Coronary artery calcium score may be a novel predictor of COVID-19 prognosis: a retrospective study

Background

Although several studies have reported an association between atherosclerosis-related diseases and COVID-19, the relationship between COVID-19 severity and atherosclerosis progression remains unclear. The aim of this study is to determine the coronary artery calcium score (CACS) prognostic value in patients with COVID-19 using indices such as deterioration in oxygenation and CT images of the chest.

Methods

This was a single-centre retrospective study of 53 consecutive patients with COVID-19 in Narita who were admitted to our hospital between March 2020 and August 2020. CACS was calculated based on non-gated CT scans of the chest performed on admission day. The patients were divided into the following two groups based on CACS: group 1 (CACS ≥180, n=11) and group 2 (CACS <180, n=42). Following univariate analysis of the main variables, multivariate analysis of variables that may be associated with COVID-19 progression was performed.

Results

Multivariable logistic regression analysis of age, sex, smoking history, diabetes, hypertension, dyslipidaemia, number of days from symptom onset to hospitalisation and CACS of ≥180 was performed. It revealed that unlike CACS of <180, CACS of ≥180 is associated with exacerbation of oxygenation or CT images of the chest during hospitalisation (OR: 12.879, 95% CI: 1.399 to 380.401). Furthermore, this model of eight variables showed good calibration (Hosmer-Lemeshow p=0.119).

Conclusion

CACS may be a prognosis marker of COVID-19 severity. Although coronary artery calcification is not typically assessed in pneumonia cases, it may provide a valuable clinical indicator for predicting severe COVID-19 outcomes.

Clinical and CT features of ovarian torsion in infants, children and adolescents

OBJECTIVE

To investigate the clinical features and computed tomography (CT) findings of pediatric ovarian torsion.

METHODS

A retrospective analysis of the clinical and CT data of 61 newborns, infants, children, and adolescents with ovarian torsion confirmed by histopathology was performed.

RESULTS

Clinical features included abdominal mass, abdominal pain, nausea and vomiting, and fever. The tumor marker α-fetoprotein was increased in five cases. Ovarian enlargement was found in 26 cases, and follicles were detected in the peripheral region of the ovary in 21 cases. Twenty-one cases presented as solid mixed-density masses on CT images. A total of 30 cases of ovarian torsion were associated with a benign ovarian mass. Among 27 cases of cystic or predominantly cystic masses, the mass had a thickened wall in 26 cases and showed an uneven density in 23 cases. Among all 61 patients, a torsed pedicle was detected in 47 cases. A torsed ovary or mass exhibited mild contrast enhancement in seven cases. Uterine deviation toward the involved side, blurred fat space around lesions, and pelvic free fluid were also found.

CONCLUSION

Pediatric ovarian torsion presents a relatively characteristic CT appearance. Correct diagnosis can be established based on clinical and imaging features.

Voice, rhythm, and beep stimuli differently affect the right hemisphere preponderance and components of stimulus-preceding negativity

The present study investigated whether auditory stimuli with different contents affect right laterality and the components of stimulus-preceding negativity (SPN). A time-estimation task was performed under voice, rhythm, beep, and control conditions. The SPN interval during which participants anticipated the stimulus was divided into quarters to define early and late SPNs. Early and late components of SPN were also extracted using a principal component analysis. The anticipation of voice sounds enhanced the early SPN and the early component, which reflected the anticipation of language processing. Beep sounds elicited the right hemisphere preponderance of the early component, the early SPN, and the late SPN. The rhythmic sound tended to attenuate the amplitude compared with the two other stimuli. These findings further substantiate the existence of separate early and late components of the SPN. In addition, they suggest that the early component reflects selective anticipatory attention toward differing types of auditory feedback.

