Enhanced Computed Tomography Imaging Research Articles

Predictive value of CT-based extracellular volume fraction in the preoperative pathologic grading of rectal adenocarcinoma: A preliminary study

ObjectiveThis study aimed to investigate whether the extracellular volume fraction (ECV) determined using enhanced computed tomography (CT) can predict the pathologic grade of rectal adenocarcinoma. MethodsWe prospectively analyzed 43 patients with rectal adenocarcinoma confirmed surgically and pathologically and who had undergone preoperative enhanced CT imaging. The plain, arterial, venous, and balance phase values were recorded, and the absolute contrast-enhanced CT differences ΔS1 = HUarterial phase–HUplain scan, ΔS2 = HUvenous phase–HUplain scan, ΔS3 = HUbalance phase–HUplain scan were obtained. The ECV of the primary lesion was calculated by measuring the CT values of the regions of interest in the plain and balance phases. Patients were allocated to either a low-grade or a high-grade group based on the histologic grading standard for colorectal adenocarcinoma (nonspecial type, World Health Organization 2010 standard). The differences in the parameters between the two groups were evaluated for statistical significance. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency. ResultsThe 43 enrolled patients [12 in the high-grade group (27.9%) and 31 in the low-grade group (72.1%)] had an average age of 64.47 years. The arterial phase (P = 0.005) as well as ΔS1 (P = 0.006), ΔS3 (P = 0.021), and ECV (P＜ 0.001) differed significantly between the high-grade and low-grade groups, with ECV (P＜ 0.001) and ΔS3 (P = 0.042) being positively correlated with the pathologic grade and arterial phase (P = 0.025) and ΔS1 (P = 0.005) being negatively correlated. The ROC curve demonstrated that the best efficacy in evaluating the pathologic grade of rectal cancer was achieved by ECV, with an area under the curve of 0.892 (95% confidence interval: 0.757–1.000). The diagnostic threshold was 34.42%, sensitivity was 91.7%, and specificity was 83.9%. ConclusionThe use of enhanced CT to obtain ECV is helpful in predicting the pathologic grade of rectal cancer; however, this result has to be confirmed in a study with a larger sample size.

Accurate preoperative staging and HER2 status prediction of gastric cancer by the deep learning system based on enhanced computed tomography.

To construct the deep learning system (DLS) based on enhanced computed tomography (CT) images for preoperative prediction of staging and human epidermal growth factor receptor 2 (HER2) status in gastric cancer patients. The raw enhanced CT image dataset consisted of CT images of 389 patients in the retrospective cohort, The Cancer Imaging Archive (TCIA) cohort, and the prospective cohort. DLS was developed by transfer learning for tumor detection, staging, and HER2 status prediction. The pre-trained Yolov5, EfficientNet, EfficientNetV2, Vision Transformer (VIT), and Swin Transformer (SWT) were studied. The tumor detection and staging dataset consisted of 4860 enhanced CT images and annotated tumor bounding boxes. The HER2 state prediction dataset consisted of 38900 enhanced CT images. The DetectionNet based on Yolov5 realized tumor detection and staging and achieved a mean Average Precision (IoU=0.5) (mAP_0.5) of 0.909 in the external validation cohort. The VIT-based PredictionNet performed optimally in HER2 status prediction with the area under the receiver operating characteristics curve (AUC) of 0.9721 and 0.9995 in the TCIA cohort and prospective cohort, respectively. DLS included DetectionNet and PredictionNet had shown excellent performance in CT image interpretation. This study developed the enhanced CT-based DLS to preoperatively predict the stage and HER2 status of gastric cancer patients, which will help in choosing the appropriate treatment to improve the survival of gastric cancer patients.

Nomogram Based on CT Radiomics Features Combined With Clinical Factors to Predict Ki-67 Expression in Hepatocellular Carcinoma.

