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A major impedance to neuronal regeneration after peripheral nerve injury(PNI)is the activation of various programmed cell death mechanisms in the dorsal root ganglion.Ferroptosis is a form of pro-grammed cell death distinguished by imbalance in iron and thiol metabolism,leading to lethal lipid peroxidation.However,the molecular mechanisms of ferroptosis in the context of PNI and nerve regeneration remain unclear.Ferroportin(Fpn),the only known mammalian nonheme iron export protein,plays a pivotal part in inhibiting ferroptosis by maintaining intracellular iron homeostasis.Here,we explored in vitro and in vivo the involvement of Fpn in neuronal ferroptosis.We first delineated that reactive oxygen species at the injury site induces neuronal ferroptosis by increasing intracellular iron via accelerated UBA52-driven ubiquitination and degradation of Fpn,and stimulation of lipid peroxidation.Early administration of the potent arterial vasodilator,hydralazine(HYD),decreases the ubiquitination of Fpn after PNI by binding to UBA52,leading to suppression of neuronal cell death and significant ac-celeration of axon regeneration and motor function recovery.HYD targeting of ferroptosis is a promising strategy for clinical management of PNI.
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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.
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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.
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Objective:To compare Al 18F-1, 4, 7-trizacyclononane-1, 4, 7-triacetic acid (NOTA)-fibroblast activation protein inhibitor (FAPI)-04 PET/CT with 18F-FDG PET/CT in the evaluation of patients with initial gastric cancer. Methods:Twenty patients (13 males, 7 females, age: 27-77 years) with histologically proven gastric cancer were recruited prospectively between March 2021 and July 2022 in the First Affiliated Hospital of Zhengzhou University. Each patient underwent both 18F-FDG and Al 18F-NOTA-FAPI-04 PET/CT within one week. SUV max, tumor background ratio (TBR) and positive detection rate of the two methods were compared (Wilcoxon signed rank sum test, McNemar χ2 test). Results:Al 18F-NOTA-FAPI-04 showed higher SUV max and TBR than those of 18F-FDG in primary tumors (10.2(8.0, 13.7) vs 5.2(3.3, 7.7), z=-3.47, P=0.001; 7.6(5.6, 10.3) vs 2.4(1.8, 3.0), z=-3.85, P<0.001). For the detection of primary gastric cancer, the positive detection rate of Al 18F-NOTA-FAPI-04 PET/CT showed the trend of being higher than that of 18F-FDG PET/CT (95%(19/20) and 75%(15/20); χ2=2.25, P=0.125). For assessing lymph node metastasis, the detection rate of Al 18F-NOTA-FAPI-04 PET/CT was higher than that of 18F-FDG PET/CT (78.9%(101/128) vs 64.8%(83/128); χ2=13.47, P<0.001). The SUV max and TBR of Al 18F-NOTA-FAPI-04 in lymph node were higher than those of 18F-FDG (5.3(3.5, 9.2) vs 2.8(1.8, 4.7), z=-7.31, P<0.001; 4.6(2.6, 6.5) vs 1.7(1.0, 3.0), z=-8.44, P<0.001). For the detection of peritoneal carcinomatosis, Al 18F-NOTA-FAPI-04 PET/CT showed higher peritoneal cancer index (PCI), SUV max, and TBR compared to 18F-FDG PET/CT (PCI: 12.0(3.0, 29.8) vs 5.5(0.5, 17.5), z=-2.22, P=0.026; SUV max: 8.2(4.4, 12.5) vs 2.7(1.9, 4.0); z=-2.52, P=0.012; TBR: 5.1(2.9, 13.3) vs 1.1(0.9, 2.0); z=-2.52, P=0.012). Conclusion:Al 18F-NOTA-FAPI-04 PET/CT outperforms 18F-FDG PET/CT in primary and metastatic lesions of gastric cancer and might be a potential novel modality for imaging patients with gastric cancer.
