Dual energy CT-derived quantitative parameters and hematological characteristics predict pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell carcinoma patients.

Abstract

Purpose: There is no gold standard method to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients before surgery after neoadjuvant chemoradiotherapy (nCRT). This study aims to investigate whether dual layer detector dual energy CT (DECT) quantitative parameters and clinical features could predict pCR for ESCC patients after nCRT.

Patients and methods: This study retrospective recruited local advanced ESCC patients who underwent nCRT followed by surgical treatment from December 2019 to May 2023. According to pCR status (no visible cancer cells in primary cancer lesion and lymph nodes), patients were categorized into pCR group (N = 25) and non-pCR group (N = 28). DECT quantitative parameters were derived from conventional CT images, different monoenergetic (MonoE) images, virtual non-contrast (VNC) images, Z-effective (Zeff) images, iodine concentration (IC) images and electron density (ED) images. Slope of spectral curve (λHU), normalized iodine concentration (NIC), arterial enhancement fraction (AEF) and extracellular volume (ECV) were calculated. Difference tests and spearman correlation were used to select quantitative parameters for DECT model building. Multivariate logistic analysis was used to build clinical model, DECT model and combined model.

Results: A total of 53 patients with locally advanced ESCC were enrolled in this study who received nCRT combined with surgery and underwent DECT examination before treatment. After spearman correlation analysis and multivariate logistic analysis, AEF and ECV showed significant roles between pCR and non-pCR groups. These two quantitative parameters were selected for DECT model. Multivariate logistic analysis revealed that LMR and RBC were also independent predictors in clinical model. The combined model showed the highest sensitivity, specificity, PPV and NPV compared to the clinical and DECT model. The AUC of the combined model is 0.893 (95%CI: 0.802-0.983). Delong's test revealed the combined model significantly different from clinical model (Z =-2.741, P = 0.006).

Conclusion: Dual-layer DECT derived ECV fraction and AEF are valuable predictors for pCR in ESCC patients after nCRT. The model combined DECT quantitative parameters and clinical features might be used as a non-invasive tool for individualized treatment decision of those ESCC patients. This study validates the role of DECT in pCR assessment for ESCC and a large external cohort is warranted to ensure the robustness of the proposed DECT evaluation criteria.