Objective To assess the dose reduction potential of adaptive statistical iterative reconstruction(ASiR)and model-based iterative reconstruction(MBIR)in pelvic CT with a standard male phantom.Methods A Fluke Biomedical RANDO standard male phantom was scanned with discovery CT750 HD using different tube currents.CT images were reconstructed with FBP,50％ASiR and MBIR.The CT value,the image noise and the contrast-to-noise ratio(CNR)for the sacral vertebra relative to muscle were measured.The volume CT dose indexes(CTDIvo1)and dose-length product(DLP)were recorded.Results Compared with FBP,using 50％ASiR and MBIR had significant reduced image noise and greater CNR.The effective minimal tube currents for displaying sacral vertebra were 250 mA(FBP),180 mA(50％ASiR),and 100 mA(MBIR).With the similar image quality using FBP,the dose was reduced by 28.0％ and 59.9％ using 50％ASiR and MBIR,respectively.Conclusions Using advanced iterative algorithms can reduce image noise,improve CNR,and reduce the radiation dose in pelvic CT examination.

Objective To explore the diagnostic performance of 64-slice spiral CT combined with exercise treadmill test for coronary artery disease ( CAD). Methods Eighty-six patients suspected of CAD were divided into low risk group, intermediate risk group and high risk group according to estimated pretest probabilities of CAD. All patients underwent coronary angiography, 64-slice spiral CT and exercise treadmill test. With coronary artery stenosis rate >50% as positive findings, the sensitivity, specificity, positive predictive value ( PPV), negative predictive value ( NPV) and accuracy of 64-slice spiral CT and 64-slice spiral CT combined with exercise treadmill test in diagnosis of CAD were calculated. Results With coronary angiography as the "golden criteria", the sensitivity, specificity, PPV, NPV and accuracy of 64-slice spiral CT in diagnosis of CAD were 95.2% , 88.6% , 88.9% , 95.1% and 91.9% , respectively. The sensitivity of low risk group, intermediate risk group and high risk group was 100% , 100% and 92.6%, specificity was 94.4% , 94.1% and 66.7%, PPV was 80.0%, 91.7% and 89.3%, NPV was 100% , 100% and 75.0%, and accuracy was 95.5% , 96.4% and 86.1%, respectively. The sensitivity, specificity, PPV, NPV and accuracy of 64-slice spiral CT combined with exercise treadmill test in diagnosis of CAD were 97.6%, 97.7%, 97.6%, 97.7% and 97.7%, respectively. Conclusion 64-slice spiral CT combined with exercise treadmill test works well in screening CAD, especially for those with a low or intermediate estimated pretest probability.

Objective: To investigate the feasibility and value of preoperative prediction of pathological grade of hepatocellular carcinoma (HCC) based on enhanced CT radiomics. Methods: Imaging and clinical data of 429 HCC patients confirmed by surgical pathology were retrospectively analyzed. The patients were divided into training group (n=329) and test group (n=100), and their clinical characteristics were recorded. Radiology features of arterial-phase (AP) and portal venous-phase (VP) CT images were extracted, the least absolute shrinkage and selection operator method (LASSO) were used to reduce dimension and select the most valuable radiomics features. Then CT radiomics models were built base on AP features, VP features and AP+VP features, respectively. Radiological scores (rad-score) of 2 groups were calculated and then classified. According to surgical pathology results, the pathological grade of HCC was defined as high-grade and low-grade, and the optimal radiomics prediction model was selected through 10-fold cross-validation training. Finally clinical model and combined model (clinical features combined with radiomics) were constructed after screening clinical characteristics for predicting pathological grade of HCC. ROC curves of the above 3 models for predicting pathological grade of HCC in training group and test group were drawn, and their diagnostic efficacy were evaluated. Results: Combined radiomics model was the best among 3 models, and the rad-scores of high-grade and low-grade HCC were significantly different in both training group and test group (Z=8.58, 3.24, both P<0.05). In test group,no statistical difference of AUC of combined model(0.70),of radiomics model (0.69) nor clinical model (0.63) was detected for predicting pathological grading of HCC (all P>0.05). Conclusion: Radiomics features based on enhanced CT images can be used to preoperative predict pathological grade of HCC, providing reference for diagnosis and treatment of HCC.