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Objective:To explore the clinical efficacy of CT-guided 125I seed implantation in patients with oligometastatic non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) activating mutations (EGFRm+ ) without progression after first-line EGFR-tyrosine kinase inhibitors (TKIs) treatment. Methods:From January 2015 to January 2019, 89 eligible patients (38 males, 51 females; age: (62±11) years) in the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. They were divided into 2 groups according to different treatment methods. The 125I seeds were implanted for oligometastatic lesions and/or primary tumors without progression after first-line EGFR-TKIs therapy in local consolidation treatment group (Group A, n=32). The maintenance treatment group (Group B, n=57) only received EGFR-TKIs until disease progression. The progression-free survival (PFS) and overall survival (OS) of the 2 groups were estimated by Kaplan-Meier curves, and were compared by using log-rank test. Complications in Group A were observed. Results:The follow-up time of the group A and group B were 36.5(31.0, 43.3) months and 30.0(24.0, 35.0) months respectively. The median PFS and OS in group A were 15.0(95% CI: 12.8-17.2 ) months and 37.0(95% CI: 33.9-40.1) months, both of which were significantly longer than those in group B (12.0(95% CI: 10.9-13.1) months and 31.0(95% CI: 28.9-33.1) months; χ2 values: 8.80, 7.15, P values: 0.003, 0.007). In Group A, the total incidence of complications in CT-guided 125I seed implantation was 21.9%(7/32), and the common complications and adverse events were pneumothorax and hemoptysis. Only 1 patient underwent chest tube insertion, and the rest were treated with conservative treatment. No operation related death occurred. Conclusion:CT-guided 125I seed implantation is safe and feasible for patients with EGFRm+ oligometastatic NSCLC without progression after first-line EGFR-TKIs treatment, and can prolong the PFS and OS of patients.
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Objective:To construct of a computed tomography (CT) based radiomics model for predicting the prognosis of patients with gastric neuroendocrine neoplasm (GNEN) and inves-tigate its application value.Methods:The retrospective cohort study was conducted. The clinico-pathological data of 182 patients with GNEN who were admitted to 2 medical centers, including the First Affiliated Hospital of Zhengzhou University of 124 cases and the Affiliated Cancer Hospital of Zhengzhou University of 58 cases, from August 2011 to December 2020 were collected. There were 130 males and 52 females, aged 64(range, 56-70)years. Based on random number table, all 182 patients were divided into the training dataset of 128 cases and the validation dataset of 54 cases with a ratio of 7:3. All patients underwent enhanced CT examination. Observation indicators: (1) construction and validation of the radiomics prediction model; (2) analysis of prognostic factors for patients with GNEN in the training dataset; (3) construction and evaluation of the prediction model for prognosis of patients with GNEN. Measurement data with skewed distribution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and the chi-square test, corrected chi-square test or Fisher exact probability were used for comparison between groups. The Kaplan-Meier method was used to calculate survival rate and draw survival curve, and the Log-rank test was used for survival analysis. The COX regression model was used for univariate and multivariate analyses. The R software (version 4.0.3) glmnet software package was used for least absolute shrinkage and selection operator (LASSO)-COX regression analysis. The rms software (version 4.0.3) was used to generate nomogram and calibration curve. The Hmisc software (version 4.0.3) was used to calculate C-index values. The dca.R software (version 4.0.3) was used for decision curve analysis. Results:(1) Construction and valida-tion of the radiomics prediction model. One thousand seven hundred and eighty-one radiomics features were finally extracted from the 182 patients. Based on the feature selection using intra-group correlation coefficient >0.75, and the reduce dimensionality using LASSO-COX regression analysis, 14 non zero coefficient radiomics features were finally selected from the 1 781 radiomics features. The radiomics prediction model was constructed based on the radiomics score (R-score) of these non zero coefficient radiomics features. According to the best cutoff value of the R-score as -0.494, 128 patients in the training dataset were divided into 64 cases with high risk and 64 cases with low risk, 54 patients in the validation dataset were divided into 35 cases with high risk and 19 cases with low risk. The area under curve (AUC) of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the training dataset was 0.83[95% confidence interval ( CI ) as 0.76-0.87, P<0.05], 0.84(95% CI as 0.73-0.91, P<0.05), 0.91(95% CI as 0.78-0.95, P<0.05), respectively. The AUC of radiomics