Objective:To establish and validate a clinical and CT radiomics combined model for predicting lymph node metastasis (LNM) risk in patients with hilar cholangiocarcinoma (HCCA).Methods:This was a case-control study. Data from 158 pathologically confirmed HCCA patients between January 2016 and January 2022 at the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. Using stratified random sampling, the patients were randomly divided into a training set ( n=95) and an internal validation set ( n=63) at a 6∶4 ratio. According to postoperative pathology, 31 LNM-positive cases and 64 LNM-negative cases were in the training set, and 22 LNM-positive cases and 41 LNM-negative cases were in the internal validation set. A cohort of 50 HCCA patients was retrospectively collected from West China Hospital of Sichuan University between October 2018 and June 2021 as an external validation set, including 21 LNM-positive and 29 LNM-negative cases. Clinical features were selected by multivariate logistic regression analysis to establish a clinical model. Radiomics features were extracted from portal venous phase CT images using 3D Slicer software. A radiomics model was developed using the least absolute shrinkage and selection operator regression algorithm. A clinical-radiomics model was constructed by integrating clinical features and Radscore, and a nomogram was developed. The prediction performance of models was evaluated by the area under the receiver operating characteristic curve (AUC). The AUC values were compared using the DeLong test. Calibration curves and decision curves were plotted to assess calibration and clinical net benefit. Results:Clinical N (cN) staging was an independent risk factor for LNM ( OR=6.86, 95% CI 2.70-18.49, P<0.001). Totally 12 optimal features were selected to construct the radiomics model, and the clinical-radiomics nomogram model was constructed by combining cN staging and Radscore. In the external validation set, the AUC (95% CI) of the clinical model, radiomics model, and clinical-radiomics nomogram were 0.706 (0.576-0.836), 0.768 (0.637-0.899), and 0.803 (0.680-0.926), respectively. The nomogram achieved higher AUC than clinical and radiomics models with statistical significance ( Z=2.01, 2.21; P=0.044, 0.027). The calibration and decision curves demonstrated good model fit, providing clinical net benefits for patients. Conclusion:The clinical-radiomics nomogram model combining cN staging and CT radiomics features can effectively predict LNM risk in HCCA patients.

Objective To observe the reproducibility of radiomics feature(RF)extracted from virtual monoenergetic image(VMI)of rabbit VX2 hepatoma models obtained with 3 different dual-energy CT(DECT)systems,and to explore relationship of reproducibility and diagnostic performance of RF.Methods Fifteen rabbits with VX2 hepatoma were randomly divided into 3 groups(each n=5).Contrast-enhanced abdominal CT scanning under volume CT dose index(CTDIvol)levels of 6,9 and 12 mGy were performed with dual-source DECT(dsDECT),rapid kV switching DECT(rsDECT)and dual-layer detector DECT(dlDECT),respectively.VMI were reconstructed at 10 keV increments from 40 to 140 keV.RF were extracted from VMI,the reproducibility was assessed using intra-class correlation coefficient(ICC),and those with ICC≥0.8 were considered as reproducible RF.The percentage of reproducible features(denoted by R)were compared among different scanner pairings and different CTDIvol levels.Within each CTDIvol group,the reconstruction energy levels yielding the maximum number(denoted by N)of common RF across different scanner pairings were identified.The receiver operating characteristic(ROC)curve was drawn,the area under the curve(AUC)was calculated,and the diagnostic efficacies of reproducible RF and other RF were compared under optimal reproducible conditions.Spearman correlation coefficient between ICC and the corresponding AUC of RF were calculated.Results RrsDECT-dsDECT(6.45％,95％CI[2.36％,8.87％])was higher than RdlDECT-dsDECT(0.72％,95％CI[0.15％,1.79％])and RrsDECT-dlDECT(1.43％,95％CI[0.60％,4.06％])(all adjusted P＜0.05),R9mGy(3.70％,95％CI[1.31％,5.73％])and R12mGy(2.63％,95％CI[0.60％,6.69％])were higher than R6mGy(1.31％,95％CI[0.12％,1.55％])(all adjusted P＜0.05).The optimal reproducible reconstruction energy levels of RF under CTDIvol of 6,9 and 12 mGy concentrated at 50-70 keV.AUC of reproducible RFs were higher than of other RF(all adjusted P＜0.05)and had certain correlation with the reproducibility(rs=0.102-0.516,P＜0.05).Conclusion The reproducibility of RF extracted from contrast-enhanced VMI CT images of rabbit VX2 hepatoma models associated with DECT scanner,CTDIvol level and reconstruction energy level.RF with higher reproducibility might have better diagnostic performance.

