Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.
Am J Otolaryngol
; 45(4): 104357, 2024.
Article
en En
| MEDLINE
| ID: mdl-38703612
ABSTRACT
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.Palabras clave
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Base de datos:
MEDLINE
Asunto principal:
Neoplasias Orofaríngeas
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Tomografía Computarizada por Rayos X
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Infecciones por Papillomavirus
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Aprendizaje Automático
Idioma:
En
Revista:
Am J Otolaryngol
Año:
2024
Tipo del documento:
Article