In vitro Demonstration of Melanoma Metastasis in Lymph Nodes of Prepared Specimens Using a Light-emitting Diode-based Multispectral Photoacoustic Ultrasound Imaging System

Although an excellent photoacoustic (PA) ultrasound method has been reported for the evaluation of lymph node melanoma metastasis in animal experiments, it remains to be evaluated in clinical trials. Recently, we performed PA ultrasound assessment using light-emitting diodes to detect metastatic melanoma in the lymph nodes of specimens prepared for microscopic examination. The PA effect was not obvious in amelanotic melanoma, but was seen in melanotic melanoma by PA imaging (PAI) and histopathological correlation in cases of primary melanotic melanoma accompanied by metastatic lymph nodes, including the coexistence of amelanotic melanoma and melanotic melanoma. Clinical workup should be performed with not only PAI but also conventional ultrasonography in cases with metastasis related to amelanotic transformation, which would likely be missed by PAI alone.

Application of CT texture analysis to assess the localization of primary aldosteronism

We performed present study to investigate whether the localization of primary aldosteronism (PA) can be predicted using quantitative texture analysis on unenhanced computed tomography (CT). Plain CT data of 82 PA patients (54 unilateral (right-sided:left-sided = 24:30), 28 bilateral) were analyzed retrospectively. After semi-automatically setting the region of interest to include the whole adrenal gland, texture analyses were performed with or without a Laplacian of Gaussian filter with various spatial scaling factors (SSFs). Logistic regression analysis was performed using the extracted histogram-based texture features to identify parameters capable of predicting excessive aldosterone production. The result of adrenal venous sampling served as gold standard in present study. As a result, logistic regression analysis indicated that the mean gray level intensity (p = 0.026), the mean value of the positive pixels (p = 0.003) in the unfiltered image, and entropy (p = 0.027) in the filtered image (SSF: 2 mm) were significant parameters. Using the model constructed by logistic regression analysis and the optimum cutoff value, the localization of PA (three multiple choices of left, right or bilateral) was determined with an accuracy of 67.1% (55/82). CT texture analysis may provide a potential avenue for less invasive prediction of the localization of PA.

Prediction of malignant glioma grades using contrast-enhanced T1-weighted and T2-weighted magnetic resonance images based on a radiomic analysis

We conducted a feasibility study to predict malignant glioma grades via radiomic analysis using contrast-enhanced T1-weighted magnetic resonance images (CE-T1WIs) and T2-weighted magnetic resonance images (T2WIs). We proposed a framework and applied it to CE-T1WIs and T2WIs (with tumor region data) acquired preoperatively from 157 patients with malignant glioma (grade III: 55, grade IV: 102) as the primary dataset and 67 patients with malignant glioma (grade III: 22, grade IV: 45) as the validation dataset. Radiomic features such as size/shape, intensity, histogram, and texture features were extracted from the tumor regions on the CE-T1WIs and T2WIs. The Wilcoxon-Mann-Whitney (WMW) test and least absolute shrinkage and selection operator logistic regression (LASSO-LR) were employed to select the radiomic features. Various machine learning (ML) algorithms were used to construct prediction models for the malignant glioma grades using the selected radiomic features. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the prediction models in the primary dataset. The selected radiomic features for all folds in the LOOCV of the primary dataset were used to perform an independent validation. As evaluation indices, accuracies, sensitivities, specificities, and values for the area under receiver operating characteristic curve (or simply the area under the curve (AUC)) for all prediction models were calculated. The mean AUC value for all prediction models constructed by the ML algorithms in the LOOCV of the primary dataset was 0.902 ± 0.024 (95% CI (confidence interval), 0.873-0.932). In the independent validation, the mean AUC value for all prediction models was 0.747 ± 0.034 (95% CI, 0.705-0.790). The results of this study suggest that the malignant glioma grades could be sufficiently and easily predicted by preparing the CE-T1WIs, T2WIs, and tumor delineations for each patient. Our proposed framework may be an effective tool for preoperatively grading malignant gliomas.

Factors associated with the size of the adhesio interthalamica based on 3.0-T magnetic resonance images

BACKGROUND

Adhesio interthalamica (AI) is a small structure connecting bilateral thalami.

