Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer.

Esophageal cancer is a highly aggressive gastrointestinal malignancy with a poor prognosis, making accurate prognostic assessment essential for patient care. The performance of the esophageal cancer prognosis model based on conventional radiomics is limited, as it mainly characterizes the spatial features such as texture of the tumor area, and cannot fully describe the complexity of esophageal cancer tumors. Therefore, we incorporate the frequency domain features into radiomics to improve the prognostic ability of esophageal cancer. Three hundred fifteen esophageal cancer patients participated in the death risk prediction experiment, with 80% being the training set and 20% being the testing set. We use fivefold cross validation for training, and fuse the 5 trained models through voting to obtain the final prognostic model for testing. The CatBoost achieved the best performance compared to machine learning methods such as random forests and decision tree. The experimental results showed that the combination of frequency domain and radiomics features achieved the highest performance in death predicting esophageal cancer (accuracy: 0.7423, precision: 0.7470, recall: 0.7375, specification: 0.8030, AUC: 0.8487), which was significantly better than the performance of frequency domain or radiomics features alone. The results of Kaplan-Meier survival analysis validated the performance of our method in death predicting esophageal cancer. The proposed method provides technical support for accurate prognosis of esophageal cancer.