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Objective:To explore the clinical value of quantitative parameters on spectral CT in predicting the invasiveness of lung adenocarcinoma manifesting as ground-glass nodules (GGN).Methods:The clinical and imaging data of 129 patients with pathologically confirmed lung adenocarcinoma who were surgically resected in the First Affiliated Hospital of Zhengzhou University from March to October 2022 were retrospectively analyzed, including 45 males and 84 females, aged from 33 to 81. According to the pathological results, they were divided into the minimally invasive adenocarcinoma (MIA) group ( n=64) and the invasive adenocarcinoma (IAC) group ( n=65). All patients underwent enhanced spectral CT within two weeks before surgery. The iodine density map, Z-Effective (Z eff) map, and electron density (ED) map were reconstructed on the post-processing workstation, and the spectral parameters, including normalized iodine concentration (NIC), arterial enhancement fraction (AEF), Z eff, and ED were measured and calculated. Conventional CT features were analyzed, including maximum diameter, CT value, nodule types, margin, lobulation sign, spiculation sign, bubble sign, pleural retraction sign, abnormal vascular sign, and air bronchial sign. The clinical features, conventional CT characteristics and spectral CT parameters of two groups were compared using the independent sample t test, the Mann-Whitney U test, and the χ 2 test. Multivariate logistic regression analysis was used to evaluate the independent risk factors of lung adenocarcinoma invasiveness, and the model was constructed. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the performance of parameters and models in predicting the invasiveness of lung adenocarcinoma. Results:There were significant differences between the MIA group and IAC group in maximum diameter, CT value, nodule type, margin, spiculation sign, pleural retraction sign, air bronchial sign, venous phase NIC, AEF, venous phase Z eff, arterial phase ED, venous phase ED ( P<0.05). Multivariate logistic regression analysis showed that the maximum diameter (OR=1.183, 95%CI 1.062-1.318), CT value (OR=1.004, 95%CI 1.001-1.007), venous phase NIC (OR=1.185, 95%CI 1.083-1.298), AEF(OR=0.975, 95%CI 0.957-0.994), venous phase Z eff (OR=0.031, 95%CI 0.005-0.196) were independent influence factors for the invasiveness of lung adenocarcinoma. The conventional CT model was established with the maximum diameter and CT value, and the spectral CT model was established with venous phase NIC, AEF, and venous phase Z eff. The combined model was established with all the parameters above. Areas under the ROC curve of the conventional CT model, the spectral CT model, and the combined model for predicting the invasiveness of lung adenocarcinoma were 0.828, 0.854, and 0.902, respectively. Conclusion:The quantitative parameters of double-layer detector spectral CT can be used as an indicator to predict the invasiveness of lung adenocarcinoma manifesting as GGN, and AEF has the highest diagnostic efficacy. Spectral CT combined with conventional CT features can further improve the diagnostic efficiency.
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Objective:To investigate the impact of artificial intelligence imaging optimization technique on the image quality and radiation dose of low-dose chest CT scan.Methods:Eighty patients who underwent chest CT examination in the Jilin University 1st hospital from July to August, 2019 were randomly divided into two groups(A, B), with 40 patients in each. The voltage of group A was 100 kV, while the other was 120 kV. According to different reconstruction method , group A was divided into two subgroups, group A1 and group A2. The images of A1 were reconstructed by iterative algorithm (ClearView 50%), while A2 images were optimized A1 by NeuAI imaging optimization technique. Group B used iterative algorithm (ClearView 50%) to reconstruct the image. The CT dose index (CTDI vol), dose-length product (DLP) and effective radiation dose ( E) of group A and group B were recorded and compared.Objective the evaluation indicators were CT noise (SD), signal-to-noise ratio (SNR) and comparative noise ratio (CNR) of ROI. Subjective evaluation was done by 2 chief radiologists using double-blind method and image quality was graded by 5-point Likert scale. Results:The patient characteristics between group A and group B showed no significant differences( P>0.05). Compared with group B, the effective radiation dose in group A was reduced by 72.1% [(1.48±0.49) mSv vs. (5.30±1.40) mSv]. The SD in group A1 was higher than that in group B, while SNR and CNR were lower ( ZSD=-4.24, ZSNR=-2.54, tCNR=-2.27, P<0.05). The SD in group A2 was significantly lower than that in group B ( ZSD=-28.24, P<0.001), and SNR and CNR were significantly higher than that in group B ( tSNR=-26.04, tCNR=-36.88, P<0.001). There was no significant difference in subjective scores of image noise between group A2 and group B, while subjective scores of lung structure in group B were better than those in group A2( χ2=4.96、7.04, P<0.05). Conclusions:Although the radiation dose was reduced by 72.1%, the low-dose chest CT images optimized by AI could reach the image quality level of standard dose.
