Li Miaomiao, Cui Yongbin, Yan Yuanyuan, Zhao Junfeng, Lin Xinjun, Liu Qianyu, Dong Shushan, Nie Mingming, Huang Yong, Li Baosheng, Yin Yong
Shandong University Cancer Center, Shandong University, Jinan, Shandong, China.
Shandong Medical College, Jinan, Shandong, China.
BMC Gastroenterol. 2025 May 10;25(1):357. doi: 10.1186/s12876-025-03964-2.
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.
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.
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).
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.
在新辅助放化疗（nCRT）后的食管癌鳞状细胞癌（ESCC）患者术前，尚无预测病理完全缓解（pCR）的金标准方法。本研究旨在探讨双层探测器双能CT（DECT）定量参数和临床特征能否预测nCRT后ESCC患者的pCR。
本研究回顾性纳入了2019年12月至2023年5月期间接受nCRT后行手术治疗的局部晚期ESCC患者。根据pCR状态（原发癌灶和淋巴结中无可见癌细胞），将患者分为pCR组（N = 25）和非pCR组（N = 28）。DECT定量参数来自常规CT图像、不同单能（MonoE）图像、虚拟平扫（VNC）图像、有效原子序数（Zeff）图像、碘浓度（IC）图像和电子密度（ED）图像。计算光谱曲线斜率（λHU）、归一化碘浓度（NIC）、动脉强化分数（AEF）和细胞外容积（ECV）。采用差异检验和Spearman相关性分析来选择用于DECT模型构建的定量参数。采用多因素逻辑回归分析构建临床模型、DECT模型和联合模型。
本研究共纳入53例局部晚期ESCC患者，他们接受了nCRT联合手术治疗，并在治疗前接受了DECT检查。经过Spearman相关性分析和多因素逻辑回归分析，AEF和ECV在pCR组和非pCR组之间显示出显著作用。选择这两个定量参数用于DECT模型。多因素逻辑回归分析显示，LMR和RBC在临床模型中也是独立预测因素。与临床模型和DECT模型相比，联合模型显示出最高的敏感性、特异性、阳性预测值和阴性预测值。联合模型的AUC为0.893（95%CI：0.802 - 0.983）。Delong检验显示联合模型与临床模型有显著差异（Z = -2.741，P = 0.006）。
双层DECT得出的ECV分数和AEF是nCRT后ESCC患者pCR的有价值预测因素。结合DECT定量参数和临床特征的模型可作为这些ESCC患者个体化治疗决策的非侵入性工具。本研究验证了DECT在ESCC患者pCR评估中的作用，需要一个大型外部队列来确保所提出的DECT评估标准的稳健性。
