
JF - Journal of Computer Assisted Tomography
SN - 0363-8715
IS - 2
ER -