Diagnostic Performance of Vascular Permeability and Texture Parameters for Evaluating the Response to Neoadjuvant Chemoradiotherapy in Patients With Esophageal Squamous Cell Carcinoma

Background: Esophageal squamous cell carcinoma (ESCC) is an aggressive cancer with a poor prognosis. The development of an accurate and non-invasive method to evaluate the pathologic response of patients with ESCC to chemoradiotherapy remains a critical issue. Therefore, the aim of this study was to assess the importance of vascular permeability and texture parameters in predicting the response to neoadjuvant chemoradiotherapy (NACRT) in patients with ESCC. Methods: This prospective analysis included patients with T1-T2 stage of ESCC, without lymphatic metastasis, and distant metastasis. All patients underwent surgery and received NACRT twice in the following order: NACRT-surgery-NACRT. Patients underwent surgery during the time period between the two NACRTs. All patients underwent dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) twice, i.e., before the first NACRT and after the second NACRT. Patients were assessed for treatment response at 30 days after the second NACRT. Patients were grouped into the complete response (CR) and partial response (PR) groups based on their responses to NACRT. Vascular permeability and texture parameters were extracted from the DCE-MRI. After assessing the diagnostic performance of individual parameters, a combined model with vascular permeability parameters and texture parameters was generated to predict the response to NACRT. Results: In our study, the CR and PR groups included 16 patients each. Four vascular permeability parameters and seven texture parameters were extracted from the DCE-MRI. The volume transfer constant (Ktrans), extracellular extravascular volume fraction (Ve), and entropy, extracted from the second DCE-MRI, showed significant differences between the CR and PR groups. The area under the curve (AUC) of Ktrans, Ve, and entropy values showed good diagnostic performances (0.813, 0.789, and 0.707, respectively). The combined model, a logistic regression model with Ktrans, Ve, and entropy, had a significant diagnostic performance (AUC=0.977). Conclusions: Combined model with vascular permeability and texture parameters could greatly improve the diagnostic performance after NACRT in patients with ESCC.