Head & neck · 2025
Fuel for Recovery: Predicting G-Tube Dependence in HPV-Negative Operable HNSCC.
СтатьяНазвание и аннотация: Перевод в очереди
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McNicoll Marie-Michelle, Mlynarek Alex M, Richardson Keith, Hier Michael, Sadeghi Nader, Casola Anna, Bouganim Nathaniel, Esfahani Khashayar, Tsien Christina, Shenouda George, Sultanem Khalil, Mascarella Marco A
Аннотация
BACKGROUND: HPV-negative head and neck squamous cell carcinoma (HNSCC) patients can face prolonged gastrostomy tube (g-tube) dependence, affecting quality of life and delaying treatment. Existing models lack key health metrics like sarcopenia; this study develops a risk model to enhance preoperative assessment.
METHODS: This study analyzed 265 HNSCC patients. Sarcopenia was assessed via cervical paraspinal skeletal muscle mass (C3) on CT scans. Patients requiring g-tube support > 28 days were identified. Risk models were developed and compared to the PEG Score. A 50-patient validation cohort assessed model performance.
RESULTS: Key predictors included male sex, sarcopenia, prior radiotherapy, and adjuvant therapy. Logistic regression (LR) model had the highest AUC (0.78); followed by Recursive Partitioning analysis (RPA) model (0.76), both outperforming the PEG Score (0.67). RPA model maintained superior accuracy in validation (AUC = 0.79).
CONCLUSION: RPA model outperformed PEG Score, offering a more precise risk tool for preoperative planning and early intervention.
METHODS: This study analyzed 265 HNSCC patients. Sarcopenia was assessed via cervical paraspinal skeletal muscle mass (C3) on CT scans. Patients requiring g-tube support > 28 days were identified. Risk models were developed and compared to the PEG Score. A 50-patient validation cohort assessed model performance.
RESULTS: Key predictors included male sex, sarcopenia, prior radiotherapy, and adjuvant therapy. Logistic regression (LR) model had the highest AUC (0.78); followed by Recursive Partitioning analysis (RPA) model (0.76), both outperforming the PEG Score (0.67). RPA model maintained superior accuracy in validation (AUC = 0.79).
CONCLUSION: RPA model outperformed PEG Score, offering a more precise risk tool for preoperative planning and early intervention.