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 investigate the safety and feasibility of the interventional therapy on hemobilia induced by hepatic artery pseudoaneurysm(PsAn).Methods The clinical data of 28 patients with hemobilia in our department was analyzed retrospectively.All the patients were firstly treated with internal medicine,and then treated by interventional therapy.The angiography clearly displayed the shape,size and location of the PsAn,and superselective embolism was performed during the operation.The symptomatic treatment and the bile duct drainage were performed after operation.Postoperative follow-up time was 1-3 6 months,and the follow-up endpoint was the death of patient.Results 28 patients were diagnosed as hepatic artery PsAn.The interventional embolization therapy on hemobilia was completely effective.A total of 110 spring coils were applicated in the study,there was no complication related to interventional therapy.One patient with hilar cholangiocarcinoma was dead due to infection and liver failure after four weeks of operation.Two patients with choledochal carcinoma died of tumor progression after 11.5 and 14.2 months of interventional therapy,respectively.In addition,2 patients with gastric carcinoma died of tumor progression after 4 and 6.5 months of operation,respectively,and 1 patient with common bile duct stones who underwent laparoscopic surgery died of complications of coronary heart disease after 5 months of interventional therapy. The other patients all had satisfactory therapeutic effect and recovered well.Conclusion The interventional therapy has definite therapeutic effect on hemobilia induced by hepatic artery PsAn.The technique is worthy of popularized clinically due to its features of simplicity,safety and reliablity.

Objective: To explore the therapeutic value of interventional methods for hemorrhage caused by mandibular arteriovenous malformations. Methods: The clinical data of 7 patients (3 males and 4 females) with mandibular arteriovenous malformations treated by interventional therapy from January 2012 to January 2018 in the First Affiliated Hospital, Zhengzhou University were retrospectively analyzed. Of all patients, 4 patients suffered from sudden massive hemorrhage and 3 patients suffered from spontaneous repeated bleeding. The age ranged from 8.0 to 13.0 (10.6±1.7) years. Of the 7 patients, 3 underwent interventional embolization via arteries and veins, and 4 underwent embolization only via arteries. The embolic materials were polyvinyl alcohol granules and coils. The follow-up period was 9—18 months and the curative effect was observed. Results: Among the 7 patients, 4 cases of acute massive hemorrhage were effectively controlled after interventional operation, 3 cases of chronic bleeding disappeared after interventional operation. No recurrence of bleeding occurred during the follow-up period, only 1 patient presented with oral infection and gingival swelling and hyperplasia. The symptoms were effectively controlled after anti-infection and debridement. No severe complications occurred in all patients. Conclusion Interventional therapy for ateriovenous malformation with hemorrhage is effective, safe and feasible, which is worthy of clinical application.

With the development of detection methods, various biomarkers of liver cancer have been detected constantly, which is of great significance for the early diagnosis and real-time monitoring of liver cancer after treatment. Based on the differences in the sensitivity and specificity of different biomarkers, exploration of the value of diverse biomarkers in the diagnosis and prognosis assessment of liver cancer can provide an important reference for clinicians to scientific and rational application of distinct biomarkers.

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 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 observe the value of clinical and CT radiomics features for predicting microsatellite instability-high(MSI-H)status of gastric cancer.Methods Totally 150 gastric cancer patients including 30 cases of MSI-H positive and 120 cases of MSI-H negative were enrolled and divided into training set(n=105)or validation set(n=45)at the ratio of 7∶3.Based on abdominal vein phase enhanced CT images,lesions radiomics features were extracted and screened,and radiomics scores(Radscore)was calculated.Clinical data and Radscores were compared between MSI-H positive and negative patients in training set and validation set.Based on clinical factors and Radscores being significant different between MSI-H positive and negative ones,clinical model,CT radiomics model and clinical-CT radiomics combination model were constructed,and their predictive value for MSI-H status of gastric cancer were observed.Results Significant differences of tumor location and Radscore were found between MSI-H positive and negative patients in both training and validation sets(all P＜0.05).The area under the curve(AUC)of clinical model,CT radiomics model and combination model for evaluating MSI-H status of gastric cancer in training set was 0.760,0.799 and 0.864,respectively,of that in validation set was 0.735,0.812 and 0.849,respectively.AUC of clinical-CT radiomics combination model was greater than that of the other 2 single models(all P＜0.05).Conclusion Clinical-CT radiomics combination model based on tumor location and Radscore could effectively predict MSI-H status of gastric cancer.