prediction model in predicting 18-, 24-, 30-month overall survival rate of patients in the validation dataset was 0.84(95% CI as 0.75-0.92, P<0.05), 0.84 (95% CI as 0.73-0.91, P<0.05), 0.86(95% CI as 0.82-0.94, P<0.05), respectively. (2) Analysis of prognostic factors for patients with GNEN in the training dataset. Results of multivariate analysis showed gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression were independent factors influencing prognosis of patients with GNEN in the training dataset ( P<0.05). (3) Construction and evaluation of the prediction model for prognosis of patients with GNEN. The clinical prediction model was constructed based on the independent factors influen-cing prognosis of patients with GNEN including gender, age, treatment method, tumor boundary, tumor T staging, tumor N staging, tumor M staging, Ki-67 index, CD56 expression. The C-index value of clinical prediction model in the training dataset and the validation dataset was 0.86 (95% CI as 0.82-0.90) and 0.80(95% CI as 0.72-0.87), respectively. The C-index value of radiomics prediction model in the training dataset and the validation dataset was 0.80 (95% CI as 0.74-0.86, P<0.05) and 0.75(95% CI as 0.66-0.84, P<0.05), respectively. The C-index value of clinical-radiomics combined prediction model in the training dataset and the validation dataset was 0.88(95% CI as 0.85-0.92) and 0.83 (95% CI as 0.77-0.89), respectively. Results of calibration curve show that clinical prediction model, radiomics prediction model and clinical-radiomics combined prediction model had good predictive ability. Results of decision curve show that the clinical-radiomics combined prediction model is superior to the clinical prediction model, radiomics prediction model in evaluating the prognosis of patients with GNEN. Conclusions:The predection model for predicting the prognosis of patients with GNEN is constructed based on 14 radiomics features after selecting. The prediction model can predict the prognosis of patients with GNEN well, and the clinical-radiomics combined prediction model has a better prediction efficiency.
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Objective:To investigate the value of spectral CT based iodine concentration (IC) parameters for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer.Methods:Between January 2021 and November 2021, 266 patients diagnosed as gastric adenocarcinomas by endoscopy and undergoing gastrectomy at the Affiliated Cancer Hospital of Zhengzhou University were recruited prospectively. They were divided into LVI and non-LVI groups according to pathological reports. Triple phase contrasted enhanced CT scans, including arterial phase (AP), venous phase (VP) and delayed phase (DP) were performed on a spectral CT platform within one week before surgery. The IC of gastric cancer lesions at three enhanced phases were measured based on iodine maps, and the normalized IC (nIC) was calculated. The thickness of the tumor was measured. Clinicopathological features were collected, including ulceration, pathological tumor staging (pT), pathological node staging (pN), histodifferentiation, Lauren subtype, perineural invasion (PNI), positive node numbers and positive node ratio. Student′s t tes t or Mann-Whitney U test were used to compare the differences of continuous variables between the two groups, while Chi-square test or Fisher′s exact test was used for categorical data. Multivariable logistic regression analysis was used to screen independent risk factors of LVI, and to build a combined parameter based on risk factors. The receiver operating characteristic curve analysis was performed to determine the predictive efficacy of IC parameters and the combined parameter for LVI. DeLong′s test was used to compare the differences among different area under the curve (AUC). Results:There were statistical differences in tumor thickness, ulceration, pT, pN, histodifferentiation, positive node numbers, positive node ratio, Lauren subtype and PNI between LVI and non-LVI groups ( P<0.05). The values of IC VP, IC DP, nIC VP, nIC DP in LVI group were statistically higher than those in non-LVI group ( t=3.77, 4.23, 4.25, 6.12, all P<0.001), with the AUC (95%CI) of 0.674 (0.610-0.738), 0.677 (0.614-0.741), 0.731 (0.671-0.792), 0.700 (0.636-0.764) for predicting LVI, respectively. Multivariable logistic regression analysis revealed that tumor thickness (OR=1.148, 95%CI 1.085-1.237, P<0.001) and nIC VP (OR=209.904, 95%CI 14.874-644.362, P<0.001) were independent predictors for LVI, the combined parameter incorporating these two factors yielded an AUC (95%CI) of 0.790 (0.736-0.937), which was statistically higher than any single parameter of IC VP, IC DP, nIC VP and nIC DP ( Z=3.07, 3.29, 2.10, 2.60, P=0.002, 0.001, 0.036, 0.009). Conclusion:The IC and nIC values of gastric cancer lesions derived from the VP and DP on spectral CT can effectively predict LVI status in gastric adenocarcinomas, and the combination of nIC VP and tumor thickness can further improve the predictive efficacy.
