Development of machine learning models for the prediction of long-term feeding tube dependence after oral and oropharyngeal cancer surgery.
Oral Oncol
; 148: 106643, 2024 Jan.
Article
em En
| MEDLINE
| ID: mdl-38006688
ABSTRACT
PURPOSE:
To predict the necessity of enteral nutrition at 28 days after surgery in patients undergoing major head and neck oncologic procedures for oral and oropharyngeal cancers. MATERIAL ANDMETHODS:
Data from 193 patients with oral cavity and oropharyngeal squamous cell carcinoma were retrospectively collected at two tertiary referral centers to train (n = 135) and validate (n = 58) six supervised machine learning (ML) models for binary prediction employing 29 clinical variables available pre-operatively.RESULTS:
The accuracy of the six ML models ranged between 0.74 and 0.88, while the measured area under the curve (AUC) between 0.75 and 0.87. The ML algorithms showed high specificity (range 0.87-0.96) and moderate sensitivity (range 0.31-0.77) in detecting patients with ≥28 days feeding tube dependence. Negative predictive value was higher (range 0.81-0.93) compared to positive predictive value (range 0.40-0.71). Finally, the F1 score ranged between 0.35 and 0.74.CONCLUSIONS:
Classification performance of the ML algorithms showed optimistic accuracy in the prediction of enteral nutrition at 28 days after surgery. Prospective studies are mandatory to define the clinical benefit of a ML-based pre-operative prediction of a personalized nutrition protocol.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Neoplasias Orofaríngeas
/
Neoplasias de Cabeça e Pescoço
Limite:
Humans
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article