393. A RADIOMICS STRATEGY BASED ON CT INTRA-TUMORAL AND PERITUMORAL REGIONS FOR PREOPERATIVE PREDICTION OF NEOADJUVANT CHEMORADIOTHERAPY FOR ESOPHAGEAL CANCER

The standard treatment for esophageal cancer patients is neoadjuvant chemoradiotherapy followed by surgery. However, some of these patients do not achieve pathological complete response with this therapy, resulting in poor outcomes. The objective of this study is to develop a method for selecting patients who can achieve pathological complete response through pre-neoadjuvant therapy chest-enhanced CT scans. The study enrolled 201 patients with esophageal cancer and divided them into a training set and a testing set in a 7:3 ratio. Radiomics features of intra-tumoral and peritumoral images were extracted from preoperative chest-enhanced CT scans of these patients. The features underwent dimensionality reduction in two steps, using Student’s t-test and least absolute shrinkage and selection operator. The selected intra-tumoral and peritumoral (including marginal and adjacent ROI) features were used to build models with four machine learning classifiers. The models with satisfactory accuracy and stability levels were considered to perform well. Finally, the performance of these well-performing models on the testing set was displayed using ROC curves. Among the 16 models, the best-performing models were the integrated (intra-tumoral and peritumoral features) -XGBoost and integrated-random forest models. In the training set, the two models had average ROC AUCs of 0.906 and 0.918 respectively, with relative standard deviations (RSDs) of 6.26 and 6.89. In the testing set, the AUCs were 0.845 and 0.871, respectively. There was no significant difference in the ROC curves between the two models. The addition of peritumoral radiomics features to the radiomics analysis may improve the predictive performance of pathological response for esophageal cancer patients to neoadjuvant chemoradiotherapy. The integrated (intra-tumoral and peritumoral features) -XGBoost and integrated-random forest models developed in this study show potential for predicting pathological complete response in esophageal cancer patients and may help in selecting patients for neoadjuvant therapy.