PURPOSE

To evaluate the effects of patient age, sex, and lateral diameter of the third ventricle on the long diameter of the AI using multivariate analyses based on magnetic resonance (MR) images obtained with 3.0-T scanners.

MATERIAL AND METHODS

This clinical retrospective study included images of 153 patients who underwent MR examination using 3.0-T scanners. The long diameter of the AI and lateral diameter of the third ventricle were measured on images in the mid-sagittal plane and axial plane at the anterior commissure, respectively. Univariate and multivariate analyses were performed.

RESULTS

AI was observed in 138 patients (70 men, 68 women; mean age = 63.7 ± 13.7 years; mean AI size =5.34 ± 1.63 mm). By univariate analyses, patient age (r = -0.262, P = 0.002), sex ( P = 0.010), and lateral diameter of the third ventricle (r = -0.642, P < 0.001) were significantly associated with the long diameter of the AI. With multiple linear regression analyses with a stepwise selection of parameters, only the lateral diameter of the third ventricle (estimate = -0.432, P < 0.001) was significantly associated with the long diameter of the AI. The lateral diameter of the third ventricle was longer in patients without AI (15 patients) than in those with AI ( P = 0.006).

CONCLUSION

The lateral diameter of the third ventricle was a major factor negatively associated with the long diameter of the AI.

Deep learning for staging liver fibrosis on CT: a pilot study

OBJECTIVES

To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

METHODS

This clinical retrospective study, approved by our institutional review board, included 496 CT examinations of 286 patients who underwent dynamic contrast-enhanced CT for evaluations of the liver and for whom histopathological information regarding liver fibrosis stage was available. The 396 portal phase images with age and sex data of patients (F0/F1/F2/F3/F4 = 113/36/56/66/125) were used for training a deep convolutional neural network (DCNN); the data for the other 100 (F0/F1/F2/F3/F4 = 29/9/14/16/32) were utilised for testing the trained network, with the histopathological fibrosis stage used as reference. To improve robustness, additional images for training data were generated by rotating or parallel shifting the images, or adding Gaussian noise. Supervised training was used to minimise the difference between the liver fibrosis stage and the fibrosis score obtained from deep learning based on CT images (F_DLCT score) output by the model. Testing data were input into the trained DCNNs to evaluate their performance.

RESULTS

The F_DLCT scores showed a significant correlation with liver fibrosis stage (Spearman's correlation coefficient = 0.48, p < 0.001). The areas under the receiver operating characteristic curves (with 95% confidence intervals) for diagnosing significant fibrosis (≥ F2), advanced fibrosis (≥ F3) and cirrhosis (F4) by using F_DLCT scores were 0.74 (0.64-0.85), 0.76 (0.66-0.85) and 0.73 (0.62-0.84), respectively.

CONCLUSIONS

Liver fibrosis can be staged by using a deep learning model based on CT images, with moderate performance.

KEY POINTS

• Liver fibrosis can be staged by a deep learning model based on magnified CT images including the liver surface, with moderate performance. • Scores from a trained deep learning model showed moderate correlation with histopathological liver fibrosis staging. • Further improvement are necessary before utilisation in clinical settings.

Deep learning with convolutional neural network in radiology

Deep learning with a convolutional neural network (CNN) is gaining attention recently for its high performance in image recognition. Images themselves can be utilized in a learning process with this technique, and feature extraction in advance of the learning process is not required. Important features can be automatically learned. Thanks to the development of hardware and software in addition to techniques regarding deep learning, application of this technique to radiological images for predicting clinically useful information, such as the detection and the evaluation of lesions, etc., are beginning to be investigated. This article illustrates basic technical knowledge regarding deep learning with CNNs along the actual course (collecting data, implementing CNNs, and training and testing phases). Pitfalls regarding this technique and how to manage them are also illustrated. We also described some advanced topics of deep learning, results of recent clinical studies, and the future directions of clinical application of deep learning techniques.

