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Objective:To evaluate the diagnostic efficacy of prostate imaging recurrence reporting (PI-RR) system for detecting local recurrence after radical prostatectomy (RP) in prostate cancer (PCa) and to assess the consistency of the PI-RR scores assigned by different seniority radiologists.Methods:This study was a cross-sectional study. A total of 176 PCa patients who underwent multi-parametric MRI (mpMRI) for biochemical recurrence (BCR) after RP from July 2015 to October 2021 at the First Affiliated Hospital of Soochow University were retrospectively collected. The mpMRI images were reviewed and the PI-RR scores of the main lesions were assigned independently by six different seniority radiologists (2 junior, 2 senior and 2 expert radiologists). Following the reference standard determined by biopsy pathologic results, follow-up imaging, or prostate specific antigen levels, the patients were divided into two groups: 54 patients with local recurrence and 122 patients without local recurrence. The intraclass correlation coefficient ( ICC) and Kappa test were used to evaluate the consistency of the PI-RR scores by different seniority radiologists. The receiver operating characteristic (ROC) curve analysis was employed to evaluate the diagnostic efficacy of the PI-RR scores assessed by different seniority radiologists for detecting local recurrence of PCa after RP. The DeLong test was utilized to compare the areas under the ROC curve (AUC) of different seniority radiologist PI-RR scores and a false discovery rate (FDR) was applied to correct results using the Benjamini and Hochberg method. Sensitivity and specificity were calculated according to the cutoff value of PI-RR score≥3 or 4. Results:The ICC (95% CI) of all different seniority radiologists was 0.70 (0.64-0.76). The Kappa value was 0.528, 0.325 and 0.370 respectively between expert and senior radiologists, expert and junior radiologists, senior and junior radiologists. The AUC (95% CI) of junior, senior, and expert radiologists were separately 0.73 (0.65-0.81), 0.81 (0.74-0.88), and 0.86 (0.80-0.93). The AUC of the expert radiologist PI-RR score was higher than those of senior and junior radiologist PI-RR scores ( Z=2.22, 3.21, FDR P=0.039, 0.003). The PI-RR score of senior radiologist had higher AUC than that of junior radiologist ( Z=2.22, FDR P=0.026). With the PI-RR score of 3 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.59, 0.65, and 0.78 and the specificity were 0.82, 0.93, and 0.95. With the PI-RR score of 4 or greater as a cutoff value, the sensitivity of junior, senior and expert radiologists were respectively 0.50, 0.54, and 0.69 and the specificity were 0.88, 0.96 and 0.97. Conclusion:PI-RR score can accurately diagnose local recurrence of PCa after RP. PI-RR score has a moderate inter-reader consistency across different seniority radiologists. And the diagnostic performance is influenced by the experience of radiologists.
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In the past 20 years,the development of artificial intelligence has made rapid progress,and it is increasingly applied in the medical field,including medical image-assisted diagnosis and treatment,health management,disease risk prediction and so on.In this paper,the application status of artificial intelligence-assisted detection and diagnosis system based on deep learning in anorectal diseases is summarized,and the new methods related to the diagnosis and treatment of anorectal diseases at home and abroad are summarized.It mainly reviews the research progress of artificial intelligence technology in the diagnosis and treatment of anal fistula,perianal abscess,hemorrhoids and other anorectal diseases.
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Objective:To explore the predictive value of deep learning (DL)-based coronary artery calcification score (CACS) for obstructive coronary artery disease (CAD) and noncalcified plaque/mixed plaque in type 2 diabetes mellitus (T2DM).Methods:Forty hundred and twenty-four consecutive T2DM patients who accepted CACS scan and coronary CT angiography (CCTA) from December 2012 to December 2019 were included retrospectively, with clinical risk factors and plaque features collected. Plaque composition was classified as calcified, non-calcified or mixed plaque. Obstructive CAD was defined as maximum diameter stenosis≥50%. CACS was calculated with a fully automated method based on DL. Univariate and multivariate logistic regressions were applied to select statistically significant factors and the odds ratios(ORs) were measured. Receiver operating characteristic (ROC) curve was evaluated to assess the predictive performance.Results:Increased CACS was associated with a significantly higher odds of obstructive CAD in CCTA (adjusted ORs were 2.22, 6.18 and 16.98 for CACS=1-99, 100-299, 300-999 vs. CACS=0, and P values were 0.009,<0.001,<0.001 respectively). The area under ROC curve (AUC) of CACS to predict obstructive CAD was 0.764. Compared with 0, increased CACS was associated with increased risk of non-calcified/mixed plaque (adjusted ORs were 2.75, 4.76, 5.29 for CACS=1-99, 100-299, 300-999 respectively and P values were 0.001,<0.001,<0.001 respectively). The AUC of CACS to predict non-calcified/mixed plaque was 0.688. It took 1.17 min to perform automated measurement of CACS based on DL in total, which was significantly less than manual measurement of 1.73 min ( P<0.001). Conclusion:DL-based CACS can predict obstructive CAD and non-calcified plaque/mixed plaque in T2DM, which is economical and efficient, and has important value for clinical diagnosis and treatment.
