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To develop a radiomics-based hepatocellular carcinoma (HCC) grade classifier model based on data from gadoxetic acid-enhanced MRI.This retrospective study included 137 patients who underwent hepatectomy for a single HCC and gadoxetic acid-enhanced MRI within 60 days before surgery. HCC grade was categorized as low or high (modified Edmondson-Steiner grade I-II vs. III-IV). We used the hepatobiliary phase (HBP), portal venous phase, T2-weighted image(T2WI), and T1-weighted image(T1WI). From the volume of interest in HCC, 833 radiomic features were extracted. Radiomic and clinical features were selected using a random forest regressor, and the classification model was trained and validated using a random forest classifier and tenfold stratified cross-validation. Eight models were developed using the radiomic features alone or by combining the radiomic and clinical features. Models were validated with internal enrolled data (internal validation) and a dataset (28 patients) at a separate institution (external validation). The area under the curve (AUC) of the validation results was compared using the DeLong test.In internal and external validation, the HBP radiomics-only model showed the highest AUC (internal 0.80 ± 0.09, external 0.70 ± 0.09). In external validation, all models showed lower AUC than those for internal validation, while the T2WI and T1WI models failed to predict the HCC grade (AUC 0.30-0.58) in contrast to the internal validation results (AUC 0.67-0.78).The radiomics-based machine learning model from gadoxetic acid-enhanced liver MRI could distinguish between low- and high-grade HCCs. The radiomics-only HBP model showed the best AUC among the eight models, good performance in internal validation, and fair performance in external validation.

The aim of the study was to investigate the diagnostic feasibility of radiomics analysis using magnetic resonance elastography (MRE) to assess hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD).One hundred patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. Magnetic resonance elastography was performed using a 3.0-T scanner. After multislice segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of the regions of interest and variable clinical features, were analyzed. A random forest regressor was used to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve was evaluated using a classifier for fibrosis stage diagnosis.The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0-F1, n = 82) or clinically significant fibrosis (stages F2-F4, n = 18). Eight important features were extracted from radiomics analysis, with the 2 most important being wavelet-high high low gray level dependence matrix dependence nonuniformity-normalized and wavelet-high high low gray level dependence matrix dependence entropy. The median value of the multiple small regions of interest was identified as the most important radiologic feature. Platelet count has been identified as an important clinical feature. The area under the receiver operating characteristic curve of the classifier using radiomics was comparable with that of radiologic measures (0.97 ± 0.07 and 0.96 ± 0.06, respectively).Magnetic resonance elastography radiomics analysis provides diagnostic performance comparable with conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.

Background: Inflammatory myofibroblastic tumors (IMTs) of the gastrointestinal (GI) tract are rare phenomena, and the computed tomography (CT) findings of GI IMTs are not well-established. Objectives: To describe the characteristics of CT scans, pathological specimens, and histological subtypes of GI IMTs in adults. Patients and Methods: The multidetector computed tomography (MDCT) scans of 11 adult patients (8 males, 3 females; age range, 19 - 76 years) with pathologically proven GI tract IMTs (stomach, small bowel, and colon) were retrospectively evaluated by two abdominal radiologists. The radiological features of IMTs were investigated. The imaging features were correlated with three microscopic IMT subtypes (myxoid vascular, spindle cell, and hypocellular fibrous). Immunohistochemistry was also performed on the specimens, including smooth muscle actin (SMA), vimentin, desmin, S-100, and anaplastic lymphoma kinase. Results: The tumor size ranged from 1.4 to 15 cm (mean, 5.7 cm). Two growth patterns were classified, namely, wall-thickening (n = 3) and solitary mass-forming (n = 8) patterns; each pattern was matched with a characteristic pathological subtype. All solitary, well-circumscribed masses corresponded to the spindle cell type. Low-attenuation wall thickening with perienteric infiltration was observed in three patients with a wall-thickening pattern. All solitary, well-circumscribed masses (n = 8) showed homogeneous enhancement with variable internal low attenuation, correlated with cystic degeneration, necrosis, myxoid change (n = 6), and hemorrhagic necrosis (n = 2). No patient showed bowel obstruction, while one patient showed regional lymphadenopathy. Immunophenotypes were not correlated with any growth pattern or histological subtype. Conclusion: The GI IMTs can be classified into two patterns, including wall-thickening and well-circumscribed masses, each matched with a characteristic pathological subtype, which can help explain the tumor behavior. Concomitant CT findings may also provide diagnostic clues for IMT.

