Related Experiment Video
Updated: Jul 14, 2026
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
Evaluation of Machine Learning and Statistical Models for Predicting Long Term Gastrostomy Tube Dependency in
Amirpouyan Namavarian1, Abdulrahman Alenazi1, Katrina Hueniken2
1Department of Otolaryngology - Head & Neck Surgery, Princess Margaret Cancer Center - University Health Network, University of Toronto, Toronto, Ontario, Canada.
Background:
Swallowing dysfunction and long-term gastrostomy tube dependence are common morbidities after oral cavity cancer resection with free flap reconstruction, highlighting the need for preoperative risk prediction.
Methods:
We performed a multicenter retrospective pooled cohort study of patients with non-metastatic oral cavity cancer undergoing free flap reconstruction. Firth's penalized logistic regression, LASSO, and random forest models were trained using 10-fold cross-validation and predictive performance was assessed via area under the receiver operator curve (AUC).
Results:
Among 500 patients (mean age 61.7 years), 78 (16%) were gastrostomy tube dependent at 12 months. The random forest model was most accurate in predicting 12-month gastrostomy tube dependency (AUC; 0.77), with comparable performance from LASSO (AUC; 0.71) and Firth's penalized logistic regression (AUC; 0.70). Key predictors included TNM stage IV, T4 disease, unilateral neck dissection, reconstruction site, and no tongue resection.
Conclusions:
The three models predicted 12-month gastrostomy tube dependency after oral cavity free-flap surgery with fair accuracy and novel predictors; larger multicenter datasets may further improve predictive performance.
