Dual-Energy Computed Tomography Imaging in Early-Stage Hepatocellular Carcinoma: A Preliminary Study

Background. This study aims to evaluate the application of dual-energy computed tomography (DECT) for multiparameter quantitative measurement in early-stage hepatocellular carcinoma (HCC). Methods. The study retrospectively enrolled 30 patients with early-stage HCC and 43 patients with early-stage HCC who received radiofrequency ablation (RFA) and underwent abdomen enhanced CT scans in GSI mode. The GSI viewer was used for image display and data analysis. The regions of interest (ROIs) were delineated in the arterial phase and the venous phase. The optimal single energy value, CT values on different energy levels (40 keV, 70 keV, 100 keV, and 140 keV), the optimal energy level, the slope of the spectral attenuation curve, the effective atomic number (Zeff), iodine concentration (IC), water concentration (WC), normalized iodine concentration (NIC), and normalized water concentration (NWC) are measured and quantitatively analyzed. Results. The CT values of early-stage HCC at different single energy levels in dual phases were significantly different, and the single energy values were negatively correlated with the CT values. In the arterial phase and the venous phase, the optimal energy values for the best contrast-to-noise ratio were (68.34 ± 3.20) keV and (70.14 ± 2.01) keV, respectively. The slope of the spectral attenuation curve showed a downward trend at 40 keV, 70 keV, 100 keV, and 140 keV, but there was no statistically significant difference P > 0.05 . Zeff was positively correlated with IC and standardized IC, but has no significant correlation with WC and NWC in dual phases. Conclusion. DECT imaging contains multiparameter information and has different application values for early-stage HCC, and it is necessary to select the parameters reasonably for personalized and comprehensive evaluation.

Application of CT Radiomics Combined with Machine Learning Methods in Predicting the Recurrence or Metastasis of Gastrointestinal Stromal Tumors

Background: The study aimed to evaluate the diagnostic performance of machine learning-based CT radiomics models for predicting the recurrence and metastasis of gastrointestinal stromal tumors (GISTs) preoperatively.Methods: A total of 382 patients with histopathological confirmed GISTs were retrospectively included. According to postoperative follow-up, patients were classified into non-recurrence and metastasis group (NRM) and recurrence or metastasis group (RM). Radiomics features were extracted from arterial and portal venous phase CT images. Four feature selection methods and ten machine learning techniques were used to train predicting models on training cohort with internal validation by 10-fold cross-validation. F1 score was used to evaluate the performance of the classification model. The best model of two phase were stacked to build an ensemble model. The area under the curve (AUC), recall, precision, accuracy, and F1 score were used to evaluate the performance of the models and compare with clinical criteria based on diameter.Results: Eighty machine learning models in two phases were built and the ensemble model was integrated by analysis of variance and Naive Bayes (ANOVA_NB) model in arterial phase which selected only 5 features provided the highest F1 Score of 0.560 and Kruskal Wallis and Adaptive Boosting (KW_ AdaBoost) model in venous phase which selected only 4 features provided the highest F1 Score of 0.500. The AUC of the generated ensemble model and the clinical criteria showed no difference (0.866 vs 0.857; DeLong Test, P = 0.865). But the ensemble model had higher accuracy (0.961), recall (0.826), precision (0.905), F1 Score (0.864), and the area under the Precision-Recall curve (0.774; 95%CI, 0.552 - 0.917), compared with clinical criteria, of which, the accuracy was 0.942, recall was 0.367, precision was 0.478, the F1 Score was 0.415 and the area under the Precision-Recall curve was 0.354(95%CI, 0.552 - 0.917).Conclusions: Our findings highlight the potential of machine learning techniques based on CT radiomics in the prediction of recurrence and metastasis of GISTs preoperatively.