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Objective:To evaluate the value of preoperative prediction of vessel invasion (VI) of locally advanced gastric cancer by machine learning model based on the venous phase enhanced CT radiomics features.Methods:A retrospective analysis of 296 patients with locally advanced gastric cancer confirmed by pathology in the First Affiliated Hospital of Zhengzhou University from July 2011 to December 2020 was performed. The patients were divided into VI positive group ( n=213) and VI negative group ( n=83) based on pathological results. The data were divided into training set ( n=207) and test set ( n=89) according to the ratio of 7∶3 with stratification sampling. The clinical characteristics of patients were recorded, and the independent risk factors of gastric cancer VI were screened by multivariate logistic regression. Pyradiomics software was used to extract radiomic features from the venous phase enhanced CT images, and the minimum absolute shrinkage and selection algorithm (LASSO) was used to screen the features, obtain the optimal feature subset, and establish the radiomics signature. Four machine learning algorithms, including extreme gradient boosting (XGBoost), logistic, naive Bayes (GNB), and support vector machine (SVM) models, were used to build prediction models for the radiomics signature and the screened clinical independent risk factors. The efficacy of the model in predicting gastric cancer VI was evaluated by the receiver operating characteristic curve. Results:The degree of differentiation (OR=13.651, 95%CI 7.265-25.650, P=0.003), Lauren′s classification (OR=1.349, 95%CI 1.011-1.799, P=0.042) and CA199 (OR=1.796, 95%CI 1.406-2.186, P=0.044) were independent risk factors for predicting the VI of locally advanced gastric cancer. Based on the venous phase enhanced CT images, 864 quantitative features were extracted, and 18 best constructed radiomics signature were selected by LASSO. In the training set, the area under the curve (AUC) of XGBoost, logistic, GNB and SVM models for predicting gastric cancer VI were 0.914 (95%CI 0.875-0.953), 0.897 (95%CI 0.853-0.940), 0.880 (95%CI 0.832-0.928) and 0.814 (95%CI 0.755-0.873), respectively, and in the test set were 0.870 (95%CI 0.769-0.971), 0.877 (95%CI 0.788-0.964), 0.859 (95%CI 0.755-0.961) and 0.773 (95%CI 0.647-0.898). The logistic model had the largest AUC in the test set. Conclusions:The machine learning model based on the venous phase enhanced CT radiomics features has high efficacy in predicting the VI of locally advanced gastric cancer before the operation, and the logistic model demonstrates the best diagnostic efficacy.
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Objective:To explore the value of the deep learning image reconstruction (DLIR) algorithm in improving the CT image quality of abdominal phantoms under different radiation doses by comparing the DLIR algorithm with the conventional Adaptive Statistical Iterative Reconstruction-V (ASIR-V) technique.Methods:Two groups with tube voltages of 100 kV and 120 kV (also referred to as the 100 kV and 120 kV groups, respectively) were involved. Each group was further divided into six subgroups based on different volumetric CT dose indices (CTDI vol: 2, 4, 6, 8, 10 and 15 mGy). Subsequently, CT images based on the filtered back projection (FBP) algorithm were obtained and were then reconstructed using the ASIR-V algorithm with different weights (ASIR-V 50%, 80%, and 100%) and the DLIR algorithm with different levels (DLIR-L, M, and -H). As a result, 84 groups of images were obtained in total. Afterward, this study compared and analyzed the variations in CT values, noise, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), and subjective scores of various parts in various CTDI vol subgroups under different reconstruction conditions. In addition, the subjective scores of the image quality were compared using the Kruskal-Wallis H test, while objective indices and radiation doses were compared through the univariate analysis of variance (ANOVA) and the paired t test. Results:Under the same tube voltage, there were statistically significant differences in the noise, SNRs, and CNRs of various parts in various CTDI vol subgroups under different reconstruction conditions ( F = 415.39, 315.30, P < 0.001), while there was no statistically significant difference in the noise, SNRs, and CNRs of images constructed using ASIR-V 50% and DLIR-L ( P > 0.05). Under different tube voltages, the subjective scores of both groups show statistically significant differences (100 kV group: H = 13.47, P = 0.036; 120 kV group: H = 12.99, P = 0.043). Moreover, two physicians offered consistent subjective scores, with Kappa values > 0.70. Among these images, DLIR-H images showed the highest subjective scores, followed by DLIR-M and ASIR-V 50% images, which had roughly consistent subjective scores. Moreover, the subjective scores of the 100 kV group were slightly higher than those of the 120 kV group. With the ASIR-V 50% images of the subgroup with a CTDI vol of 15 mGy as references, the DLIR-L, -M, and -H reduced radiation doses by more than 30%, 70% and 85%, respectively on the premise that diagnostic requirements were met. Conclusions:The DLIR algorithm can not only significantly reduce the image noise and improve the image quality, but also effectively decrease the radiation doses on the premise of meeting the diagnostic requirements. It is recommended that 100 kV tube voltage combined with a medium- or high-level DLIR algorithm should be applied to low-dose abdominal CT scans in clinical applications.