Objective To investigate the value of CT radiomics combined with CT and preoperative pathological features for predicting postoperative early recurrence(ER)of local advanced esophageal squamous cell carcinoma(LAESCC).Methods Data of 334 patients with LAESCC were retrospectively analyzed.The patients were divided into training set(n=234)and verification set(n=100)at the ratio of 7:3 and were followed up to observe ER(recurrence within 12 months after surgery)or not.Univariate and multivariate logistic regression were used to analyze clinical,CT and preoperative pathological features of LAESCC in patients with or without ER in training set.The independent risk factors of ER were screened,and a CT-preoperative pathology model was constructed.Based on venous phase CT in training set,the radiomics features of lesions were extracted and screened to establish radiomics model,and finally a combined model was established based on radiomics model and the independent risk factors.Receiver operating characteristic(ROC)curves were drawn,and the area under the curve(AUC)was calculated to evaluate the diagnostic efficacy of each model.Results Among 334 cases,168 were found with but 166 without ER.In training set,117 cases were found with while the rest 117 without ER,while in verification set,51 were found with but 49 without ER.The length of lesions,cT stage and cN stage shown on CT and tumor differentiation degree displayed with preoperative pathology were all independent risk factors for ER of LAESCC(all P＜0.05).The AUC of CT-preoperative pathology model in training set and validation set was 0.759 and 0.783,respectively.Ten best radiomics features of LAESCC were selected,and AUC of the established radiomics model in training set and validation set was 0.770 and 0.730,respectively.The AUC of combined model in training and validation set was 0.838 and 0.826,respectively.The AUC of CT radiomics combined with CT and preoperative pathological features in training set was higher than that of CT-preoperative pathologymodel and radiomics model(both P＜0.01).Conclusion CT radiomics combined with CT and preoperative pathological features could effectively predict postoperative ER of LAESCC.

Objective:To investigate the predictive model construction of anastomotic thickening character after radical surgery of esophageal cancer based on computed tomogralphy(CT) radiomics and its application value.Methods:The retrospective cohort study was conducted. The clinicopathological data of 202 patients with esophageal squamous cell carcinoma (ESCC) who were admitted to The First Affiliated Hospital of Zhengzhou University from January 2013 to June 2021 were collected. There were 147 males and 55 females, aged (63±8) years. Based on random number table, 202 patients were assigned into training dataset and validation dataset at a ratio of 7:3, including 141 cases and 61 cases respectively. Patients underwent radical resection of ESCC and enhanced CT examination. Observation indicators: (1) influencing factor analysis of malignant anas-tomotic thickening; (2) construction and evaluation of predictive model; (3) performance comparison of 3 predictive models. The normality of continuous variables was tested by Kolmogorov-Smirnov method. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was analyzed using the t test. Measurement data with skewed distribution were represented as M( Q1, Q3), and comparison between groups was analyzed using the Mann-Whintney U test. Count data were represented as absolute numbers, and comparison between groups was analyzed using the chi-square test or Fisher's exact probability. The consistency between subjective CT features by two doctors and measured CT numeric variables was analyzed by Kappa test and intraclass correlation coefficient (ICC), with Kappa >0.6 and ICC >0.6 as good consistency. Univariate analysis was conducted by corresponding statistic methods. Multivariate analysis was conducted by Logistics stepwise regression model. The receiver operating characteristic (ROC) curve was drawn, and area under curve (AUC), Delong test, decision curve were used to evaluate the diagnostic efficiency and clinical applicability of model. Results:(1) Influencing factor analysis of malignant anastomotic thickening. Of the 202 ESCC patients, 97 cases had malignant anastomotic thickening and 105 cases had inflammatory anastomotic thickening. The consistency between subjective CT features by two doctors and measured CT numeric variables showed Kappa and ICC values >0.6. Results of multivariate analysis showed that the maximum thickness of anastomosis and CT enhancement pattern were independent influencing factors for malignant anastomotic thickening[ hazard ratio=1.46, 3.09, 95% confidence interval ( CI) as 1.26-1.71,1.18-8.12, P<0.05]. (2) Construction and evaluation of predictive model. ① Clinical predictive model. The maximum thickness of anasto-mosis and CT enhancement pattern were used to construct a clinical predictive model. ROC curve of the clinical predictive model showed an AUC, accuracy, sensitivity, specificity as 0.86 (95% CI as 