Application of CT Radiomics Combined with Machine Learning Methods in Predicting the Recurrence or Metastasis of Gastrointestinal Stromal Tumors

Background The study aimed to evaluate the diagnostic performance of machine learning-based CT radiomics models for predicting the recurrence and metastasis of gastrointestinal stromal tumors (GISTs) preoperatively. Methods A total of 382 patients with histopathological confirmed GISTs were retrospectively included. According to postoperative follow-up, patients were classified into non-recurrence and metastasis group (NRM) and recurrence or metastasis group (RM). Radiomics features were extracted from arterial and portal venous phase CT images. Four feature selection methods and ten machine learning techniques were used to train predicting models on training cohort with internal validation by 10-fold cross-validation. F1 score was used to evaluate the performance of the classification model. The best model of two phase were stacked to build an ensemble model. The area under the curve (AUC), recall, precision, accuracy, and F1 score were used to evaluate the performance of the models and compare with clinical criteria based on diameter. Results Eighty machine learning models in two phases were built and the ensemble model was integrated by analysis of variance and Naive Bayes (ANOVA_NB) model in arterial phase which selected only 5 features provided the highest F1 Score of 0.560 and Kruskal Wallis and Adaptive Boosting (KW_ AdaBoost) model in venous phase which selected only 4 features provided the highest F1 Score of 0.500. The AUC of the generated ensemble model and the clinical criteria showed no difference (0.866 vs 0.857; DeLong Test, P = 0.865). But the ensemble model had higher accuracy (0.961), recall (0.826), precision (0.905), F1 Score (0.864), and the area under the Precision-Recall curve (0.774; 95%CI, 0.552 - 0.917), compared with clinical criteria, of which, the accuracy was 0.942, recall was 0.367, precision was 0.478, the F1 Score was 0.415 and the area under the Precision-Recall curve was 0.354(95%CI, 0.552 - 0.917). Conclusions Our findings highlight the potential of machine learning techniques based on CT radiomics in the prediction of recurrence and metastasis of GISTs preoperatively.

Imaging features and differences among the three primary malignant non-Wilms tumors in children

Background The pathology, treatment and prognosis of malignant non-Wilms tumors (NWTs) are different, so it is necessary to differentiate these types of tumors. The purpose of this study was to review the clinical and imaging features of malignant NWTs and features of tumor metastasis. Methods We retrospectively analyzed the CT images of 65 pediatric patients with NWTs from March 2008 to July 2020, mainly including clear cell sarcoma of the kidney (CCSK), malignant rhabdomyoma tumor of the kidney (MRTK) and renal cell carcinoma (RCC). Available pretreatment contrast-enhanced abdominal CT examinations were reviewed. The clinical features of the patients, imaging findings of the primary mass, and locoregional metastasis patterns were evaluated in correlation with pathological and surgical findings. Results The study included CCSK (22 cases), MRTK (27 cases) and RCC (16 cases). There were no significant differences observed among the sex ratios of CCSK, MRTK and RCC (all P > 0.05). Among the three tumors, the onset age of MRTK patients was the smallest, while that of RCC patients was the largest (all P < 0.05). The tumor diameter of CCSK was larger than that of MRTK and RCC (all P < 0.001). For hemorrhage and necrosis, the proportion of MRTK patients was larger than that of the other two tumors (P = 0.017). For calcification in tumors, the proportion of calcification in RCC was highest (P = 0.009). Only MRTK showed subcapsular fluid (P < 0.001). In the arterial phase, the proportion of slight enhancement in RCC was lower than that in the other two tumors (P = 0.007), and the proportion of marked enhancement was the highest (P = 0.002). In the venous phase, the proportion of slight enhancement in RCC was lower than that in the other two tumors (P < 0.001). Only CCSK had bone metastasis. There was no liver and lung metastasis in RCC. Conclusions NWTs have their own imaging and clinical manifestations. CCSK can cause vertebral metastasis, MRTK can cause subcapsular effusion, and RCC tumor density is usually high and calcification. These diagnostic points can play a role in clinical diagnosis.