Objective To prospectively determine the feasibility of high-resolution in vivo MR imaging in the evaluation of esophageal carcinoma invasion at 3.0 T.Methods One hundred and eighteen patients with esophageal carcinoma,proven by the gastroscopic biopsy,were prospectively studied using 3.0 T MR.The esophageal specimens were sectioned transversely to keep consistent in the orientation with the MR images,the histopathological stage was made and the thickness of the tumor on the largest diameter of the slice were measured.The MR images were reviewed in the transverse plane.According to the seventh American joint committee on cancer,the MR stage was made and the tumor's thickness was measured.The MR images and the histopathological slices were matched.The staging diagnostic efficacy of the MR imaging was evaluated with the histopathological results as the standard reference,Kappa test was used to compare the stage of MR imaging with that at the histopathological analysis.Bland-Altman scatterplots were used to compare the thickness of tumor measured on the MR images with that at the histopathological measurement.Results Ninety seven cases(82.2％,97/118) of MR stage were accurately made,including 7 T1a,15 T1b,18 T2,25 T3 and 32 T4a cases,furthermore,14 cases were over staged and 7 cased were underestimated.The MR stage was highly consistent with the histopathological stage (Kappa=0.772).The sensitivity for the staging of high-resolution MR imaging at 3.0 T was 58.3％(7/12) to 100.0％(32/32),the specificity was 95.3％ (82/86) to 98.1％ (104/106),and the accuracy was 91.5％ (108/118) to 96.6％ (114/118),respectively.Bland-Altman scatterplots demonstrated that the discrepancy of the mean thickness between the value obtained by three radiologists respectively and the histopathological analysis were 2.0,2.6 and 2.1 mm,which demonstrated a good consistency.Conclusion High-resolution MR images obtained at 3.0 T can be used to evaluate the depth of carcinoma invasion and provide excellent diagnostic accuracy for preoperative staging.

Objective:To explore the incremental value of subtraction technique in evaluating the major features of liver reporting and data system version 2018 (LI-RADS v2018) on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI.Methods:The Gd-EOB-DTPA enhanced MRI of 117 pathologically verified hepatocellualr carcinoma(HCC) from 87 high-risk patients in Henan Provincial People′s Hospital from January 2019 to July 2020 was analyzed retrospectively. The major features of LI-RADS in arterial phase, portal venous phase, subtraction and combined images were evaluated including nonrim arterial phase hyperenhancement (Nonrim APHE), nonperipheral washout and enhancing capsule. The lesions were graded according to LI-RADS v2018. According to the lesion size (<20 mm, ≥20 mm) and T 1WI signal intensity (hypointensity, isointensity or hyperintensity), the patients were divided into different subgroups. Cochran′s Q test was used for the comparison of the detection rate of the major features of LI-RADS and the accurate diagnosis rate based on LR-5 as the diagnostic standard among multiple groups. McNemar test was used for the comparison between two groups. Results:For all HCC, hypointensity HCC and HCC ≥20 mm, the detection rate of Nonrim APHE (χ2=12.190, 12.500, 10.083, all P<0.001) and the accurate diagnosis rate of HCC (χ2=14.450, 12.500, 10.083, all P<0.001) of subtraction images from arterial phase were significantly higher than that of arterial images. For HCC<20 mm, the detection rate of Nonrim APHE combined with arterial phase images was significantly higher than that in arterial phase images (χ2=5.143, P=0.016). For all HCC and isointensity or hyperintensity HCC, the detection rate of nonperipheral washout combined with portal venous phase images was higher than that in portal venous phase images (χ2=7.111, 6.125, P=0.004, 0.008). The detection rate of enhancing capsule of subtraction images from portal venous phase was higher than that of portal venous phase images in all groups (all P<0.017). The accurate diagnosis rate of subtraction images from portal venous phase in all HCC and HCC≥20 mm was higher than that in portal venous phase images (χ2=6.722, 6.750, P=0.008, 0.006). The accurate diagnosis rate of LR-5 in all groups using subtraction images from arterial phase and portal venous phase was higher than that of MRI images (all P<0.013). Conclusion:For Gd-EOB-DTPA dynamic enhanced MRI, subtraction images from arterial phase and portal venous phase are better than arterial phase and portal venous phase images in displaying Nonrim APHE, nonperipheral washout and enhancing capsule, which can improve the LI-RADS classification of HCC.