0.80-0.92),0.77, 0.77, 0.80 for the training dataset, and 0.78 (95% CI as 0.65-0.89), 0.77, 0.77, 0.80 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=1.22, P>0.05). ② Radiomics predictive model. A total of 854 radiomics features were extracted and 2 radiomics features (wavelet-LL_first order_ Maximum and original_shape_VoxelVolume) were finally screened out to construct a radiomics predictive model. ROC curve of the radiomics predictive model showed an AUC, accuracy, sensitivity, specificity as 0.87 (95% CI as 0.81-0.93), 0.80, 0.75, 0.86 for the training dataset, and 0.73 (95% CI as 0.63-0.83), 0.80, 0.76, 0.94 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=-0.25, P>0.05). ③ Combined predictive model. Results of multivariate analysis and radiomics features were used to construct a combined predictive model. ROC curve of the combined predictive model showed an AUC, accuracy, sensitivity, specificity as 0.93 (95% CI as 0.89-0.97),0.84, 0.90, 0.84 for the training dataset, and 0.79 (95% CI as 0.70-0.88), 0.89, 0.86, 0.91 for the validation dataset, respectively. Results of Delong test showed no significant difference in AUC between the training dataset and validation dataset ( Z=0.22, P>0.05). (3) Performance comparison of 3 predictive models. Results of Hosmer-Lemeshow goodness-of-fit test showed that the clinical predictive model, radiomics predictive model and combined predictive model had a good fitting degree ( χ2=4.88, 7.95, 4.85, P>0.05). Delong test showed a significant difference in AUC between the combined predictive model and clinical predictive model, also between the combined predictive model and radiomics predictive model ( Z=2.88, 2.51, P<0.05 ). There was no significant difference in AUC between the clinical predictive model and radiomics predictive model ( Z=-0.32, P>0.05). The calibration curve showed a good predictive performance in the combined predictive model. The decision curve showed a higher distinguishing performance for anastomotic thickening character in the combined predictive model than in the clinical predictive model or radiomics predictive model. Conclusions:The maximum thickness of anastomosis and CT enhancement pattern are independent influencing factors for malignant anastomotic thickening. Radiomics predictive model can distinguish the benign from malignant thickening of anastomosis. Combined predictive model has the best diagnostic efficacy.

Objective:To explore the value of multiphasic CT-based radiomics signature in predicting the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN).Methods:The multiphasic CT images of patients with pSPN confirmed by postoperative pathology in the First Affiliated Hospital of Zhengzhou University from January 2012 to January 2021 were analyzed retrospectively. There were 23 cases of invasiveness and 59 cases of non-invasiveness. The region of interest(ROI) was artificially delineated layer by layer in the plain scan, arterial-phase and venous-phase images, respectively. The 1 316 image features were extracted from each ROI. The data set was divided into training and validation sets with a ratio of 7∶3 by stratified random sampling, and synthetic minority oversampling technique (SMOTE) algorithm was used for oversampling in the training set to generate invasive and non-invasive balanced data for building the training model. The constructed model was validated in the validation set. The receiver operating characteristic(ROC) analysis was used to evaluate model performance and the Delong′s test was applied to compare the area under the ROC curve (AUC) of different predict models. The improvement for classification efficiency of each independent model or their combinations were also assessed by net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices.Results:After feature extraction, 2, 6 and 3 features were retained to construct plain-scanned model, arterial-phase and venous-phase models, respectively. Seven independent-phase and combined-phase models were established. Except the plain-scanned model, the AUC values of other models were greater than 0.800. The arterial-phase model had the best efficiency for classification among all independent-phase models. The AUC values of arterial-phase model in the SMOTE training and validation sets were 0.913 and 0.873, respectively. By combining the radiomics signature of the arterial-phase and venous-phase models, the AUC values of training and validation sets increased to 0.934 and 0.913 respectively. There were no significant differences of the AUC values between the scan-arterial venous-phase model and arterial venous-phase model in both training and validation sets (both P>0.05). The NRI and IDI indexes showed that the combined form of plain-scan model and arterial-venous-phase model could not significantly improve the classification efficiency in the validation set (both NRI and IDI<0). Conclusions:The arterial-phase CT-based radiomics model has a good predictive performance in the invasive behavior of pSPN, and the combination with a venous-phase radiomics model can further improve the model performance.

