Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma

Wang, Yi; Tian, Weizhong; Tian, Shuangfeng; He, Liang; Xia, Jianguo; Zhang, Ji

doi:10.1186/s12880-023-01068-5

Cited by 2 publications

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study

Wang,

Shang,

Chen

et al. 2024

Medicine

View full text Add to dashboard Cite

To explore the feasibility and application value of texture analysis based on computed tomography (CT) for predicting the differentiation of esophageal squamous cell carcinoma (ESCC). Patients diagnosed with ESCC who underwent chest contrast-enhanced CT before treatment were selected. Based on the pathological results, the patients were stratified into poorly differentiated and moderately well-differentiated groups. FireVoxel software was used to analyze the region of interest based on venous phase CT images. Texture parameters including the mean, median, standard deviation (SD), inhomogeneity, skewness, kurtosis, and entropy were obtained automatically. Differences in the texture parameters and their relationship with the degree of differentiation between the 2 groups were analyzed. The value of CT texture parameters in identifying poor differentiation and moderate-well differentiation of esophageal cancer was analyzed using the ROC curve. A total of 48 patients with ESCC were included, including 24 patients in the poorly differentiated group and 24 patients in the moderate-well-differentiated group. There were negative correlations between SD, inhomogeneity, entropy, and the degree of differentiation of esophageal cancer (P < .05). The correlation of inhomogeneity was the highest (r = −0.505, P < .001). SD, inhomogeneity, and entropy could effectively distinguish between the poorly and moderately well-differentiated groups, with statistically significant differences between the 2 groups (P < .05). The best critical values for SD, inhomogeneity, and entropy were 17.538, 0.017, and 3.917, respectively. The areas under the ROC curve were 0.793, 0.792, and 0.729, respectively, with the SD and inhomogeneity being the best. The application of texture analysis on venous phase CT images holds promise as a method for forecasting the degree of differentiation in esophageal cancers, which could significantly contribute to the preoperative noninvasive evaluation of tumor differentiation.

show abstract

Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study

Wang,

Shang,

Chen

et al. 2024

Medicine

View full text Add to dashboard Cite

show abstract

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

Li,

Cui,

Yan

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose This study aimed to investigate whether dual layer detector dual energy CT (DECT) quantitative parameters and clinical features could predict pathological complete response(pCR) for esophageal squamous cell carcinoma (ESCC) patients who received neoadjuvant chemoradiotherapy (nCRT). Material and methods This study retrospective recruited local advanced ESCC patients who underwent nCRT followed by surgical treatment from December 2019 to January 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. The performance for those models were evaluated by sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver-operator curve (AUC). 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 spectral 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 predictor for pCR in ESCC patients after nCRT. The combined DECT quantitative parameters and clinical features model is aimed to provide guidance for those patients’ treatment.

show abstract

Spectral CT - a new supplementary method for preoperative assessment of pathological grades of esophageal squamous cell carcinoma

Cited by 2 publications

References 28 publications

Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study

Texture analysis based on CT for predicting the differentiation of esophageal squamous cancer: An observational study

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

Contact Info

Product

Resources

About