ObjectivesThe study developed and validated a radiomics nomogram based on a combination of computed tomography (CT) radiomics signature and clinical factors and explored the ability of radiomics for individualized prediction of Ki-67 expression in hepatocellular carcinoma (HCC).MethodsFirst-order, second-order, and high-order radiomics features were extracted from preoperative enhanced CT images of 172 HCC patients, and the radiomics features with predictive value for high Ki-67 expression were extracted to construct the radiomic signature prediction model. Based on the training group, the radiomics nomogram was constructed based on a combination of radiomic signature and clinical factors that showed an independent association with Ki-67 expression. The area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to verify the performance of the nomogram.ResultsSixteen higher-order radiomic features that were associated with Ki-67 expression were used to construct the radiomics signature (AUC: training group, 0.854; validation group, 0.744). In multivariate logistic regression, alfa-fetoprotein (AFP) and Edmondson grades were identified as independent predictors of Ki-67 expression. Thus, the radiomics signature was combined with AFP and Edmondson grades to construct the radiomics nomogram (AUC: training group, 0.884; validation group, 0.819). The calibration curve and DCA showed good clinical application of the nomogram.ConclusionThe radiomics nomogram developed in this study based on the high-order features of CT images can accurately predict high Ki-67 expression and provide individualized guidance for the treatment and clinical monitoring of HCC patients.

Prediction of early recurrence of hepatocellular carcinoma after liver transplantation based on computed tomography radiomics nomogram

BackgroundEarly recurrence results in poor prognosis of patients with hepatocellular carcinoma (HCC) after liver transplantation (LT). This study aimed to explore the value of computed tomography (CT)-based radiomics nomogram in predicting early recurrence of patients with HCC after LT. MethodsA cohort of 151 patients with HCC who underwent LT between December 2013 and July 2019 were retrospectively enrolled. A total of 1218 features were extracted from enhanced CT images. The least absolute shrinkage and selection operator algorithm (LASSO) logistic regression was used for dimension reduction and radiomics signature building. The clinical model was constructed after the analysis of clinical factors, and the nomogram was constructed by introducing the radiomics signature into the clinical model. The predictive performance and clinical usefulness of the three models were evaluated using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA), respectively. Calibration curves were plotted to assess the calibration of the nomogram. ResultsThere were significant differences in radiomics signature among early recurrence patients and non-early recurrence patients in the training cohort (P < 0.001) and validation cohort (P < 0.001). The nomogram showed the best predictive performance, with the largest area under the ROC curve in the training (0.882) and validation (0.917) cohorts. Hosmer-Lemeshow testing confirmed that the nomogram showed good calibration in the training (P = 0.138) and validation (P = 0.396) cohorts. DCA showed if the threshold probability is within 0.06-1, the nomogram had better clinical usefulness than the clinical model. ConclusionsOur CT-based radiomics nomogram can preoperatively predict the risk of early recurrence in patients with HCC after LT.

Computed Tomography-Based Radiomics for Differentiation of Thymic Epithelial Tumors and Lymphomas in Anterior Mediastinum.

ObjectiveTo investigate the differential diagnostic performance of computed tomography (CT)-based radiomics in thymic epithelial tumors (TETs) and lymphomas in anterior mediastinum.MethodsThere were 149 patients with TETs and 93 patients with lymphomas enrolled. These patients were assigned to a training set (n = 171) and an external validation set (n = 71). Dedicated radiomics prototype software was used to segment lesions on preoperative chest enhanced CT images and extract features. The multivariable logistic regression algorithm was used to construct three models according to clinico-radiologic features, radiomics features, and combined features, respectively. Performance of the three models was compared by using the area under the receiver operating characteristic curves (AUCs). Decision curve analysis was used to evaluate clinical utility of the three models.ResultsFor clinico-radiologic model, radiomics signature model, and combined model, the AUCs were 0.860, 0.965, 0.975 and 0.843, 0.961, 0.955 in the training cohort and the test cohort, respectively (all P<0.05). The accuracies of each model were 0.836, 0.895, 0.918 and 0.845, 0.901, 0.859 in the two cohorts, respectively (all P<0.05). Compared with the clinico-radiologic model, better diagnostic performances were found in the radiomics signature model and the combined model.ConclusionsRadiomics signature model and combined model exhibit outstanding and comparable differential diagnostic performances between TETs and lymphomas. The CT-based radiomics analysis might serve as an effective tool for accurately differentiating TETs from lymphomas before treatment.