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BACKGROUND@#Pneumonia-like primary pulmonary lymphoma (PPL) was commonly misdiagnosed as infectious pneumonia, leading to delayed treatment. The purpose of this study was to establish a computed tomography (CT)-based radiomics model to differentiate pneumonia-like PPL from infectious pneumonia.@*METHODS@#In this retrospective study, 79 patients with pneumonia-like PPL and 176 patients with infectious pneumonia from 12 medical centers were enrolled. Patients from center 1 to center 7 were assigned to the training or validation cohort, and the remaining patients from other centers were used as the external test cohort. Radiomics features were extracted from CT images. A three-step procedure was applied for radiomics feature selection and radiomics signature building, including the inter- and intra-class correlation coefficients (ICCs), a one-way analysis of variance (ANOVA), and least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses were used to identify the significant clinicoradiological variables and construct a clinical factor model. Two radiologists reviewed the CT images for the external test set. Performance of the radiomics model, clinical factor model, and each radiologist were assessed by receiver operating characteristic, and area under the curve (AUC) was compared.@*RESULTS@#A total of 144 patients (44 with pneumonia-like PPL and 100 infectious pneumonia) were in the training cohort, 38 patients (12 with pneumonia-like PPL and 26 infectious pneumonia) were in the validation cohort, and 73 patients (23 with pneumonia-like PPL and 50 infectious pneumonia) were in the external test cohort. Twenty-three radiomics features were selected to build the radiomics model, which yielded AUCs of 0.95 (95% confidence interval [CI]: 0.94-0.99), 0.93 (95% CI: 0.85-0.98), and 0.94 (95% CI: 0.87-0.99) in the training, validation, and external test cohort, respectively. The AUCs for the two readers and clinical factor model were 0.74 (95% CI: 0.63-0.83), 0.72 (95% CI: 0.62-0.82), and 0.73 (95% CI: 0.62-0.84) in the external test cohort, respectively. The radiomics model outperformed both the readers' interpretation and clinical factor model ( P <0.05).@*CONCLUSIONS@#The CT-based radiomics model may provide an effective and non-invasive tool to differentiate pneumonia-like PPL from infectious pneumonia, which might provide assistance for clinicians in tailoring precise therapy.
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Humans , Retrospective Studies , Pneumonia/diagnostic imaging , Analysis of Variance , Tomography, X-Ray Computed , Lymphoma/diagnostic imagingРеферат
Objective@#Recent studies have highlighted the active and potential role of perivascular adipose tissue (PVAT) in atherosclerosis and aneurysm progression, respectively. This study explored the link between PVAT attenuation and abdominal aortic aneurysm (AAA) progression using computed tomography angiography (CTA). @*Materials and Methods@#This multicenter retrospective study analyzed patients with AAA who underwent CTA at baseline and follow-up between March 2015 and July 2022. The following parameters were obtained: maximum diameter and total volume of the AAA, presence or absence of intraluminal thrombus (ILT), maximum diameter and volume of the ILT, and PVAT attenuation of the aortic aneurysm at baseline CTA. PVAT attenuation was divided into high (> -73.4 Hounsfield units [HU]) and low (≤ -73.4 HU). Patients who had or did not have AAA progression during the follow-up, defined as an increase in the aneurysm volume > 10 mL from baseline, were identified. Kaplan–Meier and multivariable Cox regression analyses were used to investigate the association between PVAT attenuation and AAA progression. @*Results@#Our study included 167 participants (148 males; median age: 70.0 years; interquartile range: 63.0–76.0 years), of which 145 (86.8%) were diagnosed with AAA accompanied by ILT. Over a median period of 11.3 months (range: 6.0–85.0 months), AAA progression was observed in 67 patients (40.1%). Multivariable Cox regression analysis indicated that high baseline PVAT attenuation (adjusted hazard ratio [aHR] = 2.23; 95% confidence interval [CI], 1.16–4.32; P = 0.017) was independently associated with AAA progression. This association was demonstrated within the patients of AAA with ILT subcohort, where a high baseline PVAT attenuation (aHR = 2.23; 95% CI, 1.08–4.60; P = 0.030) was consistently independently associated with AAA progression. @*Conclusion@#Elevated PVAT attenuation is independently associated with AAA progression, including patients of AAA with ILT, suggesting the potential of PVAT attenuation as a predictive imaging marker for AAA expansion.
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Objective:To construct a clinical-radiomics model based on MRI, and to explore its predictive value for biochemical recurrence (BCR) after radical prostatectomy in prostate cancer patients.Methods:A total of 212 patients with prostate cancer who underwent radical prostatectomy in the First Affiliated Hospital of Soochow University from January 2015 to December 2018 and had complete follow-up data were retrospectively analyzed. The random toolkit of Python language was used to randomly sample the patients at a ratio of 7∶3 without replacement, and they were divided into a training set (149 cases) and a test set (63 cases). The endpoint of follow-up was BCR or at least 3 years. BCR occurred in 50 patients in the training group and 21 patients in the test group. The imaging features of the main lesion area in the preoperative T 2WI, diffusion-weighted imaging and apparent diffusion coefficient map of patients in the training set were extracted, and the unsupervised K means clustering algorithm was used to screen the features. The selected features were fitted by a multivariate Cox regression model, and the radiomics model was constructed. Univariate Cox regression analyses were used to screen the main clinical risk factors associated with BCR, and the clinical-radiomics model was constructed combined with RadScore. In the test set, the time-dependent receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) was calculated to evaluate the predictive efficacy of the radiomics model, clinical-radiomics model and prostate cancer risk assessment after radical resection (CAPRA-S) score for the occurrence of BCR. Harrell consistency index (C-index) was used to evaluate the model to predict BCR consistency. The calibration curve was used to evaluate the degree of variation of the model. The decision curve was used to evaluate the clinical application value of the prediction model. Results:A total of 26 radiomics features were screened to establish the radiomics model. The univariate Cox showed that the preoperative clinical features included preoperative prostate-specific antigen level (HR=1.006, 95%CI 1.002-1.009, P=0.001), Gleason score of biopsy (HR=1.422, 95%CI 1.153-1.753, P=0.001), clinical T stage (HR=1.501, 95%CI 1.238-1.822, P<0.001). The multivariate Cox showed that the RadScore was an independent predictor of BCR after radical prostatectomy (HR=51.214, 95%CI 18.226-143.908, P<0.001). The selected preoperative clinical features were combined with RadScore to construct a clinical-radiomics model. In the test set, the AUCs of the time (3 years)-dependent ROC curves of the radiomics model, the clinical-radiomics model, and the CAPRA-S score were 0.824 (95%CI 0.701-0.948), 0.841 (95%CI 0.714-0.968), and 0.662 (95%CI 0.518-0.806), respectively. The C-index of the radiomics model, clinical-radiomics model and CAPRA-S score were 0.784 (95%CI 0.660-0.891), 0.802 (95%CI 0.637-0.912) and 0.650 (95%CI 0.601-0.821), respectively. The calibration curve showed that the predicted probability and actual probability of BCR by radiomics model, clinical-radiomics model and CAPRA-S score were in good agreement (χ 2=7.64, 10.61, 6.37, P=0.465, 0.225, 0.498). The decision curve showed that the clinical net benefit of the clinical-radiomics model and the radiomics model was significantly higher than the CAPRA-S score. When the threshold probability was 0.20-0.30, 0.40-0.50, and >0.55, the clinical net benefit of the clinical radiomics model was higher than that of the radiomics model. Conclusions:The clinical-radiomics model can effectively predict the occurrence of BCR in patients with prostate cancer after radical prostate ctomy, and the prediction efficacy is better than the radiomics model and CAPRA-S score.