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Objective:To investigate the benefits of artificial intelligence (AI)-based image optimization technique on image quality of coronary CT angiography (CCTA).Methods:Sixty patients, who were referred for CCTA, were prospectively enrolled between May and June 2018 in Peking Union Medical College Hospital and were randomly divided into two groups. Group A was scanned with a low tube voltage of 80 kVp and a reduced contrast media volume of lopamiro at 0.7 ml /kg and group B was scanned with a standard 120 kVp tube voltage and an injection of 70 ml lopamiro. According to the different reconstruction methods, group A was divided into two subgroups. The images of group A1 were reconstructed with iterative reconstruction (IR). IR and further AI-based image optimization were used in group A2. Group B was also reconstructed by IR. To evaluate image quality objectively, the mean attenuation of contrast-enhancement values, background noise, signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured and calculated in the region of interests (ROIs) of the aortic root (Ao), left main coronary artery (LM), left anterior descending branch (LAD), left circumflex branch (LCX) and right coronary artery (RCA), respectively. In addition, the subjective evaluation was performed by two radiologists using Likert 4 scale (1 for excellent and 4 for poor) to evaluate the image quality of coronary artery branches and segments. The estimated radiation dose in terms of volume CT dose index (CTDI vol), dose length product (DLP) and effective dose (ED) was recorded and compared between group A and group B. Analyses of the differences between groups were compared with image quality, radiation dose by t test or Wilcoxon signed ranks test, and subjective assessments were compares with χ 2 test. Results:In terms of lumen enhancement, compared to group A2, there was no significant difference in CT value of each ROI ( P>0.05); CT value of group A1 and group A2 at Ao was significantly higher than that of group B ( P<0.01), but there was no significant difference in other ROI ( P>0.05). By comparing noise, SNR and CNR, it could be seen that compared to group B, A2 group optimized by AI had a significantly lower noise level at Ao than group B ( P<0.001), and there was no statistical difference in ROI for the rest (all P>0.05).SNR at Ao was significantly higher than that of group B ( P<0.001), and there was no statistical difference in ROI for the rest ( P>0.05).However, the CNR of group A2 was significantly higher than that of group B in all ROI ( P<0.001). Compared to the AI-optimized A2 group, the noise of A2 group was significantly lower than that of A1 group at all ROI, and SNR and CNR were significantly higher than that of A1 group ( P<0.001). The subjective evaluation results of coronary segments showed that image quality of group A2 and group B was significantly better than that of group A1 ( P=0.002,0.038). There was no significant difference between group A2 and group B ( P=0.543). The radiation dose indexes of CTDI vol, DLP and ED in group A were significantly lower than those in group B (all P<0.001). The ED was decreased by 70.4%. Meanwhile, the volume of contrast media in group A was reduced by 37.1% than that that in group B. Conclusion:Compared to conventional scanning, CCTA images optimized by AI technology improved subjective and objective image quality.
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Objective To provide the administrative departments in the health sector with a method for estimating hospital beds when making regional health plans. Methods The method of general surveys was adopted to make relevant investigation on all the cities and districts in Shandong Province. Data collected were estimated by borrowed and self-developed formulas. Results By 2005, the total number of hospital beds in the province will be reduced by 5.19%, the number of beds in the city seats will be reduced by 4.97%, the number of beds in the county seats will be reduced by 1.03%, and the number of beds in the township seats will be reduced by 9.68%. Conclusion The findings of the study have filled in a gap at present when there is a lack of estimating methods. They are viable and yet still need to be polularized and perfected in practice.