Enhanced computed tomography imaging features predict tumor grade in pseudomyxoma peritonei

Because few studies have focused on the correlation between computed tomography (CT) signs and tumor grade in pseudomyxoma peritonei (PMP), we evaluated predictive value of abdominal enhanced CT in identifying high- vs. low-grade cases. In all, 75 patients diagnosed with PMP after surgery were consecutively recruited. The preoperative enhanced CT images were retrospectively analyzed for ascites, hepatic scalloping, omental and peritoneal lesion appearance, intralesional calcification and septa, and peripheral organ involvement. Logistic regression models were applied to analyze the relationship of CT signs with PMP grade. Receiver operating characteristic curves were generated to evaluate the potential utility of CT signs in detecting high-grade PMP. Massive ascites (P=0.017) and peritoneal solid nodules (P<0.001) were more common in high-grade cases. Multivariate logistic regression identified massive ascites [odds ratio (OR) =4.389, 95% confidence interval (CI): 1.210-15.921; P=0.025] and peritoneal solid nodules (OR =19.932, 95% CI: 3.560-111.596; P<0.001) as independent predictors of high-grade PMP. For the 55 patients with hepatic scalloping, the maximum thickness of mucin deposition at the hepatic scalloping wave in high-grade PMP was thinner than that in low-grade PMP (P=0.021). Thickness of mucin deposition at the hepatic scalloping wave (OR =0.346, 95% CI: 0.148-0.809; P=0.014) was an independent predictor of high-grade PMP, with a cutoff value of 18.6 mm. Cancer antigen 125 (CA125) combined with CT signs was significantly better at diagnosing high-grade PMP than was CA125 alone in both the overall patients [area under the ROC curve (AUC): 0.812 vs. 0.656; P=0.020] and those with hepatic scalloping (AUC: 0.859 vs. 0.600; P=0.007). The CT signs of high-grade PMP significantly differ from those of low-grade PMP, and thus combining CT signs with CA125 may be highly valuable for classifying PMP.

The Value of Color Doppler Ultrasound and CT Combined with Serum AFP Examination in the Diagnosis of Hepatocellular Carcinoma.

Objective To evaluate the value of the combination of color Doppler ultrasound, computed tomography (CT), and serum tumor marker alpha-fetoprotein (AFP) examination in the diagnosis of hepatocellular carcinoma (HCC). Methods 98 patients with HCC (malignant tumor group) and 50 liver lesion patients (benign control group), were selected for the study, and retrospective statistical methods were used to evaluate the diagnostic values of the three examinations on hepatocellular carcinoma. Results (1) When comparing color Doppler ultrasound blood flow parameters, the hepatic artery diameter, peak flow velocity, minimum flow velocity, and resistance index (RI) of hepatocellular carcinoma were significantly higher than those of the benign control group (P < 0.05), while the portal vein flow velocity was significantly lower than that of the control group (P < 0.05). (2) Enhanced CT imaging of hepatocellular carcinoma lesions showed mostly outflow-type enhancement changes, with high- or slightly high-density shadowing and uneven enhancement in the arterial phase, relatively low density and withdrawal of enhancement in the portal vein phase and delayed phase. (3) The serum AFP level of hepatocellular carcinoma patients was significantly higher than that of the benign control group (P < 0.01). (4) The sensitivity of color Doppler ultrasound, CT, and serum AFP alone for the diagnosis of HCC was 79.59%, 85.71%, and 66.33%, and the accuracy was 83.78%, 87.16%, and 74.32%, respectively, while the combination of the three tests could significantly increase the sensitivity to 96.94% and the accuracy to 93.92%, compared with each individual test (P < 0.01). Conclusion Color Doppler ultrasound and CT combined with serum AFP examination could significantly improve the sensitivity and accuracy of hepatocellular carcinoma diagnosis, reduce misdiagnosis, and facilitate early diagnosis and clinical early intervention.