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Objective:To investigate the effect of calcification on the diagnostic accuracy of the quantitative flow fraction (CT-QFR) derived from coronary CT angiography (CCTA).Methods:A total of 244 patients (471 coronary arteries) who underwent both CCTA and invasive coronary angiography (ICA) for suspected coronary artery disease between 2019 and 2021 were included in the study. All analyses were conducted at the vessel level using CCTA and ICA images, and the morphological and hemodynamic parameters of all enrolled vessels were assessed. The group was divided into severe calcification (206 cases) and non-severe calcification (265 cases) based on whether the arc of lesion calcification was greater than 180°. Subsequently, the two groups were evaluated to the degree of coronary stenosis, the length of the target lesion, the length of calcification, the ratio of the length of calcification, the remodeling index of calcification, the quantitative flow fraction (QFR), the CT-QFR, and the distribution of the involved vessels. Pearson correlation analysis and the Bland-Altman scatterplot were used to analyze the correlation and consistency between CT-QFR and QFR values from different subgroups. The benchmark for coronary ischemia was QFR≤0.80, and the criteria for diagnosing coronary ischemia were CT-QFR≤0.80 and luminal stenosis≥50%, respectively, and the effectiveness of CT-QFR for coronary ischemia was evaluated by plotting the ROC curves in various calcification subgroups.Results:The degree of luminal stenosis, lesion length, calcification length ratio, and calcification remodeling index were substantially higher in the severely calcified group than in the non-severely calcified group (all P<0.05). The results of the Pearson correlation analysis demonstrated a significant association between CT-QFR and QFR in both the severe and non-severe calcification groups ( r=0.85, 95%CI 0.81-0.88, P<0.001; r=0.91, 95%CI 0.89-0.93, P<0.001); in contrast, the Bland-Altman analysis indicated that the CT-QFR and QFR measurements in the severely calcified group exhibited a high level of agreement, with a mean difference of -0.01 (95% limits of agreement -0.22 to 0.20) for measurements in the severely calcified group and 0 (95% limits of agreement -0.15 to 0.16). The specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for the diagnosis of ischaemic lesions by CT-QFR and CCTA alone were lower in the severely calcified group than in the non-severely calcified group, but the difference in AUC between the two groups for CT-QFR was not statistically significant ( P>0.05), and the difference in AUC for the morphological assessment of CCTA was statistically significant. The diagnostic effectiveness of CCTA alone was considerably worse than the specificity and AUC of CT-QFR for the various calcified subgroups for the diagnosis of ischemic lesions (all P<0.001). Conclusions:Severe calcification somewhat affected the diagnosis of ischaemic lesions by CT-QFR, but there was still a high correlation and concordance between CT-QFR and QFR within the severely calcified group, and the diagnostic efficacy was significantly better than that assessed by CCTA morphology alone.
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Objective:To investigate the value of CT features in predicting the invasion and degree of invasiveness of lung pure ground-glass nodules (pGGN) in the new histological classification in 2021.Methods:A total of 281 patients (304 lesions) with pGGN confirmed by surgical pathology from December 2018 to January 2021 in Shandong Provincial Hospital Affiliated to Shandong First Medical University were retrospectively analyzed. According to the pathological types, the patients were divided into prodromal lesion group [atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS), 129 cases], minimally invasive group [minimally invasive adenocarcinoma (MIA), 116 cases] and invasive group [invasive adenocarcinoma (IAC), 59 cases]. Clinical data (age, gender, smoking history, family history of cancer), and CT parameters [shape, boundary, lobulation, burr, vacuolar sign, bronchial abnormality sign, internal vessel sign, pleural traction sign, longest diameter, shortest diameter, unenhanced CT value, contrast-enhanced CT value in arterial phase, contrast-enhanced CT values in venous phase, the degree of enhancement (ΔCT A-N, ΔCT V-N)] were recorded and measured. The ANOVA, Kruskal-Wallis H and χ 2 test were used to compare the differences among the three groups. Binary logistic regression analysis was used to evaluate the independent risk factors of nodular invasion [prodromal lesion and invasive lesion (MIA and IAC)] and the degree of nodular invasion (MIA and IAC), and receiver operating characteristic (ROC) curve analysis was performed for each parameter. Results:There were statistically significant differences in age, pGGN morphology, lobulation, vacuolar sign, bronchial abnormality sign, internal vascular sign, pleural traction sign, longest diameter, shortest diameter, unenhanced CT value, contrast-enhanced CT value in arterial phase, contrast-enhanced CT value in venous phase among the precursor lesion group, minimally invasive group and invasive group ( P<0.05). Binary logistic regression analysis showed that vacuole sign (OR=2.832, 95%CI 1.363-5.887, P=0.005), internal vascular sign (OR=3.021, 95%CI 1.909-4.779, P<0.001) and unenhanced CT value (OR=1.003, 95%CI 1.001-1.006, P=0.019) were independent risk factors for invasion. Lobulation (OR=5.739, 95%CI 2.735-12.042, P<0.001), internal vascular sign (OR=1.968, 95%CI 1.128-3.433, P=0.017) and unenhanced CT value (OR=1.004, 95%CI 1.001-1.008, P=0.012) were independent risk factors for the degree of invasiveness. ROC curve analysis showed that the efficiency of internal vascular sign was the highest in distinguishing precursor lesion and the invasive, the area under the curve (AUC) was 0.757, the sensitivity was 50.3%, the specificity was 89.8%. The efficiency of lobulation was the highest in distinguishing MIA and IAC (AUC=0.702), with a sensitivity of 61.0% and specificity of 79.3%. Conclusions:CT features are of certain value in predicting the invasion and degree of invasiveness of lung pGGN in the new histological classification in 2021, and internal vascular sign is more effective in predicting the invasion of lung pGGN. Lobulation can predict the degree of invasiveness of pGGN better.