Liver Enhancement on Computed Tomography Is Suboptimal in Patients with Liver Steatosis

This study’s aim was twofold. Firstly, to assess liver enhancement quantitatively and qualitatively in steatotic livers compared to non-steatotic livers on portal venous computed tomography (CT). Secondly, to determine the injection volume of contrast medium in patients with severe hepatic steatosis to improve the image quality of the portal venous phase. We retrospectively included patients with non-steatotic (n = 70), the control group, and steatotic livers (n = 35) who underwent multiphase computed tomography between March 2016 and September 2020. Liver enhancement was determined by the difference in attenuation in Hounsfield units (HU) between the pre-contrast and the portal venous phase, using region of interests during in three different segments. Liver steatosis was determined by a mean attenuation of ≤40 HU on unenhanced CT. Adequate enhancement was objectively defined as ≥50 ΔHU and subjectively using a three-point Likert scale. Enhancement of non-steatotic and steatotic livers were compared and associations between enhancement and patient- and scan characteristics were analysed. Enhancement was significantly higher among the control group (mean 51.9 ± standard deviation 11.5 HU) compared to the steatosis group (40.6 ± 8.4 HU p for difference < 0.001). Qualitative analysis indicated less adequate enhancement in the steatosis group: 65.7% of the control group was rated as good vs. 8.6% of the steatosis group. We observed a significant correlation between enhancement, and presence/absence of steatosis and grams of iodine per total body weight (TBW) (p < 0.001; adjusted R2 = 0.303). Deduced from this correlation, theoretical contrast dosing in grams of Iodine (g I) can be calculated: g I = 0.502 × TBW for non-steatotic livers and g I = 0.658 × TBW for steatotic livers. Objective and subjective enhancement during CT portal phase were significantly lower in steatotic livers compared to non-steatotic livers, which may have consequences for detectability and contrast dosing.

Diagnostic accuracy of color-coded virtual noncalcium reconstructions derived from portal venous phase dual-energy CT in the assessment of lumbar disk herniation

Objectives To investigate the diagnostic accuracy of color-coded contrast-enhanced dual-energy CT virtual noncalcium (VNCa) reconstructions for the assessment of lumbar disk herniation compared to unenhanced VNCa imaging. Methods A total of 91 patients were retrospectively evaluated (65 years ± 16; 43 women) who had undergone third-generation dual-source dual-energy CT and 3.0-T MRI within an examination interval up to 3 weeks between November 2019 and December 2020. Eight weeks after assessing unenhanced color-coded VNCa reconstructions for the presence and degree of lumbar disk herniation, corresponding contrast-enhanced portal venous phase color-coded VNCa reconstructions were independently analyzed by the same five radiologists. MRI series were additionally analyzed by one highly experienced musculoskeletal radiologist and served as reference standard. Results MRI depicted 210 herniated lumbar disks in 91 patients. VNCa reconstructions derived from contrast-enhanced CT scans showed similar high overall sensitivity (93% vs 95%), specificity (94% vs 95%), and accuracy (94% vs 95%) for the assessment of lumbar disk herniation compared to unenhanced VNCa images (all p > .05). Interrater agreement in VNCa imaging was excellent for both, unenhanced and contrast-enhanced CT (κ = 0.84 vs κ = 0.86; p > .05). Moreover, ratings for diagnostic confidence, image quality, and noise differed not significantly between unenhanced and contrast-enhanced VNCa series (all p > .05). Conclusions Color-coded VNCa reconstructions derived from contrast-enhanced dual-energy CT yield similar diagnostic accuracy for the depiction of lumbar disk herniation compared to unenhanced VNCa imaging and therefore may improve opportunistic retrospective lumbar disk herniation assessment, particularly in case of staging CT examinations. Key Points • Color-coded dual-source dual-energy CT virtual noncalcium (VNCa) reconstructions derived from portal venous phase yield similar high diagnostic accuracy for the assessment of lumbar disk herniation compared to unenhanced VNCa CT series (94% vs 95%) with MRI serving as a standard of reference. • Diagnostic confidence, image quality, and noise levels differ not significantly between unenhanced and contrast-enhanced portal venous phase VNCa dual-energy CT series. • Dual-source dual-energy CT might have the potential to improve opportunistic retrospective lumbar disk herniation assessment in CT examinations performed for other indications through reconstruction of VNCa images.