An optimized morphology transform‐based diagnostic computed tomography image enhancement using edge map

AbstractIn computed tomography (CT) imaging, a low‐quality CT scan, or more precisely, a noisy low contrast CT image, may provide insufficient information for visual interpretation of impacted regions. The purpose of this article is to propose a novel optimal morphological transform‐based method for improving the noise and contrast of CT images in the wavelet domain. The low‐quality CT image is first transformed using the optimized morphology transform method, which determines the optimal value of the parameter included in the fundamental morphology transformation equation. The parameter's optimum value is determined using the particle swarm optimization (PSO) method. The proposed approach then employs the discrete wavelet transform (DWT) to decompose the input and transformed CT image into higher and lower sub‐bands. Following that, the lower sub‐bands are modified by applying the correction factor via singular value decomposition (SVD). The input image's higher sub‐band (HH) is denoised using an edge map (EM)‐based method. This produces enhanced CT images with modified lower and higher sub‐band. Finally, the modified sub‐bands and remaining unprocessed higher frequency sub‐bands are processed using the inverse discrete wavelet transform (IDWT), resulting in an enhanced CT image. Experiments and validations are conducted on a CT images obtained from a publicly available database to assess the proposed scheme's effectiveness qualitatively and quantitatively. Significant quantitative study shows that the proposed approach surpasses existing image enhancement approaches in terms of signal‐to‐noise ratio, discrete entropy, enhancement measurement, and contrast ratio. The proposed method generates higher‐quality CT images, which is advantageous for disease inspection and diagnosis.

Application of virtual noncontrast CT generation technology from intravenous enhanced CT based on deep learning in proton radiotherapy

ObjectiveThe aim of this study is to generate virtual noncontrast (VNC) computed tomography (CT) from intravenous enhanced CT by using Unet convolutional neural network (CNN). The differences among enhanced, VNC, and noncontrast CT in proton dose calculation were compared. MethodsA total of 30 groups of CT images of patients who received enhanced and noncontrast CT were selected. Enhanced and noncontrast CT were registered. Among these patients, 20 groups of the CT images were chosen as the training set. Enhanced CT images were used as the input, and the corresponding noncontrast CT images were used as output to train the Unet neural network. The remaining 10 groups of CT images were chosen as the test set. VNC images were generated by the trained Unet neural network. The same proton radiotherapy plan for esophagus cancer was designed based on three images. Proton dose distributions in enhanced, VNC, and noncontrast CT were calculated. The relative dose differences in enhanced CT with VNC and noncontrast CT were analyzed. ResultsThe mean absolute error (MAE) of the CT values between enhanced and noncontrast CT was 32.3 ± 2.6 HU. The MAE of the CT values between VNC and noncontrast CT was 6.7 ± 1.3 HU. The mean values of the enhanced CT in the great vessel, heart, lung, liver, and spinal cord were significantly higher than those of noncontrast CT, he differences were 97, 83, 42, 40, and 10 HU, respectively. The mean values of the VNC CT showed no significant difference with noncontrast CT. The differences among enhanced, VNC, and noncontrast CT in terms of the average relative proton dose for clinical target volume (CTV), heart, great vessels, and lung were also investigated. The average relative proton doses of the enhanced CT for these organs were significantly lower than those of noncontrast CT. The largest difference was observed in the great vessel, while the differences in other organs were relatively small. The γ-passing rates of the enhanced and VNC CT were calculated by 2% dose difference and 2 mm distance to agreement. Results showed that the mean γ-passing rate of VNC CT was significantly higher than enhanced CT (p < 0.05). ConclusionsThe proton radiotherapy design based on enhanced CT increased the range error, thereby resulting in calculation errors of the proton dose. Therefore, a technology that can be used to generate VNC CT from enhanced CT based on Unet neural network was proposed. The proton dose calculated based on VNC CT images was essentially consistent with that based on noncontrast CT.

Computed Tomography Image Features under Denoising Algorithm for Benign and Malignant Diagnosis of Renal Parenchymal Tumor.

