Work

Optimizing Selection of Reactive versus Prophylactic Gastrostomy Tube Placement in Patients Treated with Radiation Therapy for Head and Neck Cancer

Year: 2025

Type: article

Abstract: Purpose: To develop a predictive assessment for determining patients at high risk of reactive gastrostomy tube (GT) placement during radiation therapy (RT) for head and neck cancer. Methods and Materials: Data of patients with head and neck cancer treated with RT from 2018 to 2021 at a single institution were analyzed. Baseline patient characteristics were obtained and used to compare patients receiving prophylactic GT (pGT) with those who did not receive a pGT (reactive, or rGT group). Patients in the rGT group were further categorized as rGT-conditional (had GT placed or experienced > 10% weight loss during RT) or rGT-appropriate (no GT placed and < 10% weight loss during RT). Clinical, disease, and dosimetric factors were abstracted from the medical record, including dose to dysphagia/aspiration-related structures. A mixed linear model was used to assess weight loss through 12 months post-RT. Within the reactive group, multivariable logistic regression was used to develop a model predicting rGT-conditional cases who experienced rGT placement or weight loss > 10% during RT. Results: A total of 202 patients met the inclusion criteria: 86 in the pGT group and 116 in the rGT group. Patients in the pGT group were more likely to have advanced tumor stage, lower baseline functional oral intake scale, bilateral neck RT, concurrent chemotherapy, and higher mean pharynx dose of RT. Weight loss outcomes were similar between groups. Multivariable analysis identified several significant predictors of rGT-conditional cases. Conclusions: Among patients planned for head and neck RT using an rGT paradigm, we identified predictors of GT placement or excessive weight loss on-treatment and developed a predictive model of GT placement in this group. Identifying patients at high risk of substantial weight loss and/or GT placement during RT may optimize personalization of rGT versus pGT. External validation of this model's ability to predict patients at high risk of ultimately receiving rGT is warranted.

Source: Advances in Radiation Oncology

DOI: 10.1016/j.adro.2025.101930