The diagnostic value of quantitative parameters on dual-layer detector-based spectral CT in identifying ischaemic stroke

Diagnostic Value of Carotid Plaque Assessment with AIS Based on Quantitative Parameters of Dual-Layer Detector Spectral CT

Purpose

To investigate the quantitative assessment of carotid plaque by each parameter of dual-layer detector spectral CT and its diagnostic value in patients with acute cerebral infarction.

Patients and Methods

Eighty-three patients with carotid atherosclerotic plaques who underwent spectral CT scanning were retrospectively included. Forty-two patients with acute ischaemic stroke (AIS) were included in the study group, and 41 patients without AIS were included in the control group. We compared the detection of carotid plaques in the two groups and the differences in the spectral quantitative parameters of the plaques in the two groups, and their diagnostic efficacy was obtained.

Results

The detection rate of carotid plaques in the AIS group was higher than that in the non-AIS group (p<0.05); the carotid plaques in the AIS group mainly consisted of non-calcified plaques, while those in the non-AIS group mainly consisted of calcified plaques. The effective atomic number (Zeff), slope of the energy spectrum curve (λH), electron density (ED), and iodine-no-water value of the carotid plaques in the AIS group were lower than those in the non-AIS (p<0.05). For the differentiation of the carotid plaques in the AIS group from those in the non-AIS group, the area under the curve (AUC) of Zeff amounted to 0.637 (cut-off value: 11.865; sensitivity: 72.5%; specificity: 56.2%), the AUC of λH amounted to 0.628 (cut-off value: 19.56; sensitivity: 76.3%; specificity: 51.6%), and that for ED amounted to 0.624 (cut-off value: 110.45; sensitivity: 60.0%; specificity: 64.1%), AUC of iodine-no-water value amounted to 0.645 (cut-off value: 9.125; sensitivity: 61.3%; specificity: 65.6%).

Conclusion

In summary, the quantitative parameters of dual-layer detector spectral CT can be used to assess plaque stability and have certain value in the diagnosis of AIS. The quantitative parameters can effectively differentiate carotid plaques in AIS and non-AIS patients.

Quantitative parameters of dual-layer spectral detector computed tomography for evaluating Ki-67 and human epidermal growth factor receptor 2 expression in colorectal adenocarcinoma

Background

Ki-67 and human epidermal growth factor receptor 2 (HER2) are key biomarkers in evaluating the prognosis of colorectal adenocarcinoma (CRAC). The purpose of this study was to investigate the value of quantitative parameters in dual-layer spectral detector computed tomography (SDCT) for evaluating the expression of Ki-67 and HER2 in CRAC.

Methods

In this retrospective, cross-sectional study, 88 eligible patients with pathologically confirmed CRAC were selected from Taicang Hospital of Traditional Chinese Medicine between May 2021 and April 2023. The study participants underwent enhanced SDCT of the whole abdomen within 2 weeks before to surgery, did not receive antitumor therapy, and had complete immunohistochemical (IHC) indexes. Patients with nonadenocarcinoma pathologic types, poor quality of spectral CT images, or no complete immunohistochemistry results were excluded. Spectral parameters including CT values at 40 and 100 keV, effective atomic number, iodine concentration (IC), the slope of the spectral Hounsfield unit (HU) curve (λHU), and normalized iodine concentration (NIC) in the arterial phase (AP) and venous phase (VP) were analyzed for their value in distinguishing between the high and low expression of Ki-67 and HER2-positive and -negative status in CRAC. The statistical significance of the SDCT parameters between the different groups of Ki-67 expression and those of HER2 status was assessed with the Mann-Whitney test. Spearman correlation analysis was used to analyze the correlation between the SDCT parameters and the extent of Ki-67 expression and HER2 expression status. The receiver operating characteristic (ROC) curve was used, and the area under the curve (AUC) was calculated.

Results

The SDCT parameters of CT values at 40 keV, effective atomic number, IC, and the λHU in the VP showed significant differences between the Ki-67 high- and low-expression groups in CRAC (P=0.035, P=0.041, P=0.036, and P=0.044, respectively), with AUCs of 0.639 [95% confidence interval (CI): 0.512-0.766], 0.634 (95% CI: 0.508-0.761), 0.638 (95% CI: 0.510-0.766), and 0.633 (95% CI: 0.504-0.762), respectively. The expression of CRAC Ki-67 was positively correlated with CT values at 40 keV (r=0.227; P=0.034), effective atomic number (r=0.219; P=0.040), IC (r=0.225; P=0.035), and the λHU in VP (r=0.216; P=0.043). SDCT parameter values showed no statistical difference between negative and positive expression in HER2 (all P values >0.05). There was no significant correlation between SDCT parameters and the expression of HER2 in CRAC (all P values >0.05).

Conclusions

The quantitative parameters of SDCT in the VP provide valuable information for distinguishing between the low expression and high expression of Ki-67 in CRAC.

Nomogram Based on Dual-Layer Spectral Detector CTA Parameter for the Prediction of Infarct Core in Patients with Acute Ischemic Stroke

(1) Background: Acute ischemic stroke (AIS) is time-sensitive. The accurate identification of the infarct core and penumbra areas in AIS patients is an important basis for formulating treatment plans, and is the key to dual-layer spectral detector computed tomography angiography (DLCTA), a safer and more accurate diagnostic method for AIS that will replace computed tomography perfusion (CTP) in the future. Thus, this study aimed to investigate the value of DLCTA in differentiating infarct core from penumbra in patients with AIS to establish a nomogram combined with spectral computed tomography (CT) parameters for predicting the infarct core and performing multi-angle evaluation. (2) Methods: Data for 102 patients with AIS were retrospectively collected. All patients underwent DLCTA and CTP. The patients were divided into the non-infarct core group and the infarct core group, using CTP as the reference. Multivariate logistic regression analysis was used to screen predictors related to the infarct core and establish a nomogram model. The receiver operating characteristic (ROC) curve, the calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive efficacy, accuracy, and clinical practicability of the model, respectively. (3) Results: Multivariate logistic analysis identified three independent predictors: iodine density (OR: 0.022, 95% CI: 0.003-0.170, p < 0.001), hypertension (OR: 7.179, 95% CI: 1.766-29.186, p = 0.006), and triglycerides (OR: 0.255, 95% CI: 0.109-0.594, p = 0.002). The AUC-ROC of the nomogram was 0.913. Calibration was good. Decision curve analysis was clinically useful. (4) Conclusions: The spectral CT parameters, specifically iodine density values, effectively differentiate between the infarct core and penumbra areas in patients with AIS. The nomogram, based on iodine density values, showed strong predictive power, discrimination, and clinical utility to accurately predict infarct core in AIS patients.