To improve the quality of computed tomography (CT) images and provide help for benign and malignant diagnosis of renal parenchymal tumors, the independent component analysis (ICA) denoising algorithm was used. An improved ICA X-ray CT (X-CT) medical image denoising algorithm was proposed. ICA provided a higher signal-to-noise ratio for CT image denoising. Forty patients with renal tumor were selected as the observation group. The CT image performance of patients was evaluated by the denoising algorithm and compared with the wavelet transform algorithm, and the peak signal-to-noise ratio of the proposed algorithm was analyzed and compared. The results showed that among the 40 patients with renal tumors, 12 were renal clear cell carcinoma cases and 28 were cystic renal carcinoma cases. The accuracy of the enhanced CT image was 93.8%, and that of the CT image using the denoising algorithm was 96.3%; the difference between the two was significant (P < 0.05). The peak signal-to-noise ratio (PSNR) of the algorithm proposed was higher than the PSNR values of CT and noisy images. The PSNR of the proposed algorithm was significantly higher than that of mean filtering. The root mean square error (RMSE) algorithm of the proposed algorithm was significantly lower than that of the mean algorithm in image data processing (P < 0.05), which showed the superiority of the proposed algorithm. Enhanced CT can be staged significantly. In conclusion, the algorithm had a significant effect on the edge contour of detailed features, and the accuracy of CT images based on intelligent calculation was significantly higher than that of conventional CT images for benign and malignant renal parenchyma tumors, which was worth promoting in clinical diagnosis.

TMSF-Net: Multi-series fusion network with treeconnect for colorectal tumor segmentation

PurposeColorectal tumors are common clinical diseases. Automatic segmentation of colorectal tumors captured in computed tomography (CT) images can provide numerous possibilities for computer-assisted treatment. Obtaining large datasets is expensive, and completing labeling is time- and manpower-consuming. To solve the challenge using a limited pathological dataset, this paper proposes a multi-series fusion network with treeconnect (TMSF-Net), which can automatically achieve colorectal tumor segmentation using CT images. MethodsTo drive the TMSF-Net, three-series enhanced CT images were collected from all patients to improve the data characteristics. In the TMSF-Net, the coding path was designed as a three-branch structure to realize the feature extraction of the different series. Subsequently, the three branches were merged to start the feature analysis in the decoding path. To achieve the objective of feature fusion, different layers in the decoding path fused feature maps from the upper layer in the encoding path to achieve a cross-scale fusion. In addition, to reduce the problem of parameter redundancy, this study adopted a three-dimensional treeconnect to complete data connection on three branches. ResultsA total of 22 cases were conducted by ablation and comparative experiments to test the TMSF-Net. The results showed that the TMSF-Net can improve the network performance by multiseries fusion, and its expressiveness is better than many classic networks. ConclusionThe TMSF-Net is a many-to-one structure network, which can enhance the network learning ability and improve the analysis of potential features. Therefore, it yields good results in colorectal tumor segmentation. It can provide a new direction for neural network models based on feature fusion.

Value of perfusion parameters histogram analysis of triphasic CT in differentiating intrahepatic mass forming cholangiocarcinoma from hepatocellular carcinoma

We aim to gain further insight into identifying differential perfusion parameters and corresponding histogram parameters of intrahepatic mass-forming cholangiocarcinoma (IMCC) from hepatocellular carcinomas (HCCs) on triphasic computed tomography (CT) scans. 90 patients with pathologically confirmed HCCs (n = 54) and IMCCs (n = 36) who underwent triple-phase enhanced CT imaging were included. Quantitative analysis of CT images derived from triphasic CT scans were evaluated to generate liver perfusion and histogram parameters. The differential performances, including the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity were assessed. The mean value, and all thepercentiles of the arterial enhancement fraction (AEF) were significantly higher in HCCs than in IMCCs. The difference in hepatic arterial blood supply perfusion (HAP) and AEF (ΔHAP = HAPtumor − HAPliver, ΔAEF = AEFtumor − AEFliver) for the mean perfusion parameters and all percentile parameters between tumor and peripheral normal liver were significantly higher in HCCs than in IMCCs. The relative AEF (rAEF = ΔAEF/AEFliver), including the mean value and all corresponding percentile parameters were statistically significant between HCCs and IMCCs. The 10th percentiles of the ΔAEF and rAEF had the highest AUC of 0.788 for differentiating IMCC from HCC, with sensitivities and specificities of 87.0%, 83.3%, and 61.8%, 64.7%, respectively. Among all parameters, the mean value of ∆AEF, the 75th percentiles of ∆AEF and rAEF, and the 25th percentile of HFtumor exhibited the highest sensitivities of 94.4%, while the 50th percentile of rAEF had the highest specificity of 82.4%. AEF (including ΔAEF and rAEF) and the corresponding histogram parameters derived from triphasic CT scans provided useful value and facilitated the accurate discrimination between IMCCs and HCCs.

