Multi-omics and Multi-VOIs to Predict Esophageal Fistula in Esophageal Cancer Patients Treated with Radiotherapy

Abstract

Abstract Objective: This study aimed to develop a prediction model for esophageal fistula (EF) in esophageal cancer (EC) patients treated with intensity-modulated radiation therapy (IMRT), by integrating multi-omics features from multiple volumes-of-interest (VOIs). Methods: We retrospectively analyzed pretreatment planning computed tomographic (CT) images, three-dimensional dose distributions, and clinical factors of 287 EC patients. Nine groups of features from different combination of omics [Radiomics (R), Dosiomics (D), and RD (the combination of R and D)], and VOIs [esophagus (ESO), gross tumor volume (GTV), and EG (the combination of ESO and GTV)] were extracted and separately selected by unsupervised and supervised approaches. The final model performance was evaluated using five metrics: average area under the receiver-operator-characteristics curve (AUC), accuracy, precision, recall, and F1-score. Results: For multi-omics using RD features, the model performance in EG model shows：AUC, 0.817±0.031; 95% CI: 0.805, 0.825; p＜0.001, which is better than single VOI (ESO or GTV). Conclusion: Integrating multi-omics features from multi-VOIs enables better prediction of EF in EC patients treated with IMRT. The incorporation of dosiomics features can enhance the model performance of the prediction.