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BACKGROUND@#Nowadays, biological tissue engineering is a growing field of research. Biocompatibility is a key indicator for measuring tissue engineering biomaterials, which is of great significance for the replacement and repair of damaged tissues. @*METHODS@#In this study, using gelatin, carboxymethyl chitosan, and sodium alginate, a tissue engineering material scaffold that can carry cells was successfully prepared. The material was characterized by Fourier transforms infrared spectroscopy. In addition, the prepared scaffolds have physicochemical properties, such as swelling ratio, biodegradability.we observed the biocompatibility of the hydrogel to different adult stem cells (BMSCs and ADSCs) in vivo and in vitro. Adult stem cells were planted on gelatin-carboxymethyl chitosan-sodium alginate (Gel/SA/CMCS) hydrogels for 7 days in vitro, and the survival of stem cells in vitro was observed by live/died staining. Gel/SA/CMCS hydrogels loaded with stem cells were subcutaneously transplanted into nude mice for 14 days of in vivo culture observation. The survival of adult stem cells was observed by staining for stem cell surface markers (CD29, CD90) and Ki67. @*RESULTS@#The scaffolds had a microporous structure with an appropriate pore size (about 80 lm). Live/died staining showed that adult stem cells could stably survive in Gel/SA/CMCS hydrogels for at least 7 days. After 14 days of culture in nude mice, Ki67 staining showed that the stem cells supported by Gel/SA/CMCS hydrogel still had high proliferation activity. @*CONCLUSION@#Gel/SA/CMCSs hydrogel has a stable interpenetrating porous structure, suitable swelling performance and degradation rate, can promote and support the survival of adult stem cells in vivo and in vitro, and has good biocompatibility. Therefore, Gel/SA/CMCS hydrogel is a strong candidate for biological tissue engineering materials.
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Objective:To explore the value of different machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features in preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC).Methods:The data of 132 patients with HCC confirmed by pathology in the First Affiliated Hospital of Soochow University from January 2015 to May 2020 were retrospectively analyzed, including 72 cases of positive MVI and 60 cases of negative MVI. According to the proportion of 7∶3, the cases were randomly divided into training set and validation set. The radiomics features of hepatobiliary phase images for HCC were extracted by PyRadiomics software. The clinical and radiomics features of the training set were screened by the least absolute shrinkage and selection operator (LASSO) regression with 5 fold cross-validation, and then the optimal feature subset was obtained. Six machine learning algorithms, including decision tree, extreme gradient boosting, random forest, support vector machine (SVM), generalized linear model (GLM) and neural network, were used to build the prediction models, and the ROC curves were used to evaluate the prediction ability of the models. DeLong test was used to compare the differences of area under the curve (AUC) for 6 machine learning algorithms.Results:Totally 14 features selected by LASSO regression were obtained to form the optimal feature subset, including 2 clinical features (maximum tumor diameter and alpha-fetoprotein) and 12 radiomics features. The AUCs of decision tree, extreme gradient boosting, random forest, SVM, GLM and neural network based on the optimal feature subset were 0.969, 1.000, 1.000, 0.991, 0.966, 1.000 in the training set and 0.781, 0.890, 0.920, 0.806, 0.684, 0.703 in the validation set, respectively. There were significant differences in the AUCs between extreme gradient boosting and GLM or neural network ( Z=2.857, 3.220, P=0.004, 0.001). The differences in AUCs between random forest and SVM, GLM, or neural network were significant ( Z=2.371, 3.190, 3.967, P=0.018, 0.001,<0.001). The difference in AUCs between SVM and GLM was statistically significant ( Z=2.621 , P=0.009). There were no significant differences in the AUCs among the other machine learning models ( P>0.05). Conclusion:Machine learning models based on Gd-EOB-DTPA enhanced MRI hepatobiliary phase radiomics features can be used to preoperatively predict MVI of HCC, particularly the extreme gradient boosting and random forest models have high prediction efficiency.