The Application of Dual-phase Enhanced CT Scan in Distinguishing Adrenal Ganglioneuromas From Adrenal Lipid-poor Adenomas

Background:The utility of dual-phase enhanced CT scan in distinguishing ganglioneuromas from lipid-poor adenomas has not been reported. We aimed to prospectively compare CT findings helpful in distinguishing adrenal ganglioneuromas from adrenal lipid-poor adenomas. Methods: We estimated the CT findings of 258 adrenal masses (42 ganglioneuromas, 216 lipid-poor adenomas) in 258 patients from July 2008 to July 2020 with ganglioneuromas and July 2016 to July 2020 with lipid-poor adenomas. The CT features between ganglioneuromas and lipid-poor adenomas were compared. Results:Significant differences were detected in CT value of unenhanced (CTU), CT value of arterial phase (CTA), CT value of venous phase (CTV), degree of enhancement in arterial phase (DEAP), degree of enhancement in portal venous phase (DEPP), age, tumor size [long diameter (LD), short diameter (SD), mean diameter (MD)], shape, calcification between the ganglioneuroma and lipid-poor adenoma groups (P < 0.05).The results of receiver operating characteristics (ROC) analyses showed that areas under ROC curves (AUC) of CTU, CTA and CTV were 0.713, 0.878, and 0.914, respectively. When the cut-off values were set at 22.5 HU, 51.5 HU, and 53.5 HU for CTU, CTA, and CTV, respectively the three parameters had a sensitivity of 46.8%, 67.6%, and 88.0% and a specificity of 100%, 100%, and 88.1% in distinguishing between ganglioneuromas and lipid-poor adenomas.Conclusion: Dual-phase enhanced abdominal CT can exhibit some of the primary imaging characteristics of ganglioneuromas and lipid-poor adenomas used to distinguish between these two entities.

Correlation of Small Bowel Gastrointestinal Stromal Tumor Computed Tomography Features with the Grade of Pathological Risk

Objectives: The aim of this study was to investigate the diagnostic value of computerized tomography (CT) features of small intestinal stromal tumors in terms of their degree of risk. Methods: The clinical data and CT data of 107 patients with small intestinal stromal tumors confirmed by surgery and pathology in our hospital from June 2012 to October 2020 were selected. According to the results of postoperative pathological risk, the patients were divided into high-risk and low-risk groups, wherein 67 cases were in high-risk group and 40 cases were in the low-risk group The maximum diameter, solid component plain scan, arterial phase CT value, venous phase CT value, and delayed phase CT value of the two groups were measured, and the enhancement degree of arterial phase, venous phase, delayed CT value, and lesion enhancement mode were calculated. The difference between the two groups was compared. An independent sample t-test was used to compare quantitative indices, and the chi-squared test or Fisher’s exact test was used for qualitative index comparison. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of the arterial phase CT value, venous phase CT value, delayed phase CT value, arterial phase enhancement degree, venous phase enhancement degree, delayed phase enhancement degree, and the enhanced net value-added in the risk degree of SBGISTs. The relationship between preoperative imaging findings and tumor risk was retrospectively analyzed. Results: Univariate analysis showed that there were significant differences in the lesion location, growth pattern, lesion ulcer, necrotic cystic degeneration, lobulation, boundary with surrounding tissues, plain scan density and lesion enhancement mode, CT value in arterial phase, increment in arterial phase, CT value in venous phase, increment in venous phase, CT value in delayed phase, increment in delayed phase, and enhancement value in lesion between the two groups (P < 0.05); there were no significant differences in sex, age, calcification, bleeding, clinical symptoms, and CT value (P > 0.05). The ROC curve analysis showed that the area under the curve (AUC) of the long diameter of the lesion was 0.959 (P = 0.000), the optimal critical point of the ROC curve was the lesion ≥ 4.80 cm, the sensitivity was 88.1%, the specificity was 97.5%, and the accuracy was 91.6%; for the low-risk group, the AUC was 0.788 (the largest, P = 0.000), the sensitivity was 77.5%, the specificity was 70.1%, and the accuracy was 72.9%. Multivariate analysis showed that non-uniform density (P = 0.030; odds ratio [OR]: 12.544; 95% confidence interval [CI]: 1.269–123.969), arterial phase CT value (P = 0.024; OR: 10.790; 95% CI: 1.374–84.754), and lesion length (P = 0.000; OR: 648.694; 95% CI: 40.541–10,379.714) were risk factors for SBGISTs. Conclusions: The CT features of small intestinal stromal tumors have certain characteristics, which can help to grade the risk of small intestinal stromal tumors before surgery.