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Objective To investigate the accuracy and reproducibility of deep learning algorithms combined with asynchronous calibration quantitative computed tomography(QCT)for measuring bone mineral density(BMD),and to explore the feasibility of using low-dose scanning BMD measurement.Methods European spine phantom(ESP)was scanned with asynchronous calibration QCT and conventional synchronous calibration QCT,respectively,the accuracy and short-term reproducibility was compared.ESP were scanned with asynchronous calibration QCT,matching 120 kVp with five sets of tube currents:20,60,100,140,and 180 mA.Three levels of deep learning image reconstruction(DLIR)and hybrid model-based adaptive statistical iterative reconstruction V(40％ASIR-V)were used for reconstruction.The BMD values of three vertebrae in the ESP were measured.Furthermore,the image noise and contrast-to-noise ratio(CNR)were compared.Results The relative errors(RE)of the three vertebrae of the asynchronous calibration QCT and synchronous calibration QCT were all less than 7％.There was no statistical difference in the BMD values of the two scans at one week interval of the asynchronous calibration QCT(P＞0.05).There were no significant differences in RE among different tube currents or different reconstruction methods(P＞0.05).The image quality of deep learning-based image reconstruction of high strength(DLIR-H)at 20 mA tube current was better than that of 40％ASIR-V at 180 mA,and the radiation dose was reduced by 89％.Conclusion Asynchronous calibration QCT has high accuracy in BMD measurement,and has good repeatability.Asynchronous calibration QCT which combined with DLIR does not affect the accuracy of BMD measurement,and can significantly improve the CNR of images and reduce image noise.
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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.
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Objective:Evaluate the image quality of dual-layer spectral detector CT pulmonary angiography with low-dose contrast agents, and explore the influence of pulmonary artery diameter on the image quality.Methods:A total of 91 spectral CT pulmonary angiography from March 2022 to August 2022 were retrospectively analyzed. The cases were divided into Group 1 ( n=34, main pulmonary artery diameter ≥ 30 mm) and Group 2 ( n=57, main pulmonary artery diameter<30 mm). The dosage of contrast agent was 30 ml. The CT attenuation values(CT values), standard deviation(SD), signal-to-noise ratio(SNR), and contrast-to-noise ratio(CNR) values of pulmonary artery from the main trunk to the subsegmental pulmonary artery between two groups were compared. The CT dose index volume(CTDI vol) and dose-length production (DLP) were recorded. Two readers evaluated the image quality using three-point method. The inter-reader agreement was performed by Kappa test. Results:The CT values of the pulmonary trunk and left pulmonary artery between two groups was not significantly different ( P>0.05). The CT values of the left upper lobe artery, segmental artery, and subsegmental artery in Group 1 were lower than those in Group 2 ( t=-2.13, -2.17, Z=-2.33, P<0.05). The SD values of pulmonary trunk and segmental artery in Group 1 were higher than those in Group 2 ( t=2.27, Z=-2.23, P<0.05). The SD values of left pulmonary artery, left upper lobe artery, and subsegmental artery between two groups were not significantly different ( P>0.05). The SNR and CNR values of main pulmonary trunk, left pulmonary artery, left superior lobar artery, and segmental artery in Group 2 were higher than those in Group 1 ( Z=-2.45, -2.57, -2.09, -3.58, P<0.05; Z =-2.33, -2.42, -2.07, -3.45, P<0.05), while these values of the subsegmental artery between two groups were not significantly different ( P>0.05). The two readers had good consistency in evaluating image quality (Kappa value>0.75, P<0.05). Conclusions:Spectral CT pulmonary angiography with 30 ml contrast agent would generate good quality images. However, the distal pulmonary artery would be poorly revealed when the diameter of main pulmonary artery more than 30 mm, especially in patients with suspected pulmonary hypertension.
