Modeling Time-Dependent Recovery to Improve Esophagitis Prediction in Thoracic Reirradiation

In reirradiation, tumor control must be balanced against a high-risk of adverse effects. We evaluated the feasibility of a time-dependent recovery model to improve toxicity prediction.

Sixty-five high-risk thoracic reirradiation patients, identified by BID treatment, were included for modeling grade ≥2 acute esophagitis. The median (range) re-RT dose was 45 Gy (30-60) in 30 (20-40) fractions. Doses from each course were deformably registered to the most recent CT and converted to voxel-wise equivalent dose in 2-Gy fractions. We compared the discrimination of the last course dose, conventional direct accumulation without time consideration, and 3 time-dependent recovery models-mono-exponential, bi-exponential, and reciprocal time-each optimized via grid search with nested 5-fold cross-validation. Logistic regression with bootstrapping was used to assess the predictive value of mean and maximum dose. AUCs were compared using a bootstrap test. Covariates (age, chemotherapy, smoking status, and history of esophagitis from a previous course of thoracic radiation therapy) were also evaluated.

After BID re-RT, 26.2% (17/65) of patients experienced grade ≥2 esophagitis. Incorporating time-dependent repair algorithms achieved a higher AUC than the last course dose and direct accumulation. The bi-exponential model incorporating history of prior esophagitis achieved the highest performance (mean dose AUC: 0.83 [95% CI, 0.70-0.94] vs direct accumulation: 0.74 [0.61-0.87], P = .040; maximum dose AUC: 0.78 [0.65-0.89] vs 0.67 [0.54-0.80], P = .015).

Incorporating time-dependent recovery into dose accumulation is feasible, and our findings support its potential use over direct accumulation for more accurate toxicity prediction. These findings will pave the way for developing advanced outcome models for evidence-based reirradiation, ultimately reducing toxicity and optimizing doses for personalized and effective reirradiation.