Quantitative computed tomography texture analyses for anterior mediastinal masses: Differentiation between solid masses and cysts

OBJECTIVES

To investigate whether solid anterior mediastinal masses could be differentiated from cysts using quantitative computed tomography (CT) texture analyses in unenhanced CT (UECT) or contrast enhanced CT (CECT).

MATERIALS AND METHODS

This clinical retrospective study included 76 UECT images (40 men and 36 women, 28 cystic (mean diameter, 29.5 mm) and 48 solid (mean diameter, 48.2 mm)) and 84 CECT images (45 men and 39 women, 26 cystic (mean diameter, 31.4 mm) and 58 solid (mean diameter, 51.4 mm)) of anterior mediastinal masses, which were diagnosed histopathologically or using imaging criteria. Polygonal regions of interest were placed on these masses. CT histogram analyses for images of masses with or without filtration (Laplacian of Gaussian filters with various spatial scaling factors) were performed. DeLong's test was performed to compare areas under the curve (AUC) with receiver operating characteristic analyses.

RESULTS

From logistic regression analyses with a stepwise procedure, a combination of the mean in unfiltered images (mean0; i.e., CT attenuation) and mean in filtered images featuring coarse texture for UECT (AUC = 0.869) and the combination of mean0 and entropy in filtered images featuring fine texture for CECT (AUC = 0.997) were found to predict better the internal characteristics of anterior mediastinal masses. In UECT and CECT, diagnostic performance using these combinations tended to be high compared to mean0 alone (AUC = 0.780 [p = 0.033] and AUC = 0.983 [p = 0.130], respectively).

CONCLUSION

Solid anterior mediastinal masses can be differentiated from cysts using quantitative CT texture analyses with moderate and high diagnostic performance in UECT and CECT, respectively.

Liver Fibrosis: Deep Convolutional Neural Network for Staging by Using Gadoxetic Acid-enhanced Hepatobiliary Phase MR Images

Purpose To investigate the performance of a deep convolutional neural network (DCNN) model in the staging of liver fibrosis using gadoxetic acid-enhanced hepatobiliary phase magnetic resonance (MR) imaging. Materials and Methods This retrospective study included patients for whom input data (hepatobiliary phase MR images, static magnetic field of the imaging unit, and hepatitis B and C virus testing results available, either positive or negative) and reference standard data (liver fibrosis stage evaluated from biopsy or surgical specimens obtained within 6 months of the MR examinations) were available were assigned to the training (534 patients) or the test (100 patients) group. For the training group (54, 53, 81, 113, and 233 patients with fibrosis stages F0, F1, F2, F3, and F4, respectively; mean patient age, 67.4 ± 9.7 years; 388 men and 146 women), MR images with three different section levels were augmented 90-fold (rotated, parallel-shifted, brightness-changed and contrast-changed images were generated; a total of 144 180 images). Supervised training was performed by using the DCNN model to minimize the difference between the output data (fibrosis score obtained through deep learning [F_DL score]) and liver fibrosis stage. The performance of the DCNN model was evaluated in the test group (10, 10, 15, 20, and 45 patients with fibrosis stages F0, F1, F2, F3, and F4, respectively; mean patient age, 66.8 years ± 10.7; 71 male patients and 29 female patients) with receiver operating characteristic (ROC) analyses. Results The F_DL score was correlated significantly with fibrosis stage (Spearman rank correlation coefficient: 0.63; P < .001). Fibrosis stages F4, F3, and F2 were diagnosed with areas under the ROC curve of 0.84, 0.84, and 0.85, respectively. Conclusion The DCNN model exhibited a high diagnostic performance in the staging of liver fibrosis. ^© RSNA, 2017 Online supplemental material is available for this article.

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. ^© RSNA, 2017 Online supplemental material is available for this article.

