Multi-centered Pre-treatment CT Based Radiomics Features to Predict Locoregional Recurrence of Locally Advanced Esophageal Cancer After Definitive Chemoradiotherapy

Abstract Purpose We constructed a prediction model to predict 2-year locoregional recurrence based on the clinical features and radiomics features extracted from machine learning method using computed tomography (CT) before definite chemoradiotherapy (dCRT) in locally advanced esophageal cancer. Patients and methods: A total of 264 patients (156 in Beijing, 87 in Tianjin, and 21 in Jiangsu) were included in this study. All those locally advanced esophogeal cancer patients received definite radiotherapy, and were randomly divided into 5 subgroups with similar number and divided into training group and validation group by five cross-validation. The esophageal tumor and extratumoral esophagus were segmented to extract radiomics features from the gross tumor volume (GTV) drawn by radiation therapists before radiotherapy, and 6 clinical features associated with prognosis were added. T stage, N stage, M stage, total stage, GTV and GTVnd volume were included to construct a prediction model to predict the 2-year locoregional recurrence of patients after definitive radiotherapy. Results 264 patients were enrolled from August 2012 to April 2018, with a median age of 62 years and 81% were males. The 2-year locoregional recurrence rate was 52.6%, and the 2-year overall survival rate was 45.6%. About 66% patients received concurrent chemotherapy. In total, we extracted 786 radiomics features from CT images and. the Principal Component Analysis (PCA) method was used to screen out the maximum 30 features. Finally the Support Vector Machine (SVM) method was used to construct the integrated prediction model combining radiomics and clinical features. In the 5 training groups for predicting locoregional recurrence, the mean value of C-index was 0.9841 (95%CI, 0.9809–0.9873), and in the 5 validation groups, the mean value was 0.744 (95%CI, 0.7437–0.7443). Conclusion The prediction model could predict 2-year locoregional recurrence after radiotherapy. It can provide a foundation for searching for suitable treatment strategy for patients receiving definitive radiotherapy and guiding subsequent consolidation therapy.