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.