Impact of hepatocellular carcinoma heterogeneity on computed tomography as a prognostic indicator

We assessed the relationship between the heterogeneity of HCC on preoperative non-contrast-enhanced CT and patient prognosis. The heterogeneity of CT images from 122 patients was assessed and texture feature parameters such as mean, standard deviation (SD), entropy, mean of the positive pixels (MPP), skewness, and kurtosis were obtained using filtration. The relationship between CT texture features and 5-year overall survival (OS) or disease-free survival (DFS) was assessed. Multivariate regression analysis was performed to evaluate the independence of texture feature from clinical or pathological parameters. The Kaplan-Meier curves for OS or DFS was significantly different between patient groups dichotomized by cut-off values for all CT texture parameters with filtration at at least one filter level. Multivariate regression analysis showed the independence of most CT texture parameters on clinical and pathological parameters for OS with filtration at at least one filter level and without filtration except kurtosis. SD, entropy, and MPP with coarse filter, and skewness without filtration showed a significant correlation for DFS. CT texture features of non-contrast-enhanced CT images showed a relationship with HCC prognosis. Multivariate regression analysis showed the possibility of CT texture feature increase the prognostic prediction of HCC by clinical and pathological information.

Gadoxetate disodium-induced tachypnoea and the effect of dilution method: a proof-of-concept study in mice

OBJECTIVES

To directly investigate the rapid respiratory effect of gadoxetate disodium in an experimental study using mice.

METHODS

After confirming the steady respiratory state under general anaesthesia, eight mice were injected with all test agents in the following order: phosphate-buffered saline (A, control group), 1.25 mmol/kg of gadoteridol (B) or gadopentetate dimeglumine (C), or 0.31 mmol/kg of gadoxetate disodium (D, E). The experimenter was not blinded to the agents. The injection dose was fixed as 100 μL for Groups A-D and 50 μL for Group E. We continuously monitored and recorded respiratory rate (RR), peripheral oxygen saturation (SpO₂), and heart rate. The time-series changes from 0 to 30 s were compared by the linear mixed method RESULTS: Groups D and E showed the largest RR increase (20.6 and 20.3 breaths/min, respectively) and were significantly larger compared to Group A (3.36 breaths/min, both P<0.001). RR change of Groups D and E did not differ. RR change of Groups B and C was smaller (0.72 and 12.4 breaths/min, respectively) and did not differ statistically with Group A. Significant bradycardia was observed only in Group C (P<0.001). SpO₂ was constant in all groups.

CONCLUSIONS

Gadoxetate disodium causes a rapid tachypnoea without significant change of SpO₂ and heart rate regardless of the dilution method.

KEY POINTS

• Injection of gadoxetate disodium causes tachypnoea. • Dilution method did not alter the rapid respiratory effect of gadoxetate disodium. • The respiratory effect of gadoxetate disodium was larger than other contrast agents.

A pulmonary metastatic model of murine melanoma assessed by magnetic resonance imaging

Immune checkpoint inhibitors and kinase inhibitors have improved prognosis of malignant melanoma (MM) patients. However, these therapies cannot completely overcome the metastasis of MM. Thus, development of new therapy against metastasis should be required. A first step towards this goal, the aim of this study, is to establish a model of pulmonary metastasis from primary cutaneous MM and a monitoring system. B16-F10, a murine melanoma cell line, was subcutaneously injected into the pinna of mice. The pinna was excised when the lesion was detected. A metastatic nodule on T2-weighted imaging was detected 4 weeks after resection of the pinna. Lung metastases were observed in 37.5% (6/16) of the specimens. We established a novel murine model of the high pulmonary metastasis of MM. The MRI was useful for observations of the growth of the metastatic lesions in the lungs without dissection.