Objective:To investigate the efficiency of deep learning image reconstruction (DLIR) algorithm in the image quality and detection of hypovascular hepatic metastases under low radiation doses in comparison with adaptive statistical iterative construction-V (ASiR-V).Methods:Fifty-six patients with suspected hypovascular hepatic metastases who needed abdominal enhanced CT scans were collected prospectively in the First Affiliated Hospital of Zhengzhou University from January to April 2021. The patients received conventional radiation dose with tube current-time products of 400 mA CT scans in the first venous phase, low-dose CT scans in the second venous phase, which were set as tube current-time products of 280 mA for group A (19 cases), 200 mA for group B (19 cases) and 120 mA for group C (18 case), respectively. The images of first venous phase and 3 groups of second venous phase were both reconstructed with ASiR-V60% and high-DLIR (DLIR-H). Quantitative parameters [image noise, liver and portal vein signal to noise ratio (SNR), contrast to noise ratio (CNR)] and qualitative parameters (overall image quality, lesion conspicuity, diagnostic confidence) were compared between ASiR-V60% and DLIR-H images, and the effective radiation dose (ED) and the lesion detectability of each group was recorded. The paired t test was used to compare quantitative parameters, whereas the Wilcoxon signed-rank test of paired data was used to compare qualitative parameters. Results:In the second venous phase, ED was (5.56±0.35) mSv in group A, (3.88±0.23) mSv in group B, and (2.42±0.23) mSv in group C, with a decrease of 30%, 50% and 70% compared with the first venous phase, respectively. Moreover, with the decrease of radiation dose, the noise gradually increased, and the CNR lesions, SNR liver and SNR portal vein all gradually decreased. DLIR-H images had statistically better quantitative scores than ASiR-V60% images when the same radiation dose was applied (all P<0.001). Furthermore, the qualitative parameters of each group decreased with the decrease of radiation dose. Under the same radiation dose, the overall image quality, lesion conspicuity and diagnostic confidence of DLIR-H were higher than those of ASiR-V60% (all P<0.001). All lesions [100% (84/84)] were detected by ASIR-V60% and DLIR-H in group A, 92.0% (75/81) in group B, and 88.0% (79/89) in group C. Conclusions:Compared with ASiR-V60%, DLIR-H could reduce image noise, improve overall image quality and lesion conspicuity of hypovascular hepatic metastases as well as increase diagnostic confidence under different radiation doses.

Objective:To investigate the application value of spectral computed tomo-graphy (CT) multi-parameter imaging in predicting gastric cancer lymph node metastasis.Methods:The retrospective case-control study was conducted. The clinicopathological data of 86 patients with gastric cancer who were admitted to the First Affiliated Hospital of Zhengzhou University from January 2014 to June 2017 were collected. There were 53 males and 33 females, aged from 22 to 87 years, with a median age of 53 years. All patients received abdominal plain scan and arterial and venous phase contrast spectral scan. Images of plain scan, 70 keV monochromatic and iodine-based images in arterial and venous phase were analyzed on post-processing working station. Observation indicators: (1) gastric cancer lymph node metastasis; (2) analysis of influencing factors for lymph node metastasis in gastric cancer; (3) introduction of special cases. Count data were described as absolute numbers. Univariate analysis was performed using the chi-square test or rank sum test. Multivariate analysis was conducted using the Logistic regression model.Results:(1) Gastric cancer lymph node metastasis: of the 86 patients, 64 cases had lymph nodes metastasis and 22 had no lymph nodes metastasis. (2) Analysis of influencing factors for lymph node metastasis in gastric cancer: results of univariate analysis showed that tumor growth pattern, tumor diameter, infiltration of peritumor fat, CT value in arterial phase, CT value in venous phase, iodine value in venous phase were related factors affecting lymph nodes metastasis in gastric cancer ( χ2=6.753, Z=-3.180, χ2=7.649, Z=-2.051, -2.971, -2.547, P<0.05). Results of multivariate analysis showed that infiltration of peritumor fat and the iodine value in venous phase were greater than 12(100 μg/cm 3) and not greater than 16(100 μg/cm 3), or greater than 16(100 μg/cm 3) were independent risk factors affecting lymph nodes metastasis in gastric cancer ( odds ratio=13.154, 