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Objective:To explore the relationship between triglyceride-glucose (TyG) index with plaque components, plaque burden and characteristics of vulnerable plaque using coronary plaque analysis based on coronary artery computed tomography (CCTA).Methods:A total of 498 patients(male 296, female 202), the age ranged from 33 to 87 (63±9) years who underwent CCTA from January 2020 to September in Shandong Provincial Hospital Affiliated to Shandong First Medical University were included. The enrolled patients were divided into three groups according to the tertiles of TyG index: T 1 group (the lowest one-third), T 2 group (middle one-third) and T 3 group (the highest one-third). The plaque burden, volume and ratio of calcified, lipid and fibrous components of plaques as well as the incidence of vulnerable plaques were measured based on CCTA images. Chi-square test, ANOVA and Kruskal-Wallis test were used to compare whether the differences of indexes among the three groups were statistically significant. Multiple stepwise regression was used to analyze the influencing factors of coronary atherosclerotic plaque burden and multiple logistic regression was used to analyze the risk factors of CT high-risk plaque. Finally, ROC curve was used to evaluate the value of TyG index in the diagnosis of CT high-risk plaque, and the best diagnostic threshold of TyG index was determined. Results:The plaque burden, non-calcified plaque volume and ratio had positive relationship with TyG index ( P<0.001).TyG index was significantly correlated with plaque burden according to multiple stepwise regression analysis (regression coefficient 7.267, P<0.001). The results of CT vulnerable characteristics of plaques showed that positive remodeling, low-attenuation plaque sign and the incidence of vulnerable plaque increased with TyG index ( P<0.05). Multivariate Logistic regression analysis showed that TyG index was an independent risk factor for CT vulnerable plaque(OR=2.324,95 %CI 1.533-3.523, P<0.001). The cut-off value of TyG index that can predict vulnerable plaque was 8.43(sensitivity 77.24%, specificity 45.60%, AUC 0.645, P<0.001). Conclusions:With the increase of TyG index, the burden of coronary atherosclerosis plaque and the incidence of CT vulnerable plaque increased. TyG index is expected to be a simple and effective predictor of cardiovascular disease and adverse cardiovascular events.
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Objective@#To analyze the quantitative features of coronary plaque and evaluate its diagnostic performance for myocardial ischemic injury in patient with coronary artery disease.@*Methods@#Retrospectively enrolled 109 patients with suspected coronary artery disease, who successively underwent coronary CT angiography(CCTA) and coronary angiography in Shandong Provincial Hospital from June 2018 to September 2019. Elevated myocardial enzyme with segmental wall motion abnormalities (SWMA) in ultrasound was defined as myocardial ischemic injury, with which the subjects were divided into two groups, with and without myocardial ischemic injury (n=75,34) respectively. CCTA images of each target vessel were quantitatively analyzed by automated plaque analysis software to obtain the following indexes: minimal lumen area(MLA), plaque length(PL), total plaque volume(TPV), total plaque burden(TPB),calcified plaque volume(CPV), calcified plaque ratio(CPR), fibrous plaque volume(FPV), fibrous plaque ratio(FPR), lipid plaque volume(LPV), lipid plaque ratio(LPR), napkin-ring sign(NRS), spotty calcification(SC), remodeling index (RI) and eccentric index (EI). Chi-square, Mann-Whitney U tests, logistic regression and area under the receiver operating characteristics were determined.@*Results@#For the degree of coronary artery stenosis, MAS% was 85.00% (80.00%, 92.00%) and 63.00% (60.00%, 65.00%) in myocardial ischemic group and without myocardial ischemic injury group, which was statistically significant (Z=-4.32, P=0.001). For the quantitative plaque features, TPV 150.13 (104.44,202.20) mm3, TPB (75.67%±9.90%), FPV 95.73 (66.57, 134.23)mm3, LPV 32.18 (18.93,54.55) mm3, LPR (25.13%±13.71%) in the group with myocardial ischemic injury were larger than those in group without myocardial ischemic injury 109.94 (79.39, 121.67) mm3, 65.37%±6.94%, 67.35 (57.67, 90.11) mm3, 16.64 (13.26, 24.73) mm3, 18.44%±7.09% respectively with statistically significant (Z=-2.59, P=0.010; t=3.11, P=0.003; Z=-2.16, P=0.031; Z=-2.18, P=0.029; t=2.19, P=0.037). In logistic regression analysis, MAS%(OR=1.55,P=0.021) was independent significant predictors of myocardial ischemic injury. The AUC of MAS%, LPV, LPR, TPV, TPB, FPV were 0.84, 0.82, 0.77, 0.72, 0.74, 0.67, respectively, which were all statistically significant (P<0.05).@*Conclusions@#In quantitative plaque analysis by coronary CT angiography, MAS%, TPV, TPB, FPV, LPV, LPR were affecting factors of myocardial ischemic injury, in which MAS% was independent predictors. MAS% and LPV have higher diagnostic accuracy in myocardial ischemic injury.
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Objective:To analyze the quantitative features of coronary plaque and evaluate its diagnostic performance for myocardial ischemic injury in patient with coronary artery disease.Methods:Retrospectively enrolled 109 patients with suspected coronary artery disease, who successively underwent coronary CT angiography(CCTA) and coronary angiography in Shandong Provincial Hospital from June 2018 to September 2019. Elevated myocardial enzyme with segmental wall motion abnormalities (SWMA) in ultrasound was defined as myocardial ischemic injury, with which the subjects were divided into two groups, with and without myocardial ischemic injury ( n=75,34) respectively. CCTA images of each target vessel were quantitatively analyzed by automated plaque analysis software to obtain the following indexes: minimal lumen area(MLA), plaque length(PL), total plaque volume(TPV), total plaque burden(TPB),calcified plaque volume(CPV), calcified plaque ratio(CPR), fibrous plaque volume(FPV), fibrous plaque ratio(FPR), lipid plaque volume(LPV), lipid plaque ratio(LPR), napkin-ring sign(NRS), spotty calcification(SC), remodeling index (RI) and eccentric index (EI). Chi-square, Mann-Whitney U tests, logistic regression and area under the receiver operating characteristics were determined. Results:For the degree of coronary artery stenosis, MAS% was 85.00% (80.00%, 92.00%) and 63.00% (60.00%, 65.00%) in myocardial ischemic group and without myocardial ischemic injury group, which was statistically significant ( Z=-4.32, P=0.001). For the quantitative plaque features, TPV 150.13 (104.44,202.20) mm 3, TPB (75.67%±9.90%), FPV 95.73 (66.57, 134.23)mm 3, LPV 32.18 (18.93,54.55) mm 3, LPR (25.13%±13.71%) in the group with myocardial ischemic injury were larger than those in group without myocardial ischemic injury 109.94 (79.39, 121.67) mm 3, 65.37%±6.94%, 67.35 (57.67, 90.11) mm 3, 16.64 (13.26, 24.73) mm 3, 18.44%±7.09% respectively with statistically significant ( Z=-2.59, P=0.010; t=3.11, P=0.003; Z=-2.16, P=0.031; Z=-2.18, P=0.029; t=2.19, P=0.037). In logistic regression analysis, MAS%(OR =1.55, P=0.021) was independent significant predictors of myocardial ischemic injury. The AUC of MAS%, LPV, LPR, TPV, TPB, FPV were 0.84, 0.82, 0.77, 0.72, 0.74, 0.67, respectively, which were all statistically significant ( P<0.05). Conclusions:In quantitative plaque analysis by coronary CT angiography, MAS%, TPV, TPB, FPV, LPV, LPR were affecting factors of myocardial ischemic injury, in which MAS% was independent predictors. MAS% and LPV have higher diagnostic accuracy in myocardial ischemic injury.
