Multiomic random forest toxicity modeling of radiation esophagitis.

Background and purpose: Radiation esophagitis (RE) is a major dose-limiting toxicity resulting from radiotherapy for non-small-cell lung cancer (NSCLC). Multiomic features may provide additional predictive value for high-grade RE compared to traditionally used clinical and dose-volume histogram (DVH) parameters. We aimed to investigate the utility of multiomic features in improving RE toxicity prediction models. Materials and methods: Of the 179 NSCLC patients considered, 27 patients (15.08 %) were found to have toxicity ≥grade 3 RE per CTCAE v5.0. A total of 343 CT- and PET- based radiomic and dosiomic features were extracted. Four toxicity prediction models were created using clinical factors and features from one of the following groups: (a) base model (DVH), (b) radiomic, (c) dosiomic, and (d) combined radiomic and dosiomic. Models were developed using a random forest classifier with 100 Monte Carlo cross-validation iterations and an 80 %/20 % training/test split. Model predictive performance was evaluated by area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC). Results: The AUC and AUPRC values (mean ± standard deviation) for the 4 model types were 0.69 ± 0.10/0.42 ± 0.12 (base model), 0.71 ± 0.12/0.48 ± 0.13 (radiomic), 0.73 ± 0.10/0.48 ± 0.15 (dosiomic), and 0.75 ± 0.10/0.49 ± 0.14 (radiomic and dosiomic), respectively. The bootstrap percentile method, which was used to compare performance metrics between multiomic and base models, showed that the combined model was the best performing model type. Conclusion: All multiomic models outperformed the base model. The combined radiomic-dosiomic model provides novel insights into high-grade RE risk and may inform future strategies for toxicity mitigation and personalized treatment planning.