This study aimed to assess the diagnostic feasibility of radiomics analysis based on magnetic resonance (MR)-proton density fat fraction (PDFF) for grading hepatic steatosis in patients with suspected non-alcoholic fatty liver disease (NAFLD).This retrospective study included 106 patients with suspected NAFLD who underwent a hepatic parenchymal biopsy. MR-PDFF and MR spectroscopy were performed on all patients using a 3.0-T scanner. Following whole-volume segmentation of the MR-PDFF images, 833 radiomic features were analyzed using a commercial program. Radiologic features were analyzed, including median and mean values of the multiple regions of interest and variable clinical features. A random forest regressor was used to extract the important radiomic, radiologic, and clinical features. The model was trained using 20 repeated 10-fold cross-validations to classify the NAFLD steatosis grade. The area under the receiver operating characteristic curve (AUROC) was evaluated using a classifier to diagnose steatosis grades.The levels of pathological hepatic steatosis were classified as low-grade steatosis (grade, 0-1; n = 82) and high-grade steatosis (grade, 2-3; n = 24). Fifteen important features were extracted from the radiomic analysis, with the three most important being wavelet-LLL neighboring gray tone difference matrix coarseness, original first-order mean, and 90th percentile. The MR spectroscopy mean value was extracted as a more important feature than the MR-PDFF mean or median in radiologic measures. Alanine aminotransferase has been identified as the most important clinical feature. The AUROC of the classifier using radiomics was comparable to that of radiologic measures (0.94 ± 0.09 and 0.96 ± 0.08, respectively).MR-PDFF-derived radiomics may provide a comparable alternative for grading hepatic steatosis in patients with suspected NAFLD.

Background: In conventional ultrasonography, the ultrasound image is not labeled with information on the location or orientation of the transducer. There is also no information on the parts of the body under examination. Objectives: This study aimed to investigate the efficacy of body navigation-loaded ultrasonography, including the real-time transducer location and the inspection site, compared to conventional ultrasound. Patients and Methods: After obtaining approval from the institutional review board, 29 healthy adult volunteers were prospectively enrolled in this study. One gastrointestinal radiologist performed an abdominal ultrasound, using an ultrasound navigation image convergence system, developed by the authors. Subsequently, an equivalent conventional ultrasound image set was prepared. Three radiologists independently evaluated the two ultrasound image sets for identifying the target organ (two points), transducer location (two points), and transducer orientation (one point). In a two-week interval, conventional ultrasound images were first analyzed, and then, body navigation-loaded images were examined. The score differences between the first and second evaluations were compared using Wilcoxon signed-rank test. The inter-rater agreement of the three reviewers was determined by measuring the Fleiss’ kappa value. Results: A total of 1,402 navigation-loaded ultrasound images were acquired in this study. The ultrasound operator carefully selected 203 images for analysis. The interpretation score of all three reviewers significantly increased for each examination in the second analysis using the body navigation-loaded ultrasound images (reviewer A, from 4.07 ± 1.56 to 4.79 ± 0.69; reviewer B, from 3.83 ± 1.59 to 4.49 ± 0.88; and reviewer C, from 3.43 ± 1.60 to 4.19 ± 1.01) (P < 0.0001). The inter-rater agreement of each examination also increased significantly in the second analysis using the body navigation-loaded ultrasound images (P < 0.0001). Conclusion: According to the results of this pilot study, the body navigation-loaded ultrasound technology can assist with a simple and objective interpretation of ultrasound images from organs.

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID‐19) disease using normal, pneumonia, and COVID‐19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID‐19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID‐19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID‐19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient‐weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed‐COVID‐19Net using three CNNs over five folds were (0.99 ± 0.01 and 92.94% ± 0.45%), (0.99 ± 0.01 and 93.12% ± 0.23%), and (0.99 ± 0.01 and 93.57% ± 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross‐validation with the KUAH dataset (external) using domain adaptation. The various state‐of‐the‐art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID‐19 as well as other diseases.

This paper discusses the effectiveness of onboard measurements and data extracted from weather information for speed-power analysis. Furthermore, validation results of hull and propeller cleaning and painting during dry-docking are discussed. Wind and wave information can be obtained from onboard measurements or weather information from the National Oceanic and Atmospheric Administration (NOAA). The weather information of a specified position and time is extracted from NOAA weather data and compared with onboard measurements. In addition, to validate the effects of hull cleaning and painting during dry-docking, speed-power analysis results of before and after dry-docking are compared. The results show that both onboard measurements and weather information show acceptable reliability when added resistance and speed-power analysis results are compared with each other. Moreover, the ship performance analysis (SPA) software clearly shows the effects of hull cleaning and painting, and it can provide reliable analysis results with either onboard measurements or weather information. In conclusion, it is confirmed that the analysis method and SPA software used in this study are effective in analyzing the ship’s speed-power performance.