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Objective:To evaluate the application of multiparametric MRI (mpMRI)-transrectal ultrasound (TRUS) fusion guided biopsy in the diagnosis of clinical significant prostate cancer (PCa).Methods:A prospective analysis was performed in 168 patients with suspected PCa from September 2015 to June 2017 in the First Affiliated Hospital of Soochow University. Suspicious areas on mpMRl were defined and graded using prostate imaging reporting and data system version 2 (PI-RADS V2) score. All the patients had the TRUS-guided systematic biopsy, 108 patients with PI-RAD V2 scores ≥ 3 had additional MRI-TRUS targeted biopsies. Taking pathologic results as golden standard, the detection rates were compared between the 2 methods using χ 2 test. Results:Initially, all of the 168 patients underwent TRUS biopsy. PCa was detected in 86 (101 niduses) of 168 patients (51.19%, 86/168), 82 (91 niduses) (48.81%, 82/168) were not prostate cancer. Seventy eight (46.43%, 78/168) cases of PCa were detected by TRUS biopsy, and 63 (58.33%, 63/168) cases of PCa were detected by MRI-TRUS fusion guided biopsy, the difference was statistically significant between TRUS biopsy and MRI-TRUS fusion guided biopsy (χ 2=3.73, P=0.035). The 168 patients were biopsied with a total of 2 300 cores, including TRUS biopsy 2 016 cores and MRI-TRUS fusion targeted biopsy 284 cores. Additionally, the detection rate for per cores for MRI-TRUS fusion targeted biopsy (51.76%, 147/284) was significantly higher than that for TRUS biopsy cores (19.64%, 396/2 016) (χ 2=142.38, P<0.05). Among patients with a positive biopsy for PCa, the biopsy cores for conventional TRUS biopsy was 1 032 comparing to 214 cores for MRI-TRUS biopsy. The suspicious MRI-TRUS fusion targeted biopsy (68.69%, 147/214) detected more PCa compared with TRUS biopsy (38.37%, 396/1 032) (χ 2=66.27, P<0.05). Among patients with a positive biopsy for PCa, MRI-TRUS fusion targeted biopsy [69.74% (106/152)] detected more significant cancer cores than TRUS biopsy [54.50% (351/644) ] (χ 2=11.67, P<0.05). Conclusion:MRI-TRUS fusion biopsy combined with PI-RADS V2 increases positive rate markedly and improves the detection rate of clinical significant PCa.
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Objective:To explore the evolution of imaging findings on dynamic chest high resolution CT（HRCT） in patients with mild COVID-19.Methods:The data of epidemiology, clinical data and continuous dynamic chest high-resolution CT images of the patients with mild COVID-19 were analyzed retrospectively. Twenty-five patients (including 24 common type and 1 mild) were enrolled in the group, including 14 males and 11 females, with age of （42±12） years and hospital stay of （19±5） days. The basic images and dynamic images of HRCT were analyzed and compared by the radiologists.Results:The clinical manifestations were fever (22 cases), cough (18 cases), expectoration (8 cases), pharyngeal pain (6 cases). Most laboratory tests lacked specificity. There were no significant abnormalities on chest CT of one mild patient. HRCT findings of the common type were as follows: (1) the distribution of the lesions: most of the multiple lesions involved both lungs (19 cases), with average of 3 (3±1) lobes, located in the peripheral pulmonary zone near the pleura (22 cases); (2) the morphology and density of the lesions: most of the lesions were ground glass density foci (22 cases), which were patchy and massive (18 cases), nodular (10 cases) and arc broadband (7 cases); with the development of the disease, the density of some lesions increased with localized pulmonary consolidation (10 cases), accompanied by air bronchus sign (5 cases) and halo sign (5 cases). Dynamic changes of HRCT images in the chest: the positive manifestations were found on the 5th (5, 6) day after the onset of the disease, the progressive time of CT lesions was 5 (5, 7) days, the peak time of CT lesions was 11 (10, 13) days, and the turning time of CT lesions was 9 (8, 11) days.Conclusions:Dynamic chest HRCT can monitor the basic evolution process of the disease in patients with mild COVID-19, and provide a more intuitive basis for clinical early diagnosis and treatment.
