Development of machine learning models for the prediction of long-term feeding tube dependence after oral and oropharyngeal cancer surgery

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 and methods: 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.

Keywords: Artificial intelligence; Enteral nutrition; Head and neck neoplasms; Nutritional status; Squamous cell carcinoma of head and neck.