A Reliable Prediction Model for Renal Cell Carcinoma Subtype Based on Radiomic Features from 3D Multiphase Enhanced CT Images.

Background This study aimed to develop a prediction model to distinguish renal cell carcinoma (RCC) subtypes. Methods The radiomic features (RFs) from 5 different computed tomography (CT) phases were used in the prediction models: noncontrast phase (NCP), corticomedullary phase (CMP), nephrographic phase (NP), excretory phase (EP), and all-phase (ALL-P). Results For the ALL-P model, all of the RFs obtained from the 4 single-phase images were combined to 420 RFs. The ALL-P model performed the best of all models, with an accuracy of 0.80; the sensitivity and specificity for clear cell RCC (ccRCC) were 0.85 and 0.83; those for papillary RCC (pRCC) were 0.60 and 0.91; those for chromophobe RCC (cRCC) were 0.66 and 0.91, respectively. Binary classification experiments showed for distinguishing ccRCC vs. not-ccRCC that the area under the receiver operating characteristic curve (AUC) of the ALL-P and CMP models was 0.89, but the overall sensitivity/specificity/accuracy of the ALL-P model was better. For cRCC vs. non-cRCC, the ALL-P model had the best performance. Conclusions A reliable prediction model for RCC subtypes was constructed. The performance of the ALL-P prediction model was the best as compared to individual single-phase models and the traditional prediction model.

Improved Particle Swarm Optimization for Detection of Pancreatic Tumor using Split and Merge Algorithm

ABSTRACT Pancreatic cancer is the fourth leading cause of cancer-related death worldwide. Pancreatic tumours are characterised by a peculiar cell progression in intestinal enzymes and hormone-producing cells. Patients with pancreatic tumours have a shorter survival period depending on the type of tumour they have. Enhancing patient survival, diagnosing patients with malignant pancreatic tumours as soon as possible is the need of hour. The commonly used approach for diagnosis and clinical disease staging of pancreatic cancer is Computed Tomography (CT). However, because most abdominal CT images contain noise in addition to visceral fat in the vicinity of the pancreas, prompt detection is difficult, which requires an algorithm that distinguishes the anatomical structure clearly. This paper proposes, Particle Swarm Optimisation incorporating Split and Merge Segmentation to obtain enhanced CT images of pancreatic tumour. The image undergoes image processing procedures such as segmentation using PSO and fetch an enhanced output image with the help of gaussian filter. The algorithm outperforms Conventional PSO in terms of Entropy and Contrast, according to experimental results. The improved CT scans aid in the early detection and diagnosis of pancreatic cancer.

Establishing a survival prediction model for esophageal squamous cell carcinoma based on CT and histopathological images

Currently, the incidence of esophageal squamous cell carcinoma (ESCC) in China is high and its prognosis is poor. To evaluate the prognosis of patients with ESCC, we performed computerized quantitative analyses on diagnostic computed tomography (CT) and digital histopathological slices. A retrospective study was conducted to assess the prognosis of ESCC in 153 patients who underwent esophagectomy, and the cohort was selected based on strict clinical criteria. Each patient had an enhanced CT image, and there were two imaging protocols for CT images of all patients. Each patient in the cohort also had a histopathological tissue slide after hematoxylin–eosin staining. Under an electron microscope, the tissue slide was scanned as an image of large size. We then performed quantitative analyses to identify factors related to the prognosis of ESCC on digital histological images and diagnostic CT images. For CT images, we used the radiomics method. For histological images, we designed a set of quantitative features based on machine learning algorithms, such as K-means and principal component analysis. These features describe the patterns of different cell types in histopathological images. Subsequently, we used the survival analysis model established using only CT image features as the baseline. We also compared multiple machine learning models and adopted a five-fold cross-validation method to establish a robust survival model. In establishing survival models, we first used CT image features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.624. Then we used histopathlogical features to establish survival models, and the C-index from the Weibull Cox model on the test set reached 0.664, which was obviously better than CT’s. Lastly, we combined CT image features and histopathological image features to establish survival models. The performance was better than that in the models built using only CT image features or histopathological image features, and the C-index from the regularized Cox model on the test set reached 0.694. We also proved the effectiveness of the quantified histopathological image features in terms of prognosis using the log-rank test. Histopathological image features are more relevant to prognosis than features extracted from CT images using radiomics. The results of this study provide clinicians with a reference to improve the survival rate of patients with ESCC after surgery. These results have implications for advancing the process of explaining the poor prognosis of esophageal cancer.