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Objective:To explore the influence of deep learning reconstruction algorithm combined with low-dose CT on image quality and bone mineral density measurement and the application value in opportunistic osteoporosis screening.Methods:A total of 119 patients (aged ≥40 years) who underwent a combined chest and upper abdominal low-dose scan were prospectively included. All the images were reconstructed using filtered back projection(FBP) alogrithm, hybrid model-based adaptive statistical iterative reconstruction (ASIR-V) 50% and three levels of deep learning reconstruction algorithm respectively. Bone mineral density (BMD) values for different reconstruction conditions were measured and compared using asynchronous quantitative CT software. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of descending aorta, liver and spleen were calculated, and the image noise was the standard deviation of anterior abdominal wall fat and the image quality was objectively evaluated by using the five-point subjective evaluation method. The objective and subjective image quality of different body parts with different reconstruction method was compared.Results:There was no statistical difference in BMD with different reconstruction method ( P > 0.05). Compared with ASIR-V 50%, the SNRs of high level deep learning image reconstruction (DLIR-H)in descending aorta, latissimus dorsi, liver and spleen were increased by 103.88%, 125.09% and 136.13% respectively, and the image noise was decreased by 55.98%. Both the CNR and subjective scores (except the ability to display lung lesions) of DLIR-H were better than those of DLIR-L and ASIR-V 50% ( χ2 =158.31-275.35, P<0.001). Conclusions:The deep learning algorithm does not affect the accuracy of bone mineral density measurement, and the image quality is better than that of ASIR-V 5%. Deep learning algorithm combined with low-dose CT can be used for opportunistic osteoporosis screening.
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Objective:To compare the safety and clinical value of percutaneous computed tomography (CT)-guided fine-needle aspiration biopsy (CT-FNA) with CT-guided core-needle biopsy (CT-CNB) in diagnosis of pancreatic lesions.Methods:We retrospectively analyzed the clinical data of patients with pancreatic lesions who underwent percutaneous CT-guided biopsy from January 2017 to January 2022 at the First Affiliated Hospital of Zhengzhou University. A total of 454 patients (251 men, 203 women) were enrolled in this study with age of (60.5±11.6) years old. They were divided into the CT-FNA group ( n=300) and the CT-CNB group ( n=154) according to the biopsy method. The one-time diagnosis rate, accuracy, sensitivity, false negative rate and incidence rate of complications of the two groups were compared. Results:The one-time diagnosis rate and accuracy rate in the CT-CNB group were slightly higher than those in the CT-FNA group, but the differences were not statistically significant [92.2%(142/154) vs. 86.0%(258/300), χ 2=3.74, P=0.053; 97.4%(150/154) vs. 92.0%(276/300), χ 2=0.16, P=0.690]. Compared with the CT-FNA group, the CT-CNB group had a higher sensitivity and a lower false negative rate, and the differences were statistically significant [97.2%(138/142) vs. 91.5%(260/284), χ 2=4.89, P=0.036; 2.8%(4/142) vs. 8.5%(24/284), χ 2=4.89, P=0.036]. Common complications in the two groups were pain, hematoma and pancreatitis, and there was no statistically significant difference in the incidences of complication [9.0%(27/300) vs. 9.1%(14/154), χ 2<0.01, P=0.975]. Conclusions:Both CT-FNA and CT-CNB were safe for diagnosis of pancreatic lesions, with high diagnostic yields and with similar safety and accuracy. When compared with CT-FNA, CT-CNB has a higher sensitivity and a lower false negative rate.