3.761, 7.583, 95% confidence interval as 2.597-66.620, 1.893-8.572, 4.769-16.692, P<0.05). (3) Introduction of special cases: case 1 was male, aged 46 years. Results of preoperative spectral CT enhanced scan showed gastric antrum space occupying lesion combined with enlarged lymph nodes. During enhancement arterial phase, spectral CT 70 keV monochromatic images and corresponding iodine-based images of primary lesion layer in the transverse view showed gastric wall thickening with mild to moderate enhancement, clear fat space in serosa and enlarged lymph nodes in lesser curvature. The spectral CT 70 keV monochromatic images and corresponding iodine-based images below pylorus level in the transverse view showed subpyloric enlarged lymph nodes. During enhancement venous phase, the 70 keV monochromatic images and corresponding iodine-based images of primary lesion layer in the transverse view showed layered enhancement of gastric antrum lesions and mucosal enhancement, with a high iodine value. The patient was diagnosed as gastric antrum cancer with lymph node metastasis, no serosal or peritumoral fat invasion. Results of postoperative pathological examination showed moderately differentiated adenocarcinoma of gastric antrum with serosal invasion and lymph node metastasis. Case 2 was male, aged 53 years. Results of preoperative spectral CT enhanced scan showed gastric cancer of lesser curvature combined with enlarged lymph nodes. During enhancement arterial phase, 70 keV monochromatic images and corresponding iodine-based images of primary lesion layer in the transverse view showed gastric wall heterogeneous thickening of lesser curvature, with moderate enhancement, obscure peritumor fat space, unclear serosa, and multiple enlarged lymph nodes in lesser curvature. During enhancement venous phase, 70 keV monochromatic images in the transverse view showed unclear boundary between lesions and enlarged lymph nodes in lesser curvature, obscure peritumor fat. During enhancement arterial phase, 70 keV monochromatic images of celiac trunk layer in the transverse view showed parasplenic artery lymph nodes, with circular enhancement and no enhancement in central necrotic elements. The patient was diagnosed as gastric cancer of lesser curvature with lymph node metastasis, serosal and peritumor fat invasion. Results of postoperative pathological examination showed poorly differentiated adenocarcinoma of gastric antrum with serosal invasion and lymph node metastasis. Conclusion:The infiltration of peritumor fat and iodine value in venous phase are independent factors affecting gastric cancer lymph node metastasis.

Objective: To explore the value of specific spectral CT in evaluating antiangiogenic efficacy of Axitinib in rabbit VX2 liver tumors. Methods: Forty VX2 liver tumor-bearing rabbits were randomly assigned into study group (n=25, given Axitinib through intragastric administration for 14 days) and control group (n=15, given equal volume of normal saline through intragastric administration for 14 days). Rabbits in study group were scanned with one-stop perfusion and spectral CT mode, while in control group underwent arterial-phase spectral CT scan and perfusion CT scan, sequentially. The optimal scan time for control group was the maximum tumor contrast time based on the time-intensity curve of perfusion CT data of study group. The correlation coefficients of tumor perfusion parameters and the normalized iodine concentration (NIC) at different phases in study group were compared. Correlations between imaging features and vascular endothelial growth factor receptor-2 (VEGFR-2), and the differences among correlation coefficients were analyzed. Results: In control group, 73.33% (11/15) tumors achieved maximum contrast by using the optimal arterial scan time ([18±4]s) acquired from study group. The tumor blood flow (BF), blood volume (BV) and hepatic arterial fraction (HAF) had positive correlations with NIC. No statistical difference of correlation coefficients acquired in the optimal scan time and in the maximum correlation was found except for tumor BV. The tumor BF (r=0.828, P=0.008) and NIC (r=0.820, P=0.010) were positively correlated with VEGFR-2,while PS was negatively correlated with VEGFR-2 (r=-0.782, P=0.010), no statistical difference was found among the above three correlation coefficients (P>0.05). Conclusion: Single-phase contrast-enhanced spectral CT scan at tumor-specific and individual-specific acquisition time might be able to replace perfusion CT in evaluating antiangiogenic efficacy of Axitinib in rabbit VX2 liver tumors.