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Objective:To explore the clinical value of auto-tube voltage controlled contrast medium injection based on 3rd generation dual source CT coronary angiography.Methods:Patients with suspected coronary heart disease were prospectively enrolled from March to December, 2019 at Shandong Provincial Hospital and coronary CT angiography (CCTA) images were acquired from 220 patients, including 107 male, 113 female, aged from 34 to 82 years. Patients were divided into experimental and control groups with the random number table. In experimental group (113 patients), automatic tube voltage selection technology was used, the contrast agent dosage was set according to tube voltage. The injection time was 10 s; In control group (107 patients), tube voltage and contrast agent dosage were set according to weights. The injection time was 12 s. Images were acquired by ECG gating using the 3rd generation dual source CT (DSCT) with intravenous injection of 350 mg/L contrast medium, followed up with saline of the same dose. Interclass correlation coefficient (ICC) was used to evaluate the individual bias of raters. The rank sum test was used to evaluate the group-level differences of subjective image quality and contrast agent dosage. The t-test was used to evaluate the group-level differences of objective image quality and effective radiation dose (ED). Results:The noise of aortic root in the two groups were (27±4), (26±5) HU, respectively, with no statistical difference ( t=1.017, P=0.284). All ICC values were more than 0.5 indicating good correlation batween 2 raters. The objective image quality score was no significant differences( P>0.05). The subjective image quality scores of the two groups were 1.15±0.10 and 1.18±0.12, respectively, with no statistical difference (Z=-0.231, P=0.818). The ED value (2.2±0.6) mSv of experimental group was statistically lower than that of control group (4.6±1.8) mSv ( t=-13.107, P<0.001); the contrast dosage (35±7) ml of experimental group was statistically lower than that of control group（46±6）ml ( t=-8.699, P<0.001). Conclusions:The novel scanning protocol with auto-tube voltage based contrast agent setting is more convenient and practical with reduced radiation dose and contrast dose, while maintaining image quality.
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Objective:To explore the value of spectral CT radiomics quantitative features on differentiating lung cancer nodule from inflammatory nodule.Methods:The spectral CT imaging data of 96 lung cancer nodules and 45 inflammatory nodules from the First Affiliated Hospital of Soochow University were analyzed retrospectively. According to a ratio of two to one, patients were randomly assigned to the training group and validation group, including 64 lung cancer nodules and 30 inflammatory nodules in the training group, 32 lung cancer nodules and 15 inflammatory nodules in the validation group. MaZda software was used for radiomic feature extraction from the 70 keV monochromatic images in arterial phase and venous phase for lung cancer nodules and inflammatory nodules in the training group. Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE+ACC) and mutual information (MI) were used to select 10 optimal features for the optimal feature subsets. The optimal feature subsets were analyzed by using linear discriminant analysis (LDA) and nonlinear discriminant analysis (NDA) to calculate the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, specificity, precise and F1 score in differentiating lung cancer nodule from inflammatory nodule. The prediction model was established using the optimal feature subsets in the training group with artificial neural network (ANN). Then the established prediction model was used to differentiate lung cancer nodule from inflammatory nodule in the validation group. Delong test was used to compare the differences in the AUC of different optimal feature subsets.Results:In arterial phase, the optimal feature subset obtained from MI-NDA had the highest AUC of 0.888 [95% confidence interval (CI) 0.806-0.943], accuracy rate of 88.3%, sensitivity of 87.5% and specificity of 90.0%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between MI-NDA and Fisher-NDA or (POE+ACC)-NDA method ( Z=1.941, P=0.052; Z=1.683, P=0.092). In venous phase, the optimal feature subset obtained from (POE+ACC)-NDA had the highest AUC of 0.846 (95%CI 0.757-0.912), accuracy rate of 87.2%, sensitivity of 92.2% and specificity of 76.7%, on the differential diagnosis of lung cancer nodule and inflammatory nodule in the training group. There was no significant difference in AUC between(POE+ACC)-NDA and MI-NDA method ( Z=1.354, P=0.18), but significant difference between (POE+ACC)-NDA and Fisher-NDA method ( Z=2.423, P=0.015). In the validation group and training group, the optimal feature subset selected by MI-NDA method had the highest AUC of 0.888(95%CI 0.806-0.943) and 0.871(95%CI 0.741-0.951). Conclusion:Spectral CT radiomics quantitative features have great value on the differential diagnosis of lung cancer nodule and inflammatory nodule.
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Objective:To explore the value of gadolinium-ethoxybenzyl- diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI nomogram model for preoperative prediction of Ki-67 expression in hepatocellular carcinoma (HCC).Methods:Data of 85 patients of HCC confirmed by postoperative pathology, who underwent preoperative Gd-EOB-DTPA enhanced MRI between September 2016 and August 2019 in the First Affiliated Hospital of Soochow University were retrospectively evaluated. According to postoperative immunohistochemistry Ki-67 index, the 85 patients were divided into Ki-67 low expression group(Ki-67 index ≤10%, n=20) and Ki-67 high expression group (Ki-67 index >10%, n=65). Clinical data (hepatitis, cirrhosis, etc.), qualitative imaging parameters (tumor margin, capsule, etc.) were compared by χ 2 test and quantitative parameters [lesion-to-normal parenchyma ratio-arterial phase (LNR-AP), lesion-to-normal parenchyma ratio-portal phase (LNR-PP), lesion-to-normal parenchyma ratio-equilibrium phase (LNR-EP) and lesion-to-normal parenchyma ratio-hepatobiliary phase (LNR-HBP)] were compared by independent sample t test. The above statistically significant parameters were included in multivariate logistic regression to identify the independent predictors of Ki-67 high expression and then the nomogram model for predicting Ki-67 expression of HCC was established. Results:alpha-fetoprotein (AFP) tumor margin, arterial rim enhancement between the Ki-67 low expression group and the Ki-67 high expression group had significant differences (χ 2 were 8.196, 10.538 and 4.717, respectively, P<0.05). LNR-AP, LNR-PP, LNR-EP and LNR-HBP between the two groups had significant differences ( t were 2.929, 2.773, 2.890 and 3.437, respectively, P<0.05).The result of multivariate logistic regression revealed that AFP≥20 μg/L, non-smooth tumor margin and low LNR-HBP were the independent predictors of Ki-67 high expression (odds ratio were 4.090, 3.509 and 0.042, respectively, P<0.05).The Gd-EOB-DTPA enhanced MRI nomogram model for predicting Ki-67 expression of HCC was established successfully. The Area under the receiver operating characteristic curve of the nomogram was 0.837 and the corrected predictive curve fitted the ideal curve, which suggested the model had a good predictive efficiency. Conclusion:Gd-EOB-DTPA enhanced MRI nomogram model has great value in preoperative prediction of Ki-67 expression of HCC, which provided a personalized prediction method for Ki-67 expression in patient with HCC.
