Abstract
BACKGROUND: Predicting the pathological response of esophageal cancer (EC) to neoadjuvant therapy (NAT) is of significant clinical importance. AIM: To evaluate the pathological response of NAT in EC patients using multiple machine learning algorithms based on magnetic resonance imaging (MRI) radiomics. METHODS: This retrospective study included 132 patients with pathologically confirmed EC, were randomly divided into a training cohort (n = 92) and a validation cohort (n = 40) in a 7:3 ratio. All patients underwent a preoperative MRI scan from the neck to the abdomen. High-throughput and quantitative radiomics features were extracted from T2-weighted imaging (T2WI). Radiomics signatures were selected using minimal redundancy maximal relevance and the least absolute shrinkage and selection operator. Nine classification algorithms were used to build the models, and the diagnostic performance of each model was evaluated using the area under the curve (AUC), sensitivity (SEN), and specificity (SPE). RESULTS: A total of 1834 features were extracted. Following feature dimension reduction, ten radiomics features were selected to construct radiomics signatures. Among the nine classification algorithms, the ExtraTrees algorithm demonstrated the best diagnostic performance in both the training (AUC: 0.932; SEN: 0.906; SPE: 0.817) and validation cohorts (AUC: 0.900; SEN: 0.667; SPE: 0.700). The Delong test proved no significance in the diagnostic efficiency within these models (P > 0.05). CONCLUSION: T2WI radiomics may aid in determining the pathological response to NAT in EC patients, serving as a noninvasive and quantitative tool to assist personalized treatment planning.