Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology · 2024
Prediction of the need of enteral nutrition during radiation therapy for head and neck cancers.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Giraud Paul, Guihard Sebastien, Thureau Sebastien, Guilbert Philippe, Ruffier Amandine, Eugene Remi, Lamrani-Ghaouti Assia, Chargari Cyrus, Liem Xavier, Bibault Jean Emmanuel
Аннотация
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.
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.