Objective:To analyze the expression of mucin 1 (MUC1) and Ki67 in intrahepatic cholangiocarcinoma (ICC), and to explore the correlations between the expression of MUC1 and Ki67 and the clinicopathological features and prognosis of ICC patients.Methods:Clinical data of 398 patients with ICC admitted to Henan Provincial People's Hospital from January 2013 to March 2020 were retrospectively analyzed. A total of 104 patients were included in this study, including 67 males and 37 females, aged (56.6±9.3) years. Immunohistochemistry was used to detect the expression of MUC1 and Ki67 in cancer tissues. Univariate and multivariate Cox regression analysis were used to study the prognostic factors of ICC patients.Results:The expression of MUC1 was low in 65 patients and high in 39 patients. Ki67 expression was low in 52 patients and high in 52 patients. High expression of MUC1 was correlated with lymph node metastasis ( P<0.05), while high expression of Ki67 was correlated with tumor nodes number, lymph node metastasis and vascular invasion (all P<0.05). Multivariate analysis showed that ICC patients with high MUC1 expression ( HR=2.321, 95% CI: 1.420-3.792, P<0.001) and high Ki67 expression ( HR=2.012, 95% CI: 1.247-3.247, P=0.004) showed a poor prognosis after hepatectomy. ICC patients with high MUC1 expression ( HR=1.664, 95% CI: 1.058-2.618, P=0.028) and high Ki67 expression ( HR=1.883, 95% CI: 1.168-3.035, P=0.009) had a poor prognosis after hepatectomy. Conclusion:High expression of MUC1 and Ki67 is correlated with tumor growth and metastasis. MUC1 and Ki67 are independent risk factors for prognosis of ICC patients after hepatectomy.

Objective:To investigate the influencing factors for microvascular invasion (MVI) in hepatocellular carcinoma based on three-dimensional visualization and the construction of its nomogram model.Methods:The retrospective cohort study method was conducted. The clinico-pathological data of 190 patients with hepatocellular carcinoma who were admitted to Henan University People′s Hospital from May 2018 to May 2021 were collected. There were 148 males and 42 females, aged (58±12)years. The 190 patients were randomly divided into the training set of 133 cases and the validation set of 57 cases by the method of random number table in the ratio of 7:3. The abdominal three-dimensional visualization system was used to characterize the tumor morphology and other imaging features. Observation indicators: (1) analysis of influencing factors for MVI in hepatocellular carcinoma; (2) construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. Measurement data with normal distribution were expressed as Mean± SD, and independent sample t test was used for comparison between groups. Measurement data with skewed distribution were expressed as M( Q1, Q3), and non-parametric rank sum test was used for comparison between groups. Count data were expressed as absolute numbers, and the chi-square test was used for comparison between groups. Corresponding statistical methods were used for univariate analysis. Binary Logistic regression model was used for multivariate analysis. Receiver operator characteristic (ROC) curves were plotted, and the nomogram model was assessed by area under the curve (AUC), calibration curve, and decision curve. Results:(1) Analysis of influencing factors for MVI in hepatocellular carcinoma. Among 190 patients with hepatocellular carcinoma, there were 97 cases of positive MVI (including 63 cases in the training set and 34 cases in the validation set) and 93 cases of negative MVI (including 70 cases in the training set and 23 cases in the validation set). Results of multivariate analysis showed that alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology were independent factors affecting the MVI of patients with hepatocellular carcinoma ( odds ratio=5.06, 3.62, 1.00, 2.02, 2.59, 95% confidence interval as 1.61-15.90, 1.28-10.20, 1.00-1.01, 1.02-3.98, 1.03-6.52, P<0.05). (2) Construction and evaluation of nomogram model of MVI in hepatocellular carcinoma. The results of multivariate analysis were incorporated to construct a nomogram prediction model for MVI of hepatocellular carcinoma. ROC curves showed that the AUC of the training set of nomogram model was 0.85 (95% confidence interval as 0.79-0.92), the optimal fractional cutoff based on the Jordon′s index was 0.51, the sensitivity was 0.71, and the specificity was 0.84. The above indicators of validation set were 0.92 (95% confidence interval as 0.85-0.99), 0.50, 0.90, and 0.82, respectively. The higher total score of the training set suggested a higher risk of MVI in hepatocellular carcinoma. The calibration curves of both training and validation sets of nomogram model fitted well with the standard curves and have a high degree of calibration. The decision curve showed a high net gain of nomogram model. Conclusions:Alpha-fetoprotein, vascular endothelial growth factor, tumor volume, the number of tumors, and tumor morphology are independent influencing factors for MVI in patients with hepatocellular carcinoma. A nomogram model constructed based on three-dimensional visualized imaging features can predict MVI in hepatocellular carcinoma.