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Objective:To investigate the relationship between antithrombin Ⅲ (AT-Ⅲ) levels and CHA2DS2-VASc scores to assess the thromboembolism risk in patients with non-valvular atrial fibrillation (NVAF), and to explore the value of AT-Ⅲ in the risk assessment of thrombosis in these patients.Methods:We enrolled patients diagnosed with NVAF (observation group) and non-atrial fibrillation (control group), hospitalized in Fuwai Hospital of Chinese Academy of Medical Sciences from October 2018 to June 2019, and assessed the two groups for AT-Ⅲ, protein C, protein S, and lipid levels including lipoprotein (a), three acyl glycerin (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). Based on the CHA2DS2-VASc score, patients with NVAF and a score of less than 2 were assigned to the low-and middle-risk groups; the high-risk group consisted of patients with a score of 2 or more. The diagnostic performance of AT-Ⅲ was evaluated using receiver operating characteristic (ROC) curve analysis, and the risk factors for high CHA2DS2-VASc scores were analyzed using logistic regression.Results:Overall, 206 cases were enrolled in the observation group, including 54 women (26%; aged 59.85±11.06 years). The control group consisted of 76 cases, with 19 women (25%; aged 59.34±9.84 years). The two groups were gender ( χ2=0.043, P=0.836) and age ( t=0.352, P=0.725) matched. In the observation group, AT-Ⅲ activity (98.68%±11.37%) was significantly lower than that in the control group (110.87%±13.91%), demonstrating a statistically significant difference ( t=-6.841, P<0.001). In total, 102 cases (49.5%) were assigned to the high-risk group, with 104 cases (50.5%) in the low-and medium-risk groups. In the high-risk group, the AT-Ⅲ activity (93.67%±9.92%) was significantly lower than that in the low-and middle-risk groups (103.60%±10.56%), with a statistically significant difference observed ( t=6.953, P<0.001). In the high-risk group, protein C ［(94.34±26.61)% vs. (102.63±22.74)%］, TC ［(4.09±1.02) mmol/L vs. (4.69±0.97) mmol/L］, and LDL-C ［(2.18±0.83) mmol/L vs. (2.74±0.88) mmol/L］ levels were lower than those observed in the low-risk group (P<0.05). For NVAF screening, the AT-Ⅲ early warning threshold was 96.5%, and the area under the ROC curve was 0.746 (95% CI: 0.681-0.812, P<0.001). Based on logistic regression analysis, low AT-Ⅲ activity levels were an independent risk factor for high CHA2DS2-VASc scores in NVAF ( OR=7.282，95% CI: 3.098-17.117， P<0.001). Additionally, logistic regression analysis demonstrated that with increasing age ( OR=44.339, 95% CI: 15.207-129.276), lower levels of AT-Ⅲ ( OR=7.282, 95% CI: 3.098-17.117) and TC ( OR=4.349, 95% CI: 1.739-10.875), and higher CHA2DS2-VASc scores were observed for non-valvular AF ( P<0.05). Conclusion:A positive correlation exists between the CHA2DS2-VASc score and old age, low AT-Ⅲ activity, and low TC levels, indicating a high reference value for evaluating thrombosis in NVAF.
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Objective To evaluate the diagnostic value of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) parameters in diagnosing prostate cancer(PCa) in transition zone (TZ) and stratifying pathologic Gleason grade of prostate cancer. Methods A total of 55 patients who were undergoing preoperative muti?parameters MRI of T2WI, DWI, IVIM and DKI model for the exploration of prostate cancer (January 2015 to June 2017) with pathologically confirmed by MRI?transrectal ultrasound (TRUS) targeted fusion biopsy were retrospectively included. Parameters were postprocessed by IVIM models including quantitation of perfusion fraction (f), diffusivity (D) and pseudo?diffusivity (D*) and DKI models including the mean diffusivity (MD), mean kurtosis (MK) and fractional anisotropy (FA) by outlining the 3D VOI. Independent sample t?test was used to compare the differences in lesion parameters between prostate cancer and BPH, low?risk (BPH+Gleason score 6 points) and medium?high?risk lesions (Gleason score ≥7 points). Correlation between ADC values, IVIM and DKI parameters and Gleason scores were assessed with Spearman analysis.Receiver operating characteristic curve analysis was used to evaluate the efficacy of various parameters in the differential diagnosis of prostate cancer and BPH with low?risk or high?risk. Results 27 (36 focus) cases of PCa and 28 (40 focus) cases of benign prostatic hyperplasia(BPH) in PZ were included, meanwhile, the cases of GS≥7 and and BPH+(GS=6) were 33,43,respectively. There were significant differences in ADC, D, MD, MK, and FA between patients in PCa?BHP group and high?low risk group in TZ (P<0.05), D*and f had no significant differences (P>0.05). ADC and MD showed relatively higher negativity correlations (r were－0.585 and－0.489, P<0.05) with GS of PCa in TZ. ADC exhibited a higher area under the curve (AUC 0.864) compared with D with area under the curve (AUC 0.853), however, the difference is not significant (P>0.05). Of model DKI in diagnose of PCa and BPH, the highest classification accuracy was MD(AUC 0.796). The AUC derived from multiple model parameters in different combination of ADC+D value, ADC+MD value, and ADC+MD+D value were 0.892, 0.884, and 0.897, respectively. ADC and D of IVIM model showed a significance difference between GS≥7 and BPH+(GS=6) with a higher AUC of 0.826 and 0.743. The AUC was 0.851 of the combination of mean ADC and D, 0.846 of combination of mean ADC and MD, the AUC (0.856) of the combination of ADC, D and MD significant higher than any two combined parameters (P>0.05). Conclusions IVIM and DKI models may help to discriminate prostate cancer from BPH, and predict mid?higher GS PCa in TZ. But there is no significant advantage compared with ADC values. It is feasible to stratify the pathological grade of prostate cancer in TZ by mean ADC and MD.