Background: Understanding the axial lumbar spine anatomy, including knowledge of the relationship between the lumbar spine level and other paraspinal structures, is important in diagnosing and treating disease. The purpose of this study was to validate the accuracy of a convolutional neural network (CNN) model in lumbar spine level numbering on axial MR images and to find the appropriate anatomic landmarks for numbering using a class activation map (CAM).Methods: A total of 6055 axial MR images of the lumbar spine from L1–2 to L5–S1 disc levels were obtained to train and validate the CNN model. The MR images were acquired using three 3-Tesla machines. The algorithm was developed with three models, and the best-performing model was selected. The external validation set (n = 493) was obtained from other institutions using various machines. The accuracy of the numbering was analyzed using a confusion matrix and receiver operating characteristic curves. The CAMs were reviewed, and the identified anatomic structures were investigated. A reader study was performed by three radiologists, and their accuracy was compared with that of the model.Results: The overall accuracy of the best-performing model for lumbar spine numbering was 0.98 on internal validation and 0.95 on external validation. For the CAM review, mappings concentrated on both paraspinal areas, including the kidney, back muscles, and ilium according to the level. Top-1 and top-2 accuracies of the reviewers ranged between 0.56–0.75, and 0.84–0.93, respectively. After reviewing the CAMs, the accuracy increased to 0.75–0.78 and 0.93–0.98, respectively.Conclusion: A CNN model can accurately determine the level of the lumbar spine on axial MR images, and the configuration of muscles can be used to determine the lumbar level.

Background Hepatobiliary phase (HBP) hypointense nodules without arterial phase hyperenhancement (APHE) at gadoxetic acid-enhanced MRI may indicate hepatocellular carcinoma (HCC) or nonmalignant cirrhosis-associated nodules. Purpose To assess the distribution of pathologic diagnoses of HBP hypointense nodules without APHE at gadoxetic acid-enhanced MRI and to evaluate clinical and imaging features in differentiating their histologic grades. Materials and Methods This retrospective multicenter study included pathologic analysis-confirmed HBP hypointense nodules without APHE (≤30 mm) in patients with chronic liver disease or cirrhosis screened between January 2008 and June 2016. Central pathologic review by 10 pathologists determined final histologic grades as progressed HCC, early HCC, high-grade dysplastic nodule (DN), and low-grade DN or regenerative nodule. Gadoxetic acid-enhanced MRI features were analyzed by three radiologists. Multivariable logistic regression analyses with elastic net regularization were performed to identify clinical and imaging features for differentiating histologic grades. Results There were 298 patients (mean age, 59 years ± 10; 226 men) with 334 nodules evaluated, and progressed HCCs were diagnosed in 44.0% (147 of 334), early HCCs in 20.4% (68 of 334), high-grade DNs in 27.5% (92 of 334), and low-grade DNs or regenerative nodules in 8.1% (27 of 334). Serum α-fetoprotein level 100 ng/mL or greater (odds ratio, 2.7; P = .01) and MRI features including well-defined margin (odds ratio, 5.5; P = .003), hypointensity at precontrast T1-weighted imaging (odds ratio, 3.2; P < .001), intermediate hyperintensity at T2-weighted imaging (odds ratio, 3.4; P < .001), and restricted diffusion (odds ratio, 1.9; P = .04) were independent predictors for progressed HCC at multivariable analysis. Conclusion In patients at high risk for hepatocellular carcinoma (HCC), hepatobiliary phase hypointense nodules without arterial phase hyperenhancement at gadoxetic acid-enhanced MRI corresponded mainly to progressed HCCs, early HCCs, and high-grade dysplastic nodules. High α-fetoprotein level and some imaging features at MRI helped to differentiate progressed HCC from lower grade nodules. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Motosugi in this issue.

This study aimed to propose an efficient method for self-automated segmentation of the liver using magnetic resonance imaging-derived proton density fat fraction (MRI-PDFF) through deep active learning. We developed an active learning framework for liver segmentation using labeled and unlabeled data in MRI-PDFF. A total of 77 liver samples on MRI-PDFF were obtained from patients with nonalcoholic fatty liver disease. For the training, tuning, and testing of the liver segmentation, the ground truth of 71 (internal) and 6 (external) MRI-PDFF scans for training and testing were verified by an expert reviewer. For 100 randomly selected slices, manual and deep learning (DL) segmentations for visual assessments were classified, ranging from very accurate to mostly accurate. The dice similarity coefficients for each step were 0.69 ± 0.21, 0.85 ± 0.12, and 0.94 ± 0.01, respectively (p-value = 0.1389 between the first step and the second step or p-value = 0.0144 between the first step and the third step for paired t-test), indicating that active learning provides superior performance compared with non-active learning. The biases in the Bland-Altman plots for each step were - 24.22% (from - 82.76 to - 2.70), - 21.29% (from - 59.52 to 3.06), and - 0.67% (from - 10.43 to 4.06). Additionally, there was a fivefold reduction in the required annotation time after the application of active learning (2 min with, and 13 min without, active learning in the first step). The number of very accurate slices for DL (46 slices) was greater than that for manual segmentations (6 slices). Deep active learning enables efficient learning for liver segmentation on a limited MRI-PDFF.

Background: To investigate the diagnostic performance of radiomics analysis using magnetic resonance elastography (MRE) toