Multiparametric machine learning algorithm for human papillomavirus status and survival prediction in oropharyngeal cancer patients.
The Cancer Imaging Archive (TCIA)
human papillomavirus
machine learning
oropharyngeal cancer
survival prediction
Journal
Head & neck
ISSN: 1097-0347
Titre abrégé: Head Neck
Pays: United States
ID NLM: 8902541
Informations de publication
Date de publication:
11 2023
11 2023
Historique:
received:
06
08
2023
accepted:
10
09
2023
medline:
23
10
2023
pubmed:
23
9
2023
entrez:
23
9
2023
Statut:
ppublish
Résumé
Human papillomavirus (HPV) status influences prognosis in oropharyngeal cancer (OPC). Identifying high-risk patients are critical to improving treatment. We aim to provide a noninvasive opportunity for managing OPC patients by training multiple machine learning pipelines to determine the best model for characterizing HPV status and survival. Multi-parametric algorithms were designed using a 492 OPC patient database. HPV status incorporated age, sex, smoking/drinking habits, cancer subsite, TNM, and AJCC 7th edition staging. Survival considered HPV model inputs plus HPV status. Patients were split 4:1 training: testing. Algorithm efficacy was assessed through accuracy and area under the receiver operator characteristic curve (AUC). From 31 HPV status models, ensemble yielded 0.83 AUC and 78.7% accuracy. From 38 survival models, ensemble yielded 0.91 AUC and 87.7% accuracy. Results reinforce artificial intelligence's potential to use tumor imaging and patient characterizations for HPV status and outcome prediction. Utilizing these algorithms can optimize clinical guidance and patient care noninvasively.
Sections du résumé
BACKGROUND
Human papillomavirus (HPV) status influences prognosis in oropharyngeal cancer (OPC). Identifying high-risk patients are critical to improving treatment. We aim to provide a noninvasive opportunity for managing OPC patients by training multiple machine learning pipelines to determine the best model for characterizing HPV status and survival.
METHODS
Multi-parametric algorithms were designed using a 492 OPC patient database. HPV status incorporated age, sex, smoking/drinking habits, cancer subsite, TNM, and AJCC 7th edition staging. Survival considered HPV model inputs plus HPV status. Patients were split 4:1 training: testing. Algorithm efficacy was assessed through accuracy and area under the receiver operator characteristic curve (AUC).
RESULTS
From 31 HPV status models, ensemble yielded 0.83 AUC and 78.7% accuracy. From 38 survival models, ensemble yielded 0.91 AUC and 87.7% accuracy.
CONCLUSION
Results reinforce artificial intelligence's potential to use tumor imaging and patient characterizations for HPV status and outcome prediction. Utilizing these algorithms can optimize clinical guidance and patient care noninvasively.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
2882-2892Informations de copyright
© 2023 Wiley Periodicals LLC.
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