Precision of quantitative computed tomography texture analysis using image filtering: A phantom study for scanner variability

Quantitative computed tomography (CT) texture analyses for images with and without filtration are gaining attention to capture the heterogeneity of tumors. The aim of this study was to investigate how quantitative texture parameters using image filtering vary among different computed tomography (CT) scanners using a phantom developed for radiomics studies.A phantom, consisting of 10 different cartridges with various textures, was scanned under 6 different scanning protocols using four CT scanners from four different vendors. CT texture analyses were performed for both unfiltered images and filtered images (using a Laplacian of Gaussian spatial band-pass filter) featuring fine, medium, and coarse textures. Forty-five regions of interest were placed for each cartridge (x) in a specific scan image set (y), and the average of the texture values (T(x,y)) was calculated. The interquartile range (IQR) of T(x,y) among the 6 scans was calculated for a specific cartridge (IQR(x)), while the IQR of T(x,y) among the 10 cartridges was calculated for a specific scan (IQR(y)), and the median IQR(y) was then calculated for the 6 scans (as the control IQR, IQRc). The median of their quotient (IQR(x)/IQRc) among the 10 cartridges was defined as the variability index (VI).The VI was relatively small for the mean in unfiltered images (0.011) and for standard deviation (0.020-0.044) and entropy (0.040-0.044) in filtered images. Skewness and kurtosis in filtered images featuring medium and coarse textures were relatively variable across different CT scanners, with VIs of 0.638-0.692 and 0.430-0.437, respectively.Various quantitative CT texture parameters are robust and variable among different scanners, and the behavior of these parameters should be taken into consideration.

Resectable primary pleural myxoid liposarcoma with a pedicle: report of a rare case and literature review

Primary pleural myxoid liposarcoma is a rare tumor. Here, we report a primary myxoid liposarcoma occupying the majority of the left thoracic cavity with features suggesting invasion. Computed tomography (CT) at medical check-up incidentally revealed a bulky inhomogeneous fatty mass. The tumor's large size made a prediction of its resectability by preoperative CT difficult. The patient underwent an operation, which revealed that the tumor was attached to the pleura with a thin pedicle; the tumor was resected completely. Few therapies for pleural liposarcoma other than resection are available; hence, surgery should be considered even if the tumor's size implies invasion on radiological imaging. In this case report, we discuss the imaging findings of this case with a review of the related literature.

Quantitative computed tomography texture analysis for estimating histological subtypes of thymic epithelial tumors

OBJECTIVES

To investigate whether high-risk thymic epithelial tumor (TET) (HTET) can be differentiated from low-risk TET (LTET) using computed tomography (CT) quantitative texture analysis.

MATERIALS AND METHODS

The data of 39 patients (mean age, 58.6±14.1 years) (39 unenhanced CT (UECT) and 33 contrast-enhanced CT (CECT)) who underwent thymectomy for TET were retrospectively analyzed. A region of interest was placed to include the entire TET within the slice at its maximum diameter. Texture analysis was performed for images with or without a Laplacian of Gaussian filter (with various spatial scaling factors [SSFs]). Two radiologists evaluated the visual heterogeneity of TET using a 3-point scale.

RESULTS

The mean in the unfiltered image (mean0u) and entropy in the filtered image (SSF: 6mm) (entropy6u) for UECT, and the mean in the unfiltered image (mean0c) for CECT were significant parameters for differentiating between HTET and LTET as determined by logistic regression analysis. The area under the receiver operating characteristics curve (AUC) for differentiating HTET from LTET using mean0u, entropy6u, and mean0c was 0.75, 0.76, and 0.89, respectively. And the combination of mean0u and entropy6u allowed AUC of 0.87. Entropy6u provided a higher diagnostic performance compared with visual heterogeneity analysis (p≤0.018).

CONCLUSION

Using CT quantitative texture analysis, HTET can be differentiated from LTET with a high diagnostic performance.

Computed tomography and magnetic resonance imaging of a plexiform angiomyxoid myofibroblastic tumor: a case report

Background

Plexiform angiomyxoid myofibroblastic tumor (PAMT) is a very rare mesenchymal tumor of the stomach. Here we report a case of pathologically confirmed PAMT with an unique cyst formation.