Spontaneous rupture of the umbilical vein associated with liver cirrhosis: A case report.

IntroductionA decompensated cirrhosis sometimes develops collateral circulation due to severe fibrosis. Rupture of this collateral circulation can induce a fatal situation.Case presentationA 59-year-old man with alcoholic cirrhosis was admitted to our emergency department with a chief complaint of impaired consciousness. The patient had hypotension upon arrival, and enhanced computed tomography (CT) revealed a massive hemoperitoneum. Imaging examinations, including interventional radiography, could not identify the source of bleeding preoperatively; therefore, emergency surgery was performed. Intraoperatively, a collapsed small vessel connecting liver segment 4 and the expanded umbilical vein as collateral circulation due to portal hypertension was detected as the source of bleeding. Ligating the stump of the small vessel and postoperative intensive care saved the patient's life. The patient was discharged from our hospital 14 days after the surgery.Clinical discussionA rupture of the umbilical vein associated with cirrhosis is rare and causes a critical situation because of the fast blood flow in the collateral circulation caused by portal hypertension. Moreover, the amount of bleeding tends to increase since the abdominal cavity is a free space and patients with cirrhosis have coagulopathies, including thrombocytopenia and prolonged prothrombin time. Although a retrospective review of the enhanced CT image could identify the minute findings, arterial portography was insufficient to detect bleeding from the umbilical vein. Therefore, emergency exploratory laparotomy was required for the diagnosis and treatment.ConclusionRupture of the reopened umbilical vein can cause idiopathic spontaneous hemoperitoneum in patients with decompensated cirrhosis.

A nomogram combined with radiomics features, albuminuria, and metabolic syndrome to predict the risk of myometrial invasion of bladder cancer.

BackgroundTo establish a preoperative prediction model of myometrial invasion of bladder cancer (BC) based on the radiomics characteristics of multi-parameter thin-slice enhanced computed tomography (CT) imaging.MethodsData from 100 patients with BC were analyzed retrospectively. The patients were divided into two groups: muscular invasive BC and non-muscular invasive BC. The tumor region was segmented from enhanced CT images (arterial- and venous-phase calibration maps) of all patients using Slicer-3D software. We extracted 1,223 texture features from tumor image data based on the shape and gray-level co-occurrence matrix, gray size region matrix, gray run-length matrix, adjacent gray difference matrix, and gray correlation matrix. The patients were randomly divided into a training group (n=70) and a verification group (n=30) in a 7:3 ratio. Interclass correlation coefficients >0.75, least absolute shrinkage, and selection operator regression were used for feature selection. The prediction model was established by combining Rad-score, independent clinical factors, and support vector machine (SVM), and a radiomics nomogram was constructed. The nomogram was tested using the consistency index, calibration curve, time-dependent receiver operating characteristic curve, and clinical decision curve to predict the myometrial invasion of the bladder preoperatively.ResultsSix radiomics features that were significantly related to myometrial invasion of BC were selected to construct a predictive model. The area under the curve (AUC) values of training group and verification group based on SVM were 0.898 (95% CI: 0.820–0.976) and 0.702 (95% CI: 0.495–0.909), respectively. Single factor and multiple factor analysis showed that albuminuria (95% CI: 0.243–2.206, P=0.0014) and metabolic syndrome (95% CI: 0.850–2.935, P<0.001) were independent influencing factors of BC myometrial invasion. Clinical factors and 11 radiomics features were used to construct a comprehensive model for predicting the pathological grade of BC (radiomics + clinical). After a comprehensive comparison, we found that the overall effectiveness of the model (radiomics + clinical) was the highest (AUC =0.8457).ConclusionsBased on the multi-parameter thin-layer enhanced CT radiomics feature can be used as a potential independent predictor of BC myometrial invasion, the model based on parameters can initially quantitatively characterize the risk of myometrial invasion, and has excellent potential for predicting myometrial invasion of BC.

