Prediction of the need of enteral nutrition during radiation therapy for head and neck cancers

Abstract

Introduction: Patients with a head and neck (HN) cancer undergoing radiotherapy risk critical weight loss and oral intake reduction leading to enteral nutrition. We developed a predictive model for the need for enteral nutrition during radiotherapy in this setting. Its performances were reported on a real-world multicentric cohort.

Material and methods: Two models were trained on a prospective monocentric cohort of 230 patients. The first model predicted an outcome combining severe or early fast weight loss, or severe oral intake impairment (grade 3 anorexia or dysphagia or the prescription of enteral nutrition). The second outcome only combined oral intake impairment criteria. We trained a gradient boosted tree with a nested cross validation for Bayesian optimization on a prospective cohort and predictive performances were reported on the external multicentric real-world cohort of 410 patients from 3 centres. Predictions were explainable for each patient using Shapley values.

Results: For the first and second outcome, the model yielded a ROC curve AUC of 81 % and 80%, an accuracy of 77 % and 77 %, a positive predictive value of 77 % and 72 %, a specificity of 78 % and 79 % and a sensitivity of 75 % and 73 %. The negative predictive value was 80 % and 80 %. For each patient, the underlying Shapley values of each clinical predictor to the prediction could be displayed. Overall, the most contributing predictor was concomitant chemotherapy.

Conclusion: Our predictive model yielded good performance on a real life multicentric validation cohort to predict the need for enteral nutrition during radiotherapy for HN cancers.

Keywords: Artificial intelligence; Decision support; Head and neck cancers; Nutrition; Radiotherapy.