Explainable prediction model for the human papillomavirus status in patients with oropharyngeal squamous cell carcinoma using CNN on CT images.
Humans
Oropharyngeal Neoplasms
/ virology
Tomography, X-Ray Computed
/ methods
Neural Networks, Computer
Papillomavirus Infections
/ diagnostic imaging
Male
Female
Papillomaviridae
Middle Aged
Aged
Carcinoma, Squamous Cell
/ diagnostic imaging
Squamous Cell Carcinoma of Head and Neck
/ virology
Tumor Burden
Human Papillomavirus Viruses
Convolutional neural network
Explainable artificial intelligence
Grad-CAM
Human papillomavirus
Oropharyngeal squamous cell carcinoma
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
20 06 2024
20 06 2024
Historique:
received:
23
11
2023
accepted:
18
06
2024
medline:
21
6
2024
pubmed:
21
6
2024
entrez:
20
6
2024
Statut:
epublish
Résumé
Several studies have emphasised how positive and negative human papillomavirus (HPV+ and HPV-, respectively) oropharyngeal squamous cell carcinoma (OPSCC) has distinct molecular profiles, tumor characteristics, and disease outcomes. Different radiomics-based prediction models have been proposed, by also using innovative techniques such as Convolutional Neural Networks (CNNs). Although some of these models reached encouraging predictive performances, there evidence explaining the role of radiomic features in achieving a specific outcome is scarce. In this paper, we propose some preliminary results related to an explainable CNN-based model to predict HPV status in OPSCC patients. We extracted the Gross Tumor Volume (GTV) of pre-treatment CT images related to 499 patients (356 HPV+ and 143 HPV-) included into the OPC-Radiomics public dataset to train an end-to-end Inception-V3 CNN architecture. We also collected a multicentric dataset consisting of 92 patients (43 HPV+ , 49 HPV-), which was employed as an independent test set. Finally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) technique to highlight the most informative areas with respect to the predicted outcome. The proposed model reached an AUC value of 73.50% on the independent test. As a result of the Grad-CAM algorithm, the most informative areas related to the correctly classified HPV+ patients were located into the intratumoral area. Conversely, the most important areas referred to the tumor edges. Finally, since the proposed model provided additional information with respect to the accuracy of the classification given by the visualization of the areas of greatest interest for predictive purposes for each case examined, it could contribute to increase confidence in using computer-based predictive models in the actual clinical practice.
Identifiants
pubmed: 38902523
doi: 10.1038/s41598-024-65240-9
pii: 10.1038/s41598-024-65240-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
14276Subventions
Organisme : Ministero della Salute
ID : RC2024
Informations de copyright
© 2024. The Author(s).
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