Development and validation of a radiomics signature on differentially expressed features of 18F-FDG PET to predict treatment response of concurrent chemoradiotherapy in thoracic esophagus squamous cell carcinoma

Abstract

Background and purposeTo investigate potential image markers for early prediction of treatment response on thoracic esophagus squamous cell carcinoma (ESCC) treated with concurrent chemoradiotherapy (CCRT). Materials and methods159 thoracic ESCC patients enrolled from two institutions were divided into training and validation sets. A total of 944 radiomics features were extracted from pretreatment 18F-FDG PET images. We first performed the inter-observer reproducibility test in 10 pairs of patients (responders vs. nonresponders), and the limma package was used to identify differentially expressed features (DEFs). Then the least absolute shrinkage and selection operator (LASSO) logistic regression model with 10-fold cross-validation was used to construct a treatment response related radiomics signature. Finally, the performance was assessed in both sets with receiver operating characteristic (ROC) curves and Kaplan-Meier analysis. ResultsAfter the inter-observer test, 691 features were considered reproducible and been retained (ICC > 0.9). 61 DEFs were selected from limma and entered into the LASSO logistic regression model. The radiomics signature was significantly associated with treatment response in the training (p < 0.001) and validation set (p = 0.026), which achieved area under curve (AUC) values of 0.844 and 0.835, respectively. Delong test results of two ROCs showed no significant difference (p = 0.918). The cut-off value of the radiomics signature could successfully divide patients into high-risk and low-risk groups in both sets. ConclusionThis study indicated that the proposed radiomics signature could be a useful image marker to predict the therapeutic response of thoracic ESCC patients treated with CCRT.