Objective:To establish and validate a preoperative differentiateon model of intrahepatic cholangiocarcinoma (ICC) and combined hepatocellular carcinoma (CHC) based on the inflammatory markers and conventional clinical indicators.Methods:The clinical data of 116 patients with ICC or CHC admitted to Henan Provincial People's Hospital from January 2018 to March 2023 were retrospectively analyzed, including 74 males and 42 females, aged (58.5±9.4) years old. The data of 83 patients were used to establish the differentiation model as the training group, including 50 cases of ICC and 33 cases of CHC. The data of 33 patients were used to validate the model as the validation group, including 20 cases of ICC and 13 cases of CHC. The clinical data including the platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), prognostic inflammatory index (PII), prognostic nutritional index (PNI), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR) were collected and analyzed. The receiver operating characteristic (ROC) curve was used to analyze the best cut-off values of PLR, SII, PII, PNI, NLR and LMR. Univariate and multivariate logistic regression analysis were used to determine the differential factors between ICC and CHC. The R software was used to draw the nomogram, calculate the area under the curve (AUC) to evaluate the model accuracy, and draw the calibration chart and the decision curve to evaluate the predictive efficacy of the model.Results:Univariate logistic regression analysis showed that liver cirrhosis, history of hepatitis, alpha fetoprotein, carbohydrate antigen 199, gamma-glutamyltransferase (GGT), PLR, PNI and inflammation score (IS) could be used to differentiate ICC from CHC (all P<0.05). The indicators identified in univariate analysis were included in multivariate logistic regression analysis. The results showed that absence of liver cirrhosis, GGT>60 U/L, PNI>49.53, and IS<2 indicated the pathology of ICC (all P<0.05). Based on the above four factors, a nomogram model was established to differentiate the ICC and CHC. The AUC of ROC curve of the nomogram model in the training and validation groups were 0.851 (95% CI: 0.769-0.933) and 0.771 (95% CI: 0.594-0.949), respectively. The sensitivities were 0.760 and 0.750, and the specificities were 0.818 and 0.769, respectively. The calibration chart showed that the predicted curve fitted well to the reference line. The decision curve showed that the model has a clear positive net benefit. Conclusion:The nomogram model based on inflammatory markers showed a good differentiation performance of ICC and CHC, which could benefits the individualized treatment.

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.
Carcinoma, Hepatocellular ; diagnostic imaging ; Humans ; Liver Neoplasms ; diagnostic imaging ; Magnetic Resonance Imaging ; Neoplasm Grading ; methods ; ROC Curve