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Objective:To describe the baseline characteristics of the subjects enrolled in the China Quantitative CT (QCT) big data program in 2018—2019.Methods:Based on baseline data from the Chinese health big data project from January 2018 to December 2019 from the eligible enrolled population, measurements of bone mineral density (BMD) and visceral adipose tissue (VAT) were performed using Mindways′ QCT Pro Model 4 system. The baseline data of age, gender, regional distribution, height, weight, abdominal circumference, blood pressure, blood routine and blood biochemical tests were analyzed. And the single factor analysis of variance (ANOVA) was used to check the age related trend of BMD and VAT in both genders.Results:After screening the inclusion exclusion criteria and outliers of the main indicators, 86 113 people were enrolled in the project. The enrollment rate was 92.47%, including 35 431 (41.1%) women and 50 682 (58.9%) men, and the ratio of men to women was 1.43. The mean age was (50.3±12.7) years in all the subjects, and it was (50.2±12.8) years and (50.4±12.5) years in men and women, respectively, and there was no statistical difference between the two genders ( P>0.05). Total of 43 833 people were enrolled in east China, it was the largest group by region (50.90%), it was followed by central China (16 434 people, 19.08%), and the number of people enrolled in Northeast China was the lowest (2 914 people, 3.38%). The rate of completing of health information indicators related to the main outcome of the study were all above 70%, and there were significant differences between men and women (all P<0.05). The mean BMD was (139.33±46.76) mg/cm 3 in women, (135.90±36.48) mg/cm 3 in men, which showed a decreasing trend with age in both gender (both P<0.001); the mean intra-abdominal fat area was (116.39±56.23) cm 2 in women, (191.67±77.07) cm 2 in men, and there was an increasing trend with age in both men and women (both P<0.001). Conclusions:There are gender differences in BMD and VAT measured by QCT with different age tendency, and there are gender differences in health information index. Regional factors should also be taken into account for regional differences in the inclusion of data.
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Objective:To investigate the normal reference values of spinal bone mineral density measured by quantitative computed tomography (QCT) and the differences of bone mineral density (BMD) in different regions of in Chinese adult males.Methods:Men who underwent low-dose CT lung scan for cancer screening in regions of Northeast, North, East, South, Central and Southwest of China from January 2018 to December 2019 were selected. And the lumbar vertebrae BMD values in the male subjects were measured by the QCT system (Mindways Software, Inc.). The mean BMD values and their decline rates were calculated at an age interval of 10 years, and the prevalence of osteoporosis was calculated according to the American College of Radiology spine QCT osteoporosis diagnostic criteria.Results:A total of 50 682 males with a mean age of (50.22±12.79) years (ranged 20 to 98 years) were included in this study. The peak BMD of (173.11±28.56) mg/cm 3 in the healthy Chinese adult male population appeared in the age group of 20 to 29 years and then declined with age. Before the age of 70 years, the BMD was relatively higher in males in South China, and it was lower in Central China and Southwest China, and it was intermediate in Northeast, North and East of China, with statistically significant differences. There was no significant differences in BMD in the males in the two age groups of 70 to 79 years and 80 and older among the regions in China. The overall decline rate of spinal BMD in Chinese males under QCT was about 46.92% over the lifetime, and it declined obviouslyin the 40-49 age group. The overall prevalence of osteoporosis in Chinese male population aged 50 years and above was approximately 11.42%, with the highest prevalence in Southwest China and Central China (14.72% and 13.87%, respectively) and the lowest in North China and South China (8.53% and 7.71%, respectively). Conclusions:A reference of lumbar spine BMD values for healthy males in China based on QCT is established. BMD values were highest in South China and Lowest in Central China.
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Objective:To establish the normal reference value of lumbar bone mineral density (BMD) under quantitative CT (QCT) in Chinese healthy adult females and to explore the regional differences.Methods:Total of 35 431 healthy women who met the inclusion criteria of Chinese health quantitative CT big data program were selected in this study. The BMD of the central plane of L 1 and L 2 vertebrae was measured by Mindways′s QCT system, and the mean value was taken. One-way analysis of variance was used to compare the BMD differences of lumbar vertebrae in women of different ages and regions. The subjects were grouped by an age interval of 10 years, and the level of BMD in different regions of the same age group were compaired. Results:The peak BMD of Chinese healthy adult women appeared in the age group of 20-29 years (Northeast China(183.01±24.58) mg/cm 3, North China (188.93±24.80) mg/cm 3, East China (187.54±27.71) mg/cm 3, South China (186.22±33.72) mg/cm 3, Central China (176.33±24.91) mg/cm 3, Southwest China(182.25±28.00) mg/cm 3), and then it decreased with age. The level of BMD in different regions decreased with the age. Before the age of 70 years, BMD in Central and Southwest China was always at a low level((176.23±24.91) to (90.38±28.12) mg/cm 3, 182.25±28.00 to (88.55±25.68) mg/cm 3), lower than those in Northeast China ((183.01±24.58) to (99.69±27.85) mg/cm 3), North China ((188.93±24.80) to (95.89±26.12) mg/cm 3), East China ((187.54±27.71) to (95.65±27.86) mg/cm 3). After 70 years of age, BMD tended to be the same in different regions ( P>0.05). The BMD values in Central China and Southwest China were similar in the age group of 40-60 years ( P>0.05). The BMD values in the health adult femles in the age group of 60 years in different regions of Chinawere all lower than those of bone mass abnormality (all P<0.05). The detection rate of osteoporosis in females over 50 years was the highest in Southwest China (25.65%) and it was the lowest in North China (17.30%). Conclusions:This study establishes reference values of BMD under QCT in healthy Chinese women, which can be used as a reference basis for identifying women with low BMD who are at risk of osteoporosis. The BMD value is the lowest in Southwest China and the highest in South China.