Deep Learning on Enhanced CT Images Can Predict the Muscular Invasiveness of Bladder Cancer.

BackgroundClinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.MethodsA total of 441 BCa patients were retrospectively enrolled from two centers and were divided into development (n=183), tuning (n=110), internal validation (n=73) and external validation (n=75) cohorts. The model was built based on nephrographic phase images of preoperative CT urography. Receiver operating characteristic (ROC) curves were performed and the area under the ROC curve (AUC) for discrimination between muscle-invasive BCa and non-muscle-invasive BCa was calculated. The performance of the model was evaluated and compared with that of the subjective assessment by two radiologists.ResultsThe DL model exhibited relatively good performance in all cohorts [AUC: 0.861 in the internal validation cohort, 0.791 in the external validation cohort] and outperformed the two radiologists. The model yielded a sensitivity of 0.733, a specificity of 0.810 in the internal validation cohort and a sensitivity of 0.710 and a specificity of 0.773 in the external validation cohort.ConclusionThe proposed DL model based on CT images exhibited relatively good prediction ability of muscle-invasive status of BCa preoperatively, which may improve individual treatment of BCa.

Rare primary malignant peripheral nerve sheath tumor of the left testis: A case report.

Malignant peripheral nerve sheath tumor (MPNST) is a rare disease. The present study discusses the case of a 30-year-old male patient who presented with complaints of pain in the left testis and groin over 1 month. Ultrasonography and computed tomography (CT) imaging revealed the presence of a space-occupying lesion localized in the left testis. The mass was completely resected using an open surgical approach and was diagnosed as a primary MPNST of the left testis on postoperative histopathological examination. As this type of tumor is rare, there is currently no standard diagnostic or treatment method for MPNST. Pathological examination, enhanced CT imaging and immunohistochemical investigation are helpful for establishing the diagnosis and surgical resection is considered to be an effective treatment.

Value of perfusion parameters and histogram analysis of triphasic computed tomography in pre-operative prediction of histological grade of hepatocellular carcinoma.

Background:Pre-operative non-invasive histological evaluation of hepatocellular carcinoma (HCC) remains a challenge. Tumor perfusion is significantly associated with the development and aggressiveness of HCC. The purpose of the study was to evaluate the clinical value of quantitative liver perfusion parameters and corresponding histogram parameters derived from traditional triphasic enhanced computed tomography (CT) scans in predicting histological grade of HCC.Methods:Totally, 52 patients with HCC were enrolled in this retrospective study and underwent triple-phase enhanced CT imaging. The blood perfusion parameters were derived from triple-phase CT scans. The relationship of liver perfusion parameters and corresponding histogram parameters with the histological grade of HCC was analyzed. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal ability of the parameters to predict the tumor histological grade.Results:The variance of arterial enhancement fraction (AEF) was significantly higher in HCCs without poorly differentiated components (NP-HCCs) than in HCCs with poorly differentiated components (P-HCCs). The difference in hepatic blood flow (HF) between total tumor and total liver flow (ΔHF = HFtumor − HFliver) and relative flow (rHF = ΔHF/HFliver) were significantly higher in NP-HCCs than in P-HCCs. The difference in portal vein blood supply perfusion (PVP) between tumor and liver tissue (ΔPVP) and the ΔPVP/liver PVP ratio (rPVP) were significantly higher in patients with NP-HCCs than in patients with P-HCCs. The area under ROC (AUC) of ΔPVP and rPVP were both 0.697 with a high sensitivity of 84.2% and specificity of only 56.2%. The ΔHF and rHF had a higher specificity of 87.5% with an AUC of 0.681 and 0.673, respectively. The combination of rHF and rPVP showed the highest AUC of 0.732 with a sensitivity of 57.9% and specificity of 93.8%. The combined parameter of ΔHF and rPVP, rHF and rPVP had the highest positive predictive value of 0.903, and that of rPVP and ΔPVP had the highest negative predictive value of 0.781.Conclusion:Liver perfusion parameters and corresponding histogram parameters (including ΔHF, rHF, ΔPVP, rPVP, and AEFvariance) in patients with HCC derived from traditional triphasic CT scans may be helpful to non-invasively and pre-operatively predict the degree of the differentiation of HCC.