Objective To investigate the image quality and radiation dose of spectral CT with automatic spectral imaging mode selection (ASIS) and adaptive statistical iterative reconstruction (ASIR) at abdominal CT as compared with low-kVp CT with ASIR.Methods Two hundred patients underwent the plain and arterial-phase (AP) and portal venous-phase (PVP) contrast-enhanced abdominal CT scan were analyzed prospectively.The patients were randomly assigned to the control group (low-kVp CT scan) and study group (spectral CT scan),if BMI ≤ 23.9 kg/m2,patients were assigned into group A (80 kVp) or group B (spectral CT with ASIS);if 24.0 kg/m2 ≤ BMI＜28.9 kg/m2,patients were assigned into group C (100 kVp) or group D (spectral CT with ASIS)(n=50 each).Groups A and B,groups C and D were matched by gender,age,body mass index,cross sectional area and contrast agent dose respectively.Low-kVp images and monochromatic images (40 to 60 keV) were all reconstructed by using ASIR.Radiation dose and quantitative parameters (image noise in HU and contrast-to-noise ratio of the liver,aorta and portal vein) were compared by using t test while overall image quality was assessed by Mann-Whitney U test between the matched groups.Results Radiation dose in the group B was significantly higher than group A (increased by 10％,P＜0.05) but there was no significant differences between groups C and D (P＞0.05).Compared to the control group,image noises in the study group were higher at 40 keV and 50 keV (P＜0.05),but similar at 40 keV (P＞0.05).At the energy level of 40 keV,the study group showed higher CNRs,but lower overall image quality scores than the control group except for the similar image quality scores between groups A and B during AP (P＜0.05).At the energy level of 50 keV,the CNRs and overall image quality scores in the study group were higher than or similar to the control group.At the energy level of 60 keV,the study group showed lower or similar CNRs,but higher or similar overall image quality scores compared with the control group.Conclusions The radiation dose of spectral CT with ASIS technique was slightly higher than 80 kVp CT but similar to 100 kVp CT.By combining ASIR technique,monochromatic image at 50 keV can maintain or improve CNR and overall image quality as compared with low-kVp images.

Objective To investigate the clinical feasibility of spectral monochromatic imaging combined with adaptive statistical iterative reconstruction-V (ASIR-V) in upper abdominal enhanced scanning with double-low technique for severe liver cirrhosis.Methods Totally 126 cases performed abdominal contrast-enhanced CT scanning were collected prospectively and assigned to 3 groups with 42 cases in each group with random number table method.The filtered back projection algarithm,120 kV and contrast agent dose of 420 mgI/kg were used for the cases of control group.The cases for spectral group and combined group were scanned with spectral imaging mode and contrast agent dose of 300 mgI/kg.The 60 keV monochromatic images combined with pre-0％ and post-40％ ASIR-V were selected and analyzed for spectral group.Combined group with pre-40％ ASIR-V was divided into 2 subgroups with 50 keV,post -50％ ASIR-V and 60 keV,post-40％ ASIR-V separately.One-way ANOVA test was used for analysis of radiation dose and quantitative parameters,and Rank sum test was used for image subject evaluation.Results The CT numbers and CNR had significant differences among spectral group,combined group and control group(F=4.293-13.134,P＜0.05) except for the images of liver parenchyma in PVP and that of 50 keV combined 50％ASIR-V group were higher than that of control group (q=1.825-3.736,P＜0.05).No significant differences existed for image noise and overall image quality scores of organs in four groups.The visualization of hepatic vascular branches in 50 keV combined 50％ASIR-V group was higher than that of other three groups(Z=2.793-6.328,P＜0.05).The radiation dose of combined group was lower than that of spectral and control group (q =-4.879--2.531,P ＜ 0.001).Conclusions Spectral monochromatic imaging combined with pre-and post-ASIR-V can reduce contrast agent dose and radiation dose without degrading image quality for the severe liver cirrhosis in upper abdominal enhanced scanning.