Case presentation

A 55-year-old male with a 10-year history of a gastric subepithelial tumor underwent computed tomography (CT) and magnetic resonance imaging (MRI). Two cysts were observed in the tumor, and the cyst wall showed moderately high intensity on T2-weighted images compared with the gastric wall. On dynamic study, the cyst wall showed a gradual enhancement pattern, and prominent enhancement was observed in the delayed phase. Laparoscopic partial gastric resection was performed, and a pathological diagnosis of PAMT was rendered.

Conclusion

We present a rare case of gastric PAMT, which was uniquely presented as cysts. One of the cysts in the tumor had an epithelial wall lining, which had never been reported before in gastric mesenchymal tumor, in addition to partial glandular structure. We reviewed our case, focusing on radiologic-pathologic correlation, and suggested hypothesis of cyst formation. According to our findings, PAMT with cyst formation would be included of differential diagnosis of gastric subepithelial tumors.

The natural history of streptozotocin-stimulated non-alcoholic steatohepatitis mice followed by Gd-EOB-DTPA-enhanced MRI: Comparison with simple steatosis mice

PURPOSE

To clarify the development of HCC, temporal change of steatosis and Gd-EOB-DTPA enhancement of non-alcoholic steatohepatitis (NASH) model mice by magnetic resonance imaging (MRI).

MATERIALS AND METHODS

All animal experiments were approved by the institution's Animal Research Committee. MRI was performed on six NASH and six simple steatosis (SS) model mice every 2weeks from the ages of 8weeks to 16weeks. The sequential changes in the number and size of the focal liver lesions detected on Gd-EOB-DTPA-enhanced MRI were evaluated. Additionally, the hepatic fat fraction (HFF), contrast-to-noise ratio (CNR) and relative enhancement (RE) were calculated at each time point. The temporal changes and correlations in these parameters were evaluated.

RESULTS

All alive NASH model mice demonstrated focal liver lesions from week 10, at the latest. Number of the lesions increased with time, and all the lesion enlarged with time. All the lesions larger than 1mm were confirmed as hepatocellular carcinoma (HCC) pathologically. While the HFF remained constant in NASH model mice, HFF in SS model mice dramatically increased with time. CNR of the NASH model mice remained constant through the study period, while CNR in SS model mice decreased with time. Although no correlation was seen in NASH model mice, the HFF showed a negative correlation against CNR and RE in SS model mice.

CONCLUSION

Development of HCC was observed using Gd-EOB-DTPA-enhanced MRI only in NASH model mice. Degree of steatosis and hepatic enhancement by Gd-EOB-DTPA was both constant in NASH model mice, while steatosis increased and hepatic enhancement decreased with time in SS model mice.

The effects of bolus supplementation of branched-chain amino acids on skeletal muscle mass, strength, and function in patients with rheumatic disorders during glucocorticoid treatment

OBJECTIVES

To test the effects of bolus supplementation of branched-chain amino acids (BCAA) on skeletal muscle mass, strength, and function in patients with rheumatic disorders taking glucocorticoid (GC).

METHODS

Patients with rheumatic disorders treated with prednisolone (≥10 mg/day) were randomized to ingest additional daily 12 g of BCAA (n = 9) or not (n = 9) for 12 weeks. At baseline, and 4, 8, and 12 weeks, they underwent bioelectrical impedance analysis, muscle strength and functional tests, and computed tomography analysis for cross-sectional area of mid-thigh muscle.

RESULTS

Disease activities of the patients were well controlled and daily GC dose was similarly reduced in both groups. Limb muscle mass was recovered in both groups. Whole-body muscle mass and muscle strength and functional mobility were increased only in BCAA (+) group. The effects of BCAA supplementation on recovering skeletal muscle mass were prominent in particular muscles including biceps femoris muscle.

CONCLUSIONS

This trial is the first-in-man clinical trial to demonstrate that BCAA supplementation might be safe and, at least in part, improve skeletal muscle mass, strength, and function in patients with rheumatic disorders treated with GC.