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Objective:To use quantitative computed tomography (QCT) technology to measure the bone mineral density of the spine of the Chinese healthy population, and to explore its correlation with hemoglobin and serum albumin.Methods:The data in this study came from the China Health Quantitative CT Big Data Project (China Biobank). The spine bone density was measured by using QCT Pro Image Analysis System and all cooperating centers used the European spine phantom (NO.145) for quality control. Total of 50 053 healthy persons who met the criteria for entry were selected as the research subjects. The subjects were divided into 7 groups according to age. The general data, spine bone density, serum albumin, hemoglobin of the subjects were collected. The single-factor analysis of variance, Pearson correlation analysis and multi-classification logistic regression model were applied to analyze the correlation between bone density and hemoglobin and serum albumin.Results:The bone mineral density of healthy people decreased with age ( P<0.05), and there were significant differences in hemoglobin, serum albumin and body mass index (BMI) among different age groups (all P<0.05). Linear correlation analysis showed that there were positive correlation between bone mineral density and hemoglobin in healthy males in different age groups ( r=0.086, 0.101, 0.076, 0.090, 0.072, 0.123, 0.100, all P<0.01). There were negative correlation between bone mineral density and hemoglobin in certain age groups in women (40-49 years group: r=-0.027; 70-79 yearsgroup: r=-0.077; both P<0.05). And corelation were found between bone mineral density and serum levels of albumin in certain age groups of healthy subjects (among men, 30-39 years group: r=-0.048; 40-49 years group, r=-0.027; 70-79 years group, r=-0.051; among women, 30-39 years group: r=-0.044; 40-49 years group, r=-0.042; 50-59 years group, r=-0.086; 70-79 years group, r=-0.070; all P<0.05). After adjusting for age and BMI, the multi-category logistic regression analysis showed that the hemoglobin level was protective factor of normal bone density ( OR=1.022, 95% CI:1.017-1.027) and decreased bone density ( OR=1.012, 95% CI:1.007-1.016) in healthy males, and the serum albumin was risk factor for normal bone density ( OR=0.926, 95% CI:0.905-0.948) and decreased bone density ( OR=1.006, 95% CI:0.951-1.011) in healthy women. Conclusion:There is a correlation between bone mineral density and hemoglobin and serum albumin in Chinese healthy population. Hemoglobin is a protective factor for bone mineral density in men, and serum albumin is a risk factor for bone mineral densityin women.
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Objective:To investigate the clinicopathological features and computed tomography (CT) findings of tracheal glomus tumor (GT) in order to improve the understanding and diagnosis of tracheal GT.Methods:The clinical and CT imaging data of 2 patients with tracheal GT diagnosed in the First Affiliated Hospital of Zhengzhou University were analyzed retrospectively. The image characteristics based on previous reports were analyzed.Results:The clinical manifestations of trachea GT were dyspnea, chest tightness, hemoptysis, etc., which were easy to be misdiagnosed. The CT manifestations were spherical or nodular protrusions in the trachea cavity, with uneven edges, which can be lobulated. Cystic changes can be seen in the focus. After enhancement, it showed progressive filling and obvious enhancement, without deep infiltration and distant metastasis.Conclusions:Chest CT can accurately localize tracheal GT, provide its morphological size, blood supply, growth characteristics and other characteristics, accurately display the overall morphology of the lesion, and provide some help for the development of the surgical plan, and its definitive diagnosis still relies on pathological examination.
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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.
