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Updated: Jun 27, 2025
Chromogenic In Situ Hybridization as a Tool for HPV-Related Head and Neck Cancer Diagnosis
Published on: June 14, 2019
Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas
Edwin Qiu1, Maryam Vejdani-Jahromi2, Artem Kaliaev3
1Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States of America.
Background:
Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV status using CT images.
Methods:
Pretreatment CT images from OPSCC patients were used to train a 3D DenseNet-121 model to predict HPV-p16 status. Performance was evaluated by the ROC Curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1 score.
Results:
The network achieved a mean AUC of 0.80 ± 0.06. The best-preforming fold had a sensitivity of 0.86 and specificity of 0.92 at the Youden's index. The PPV, NPV, and F1 scores are 0.97, 0.71, and 0.82, respectively.
Conclusions:
A fully automated CNN can characterize the HPV status of OPSCC patients with high sensitivity and specificity. Further refinement of this algorithm has the potential to provide a non-invasive tool to guide clinical management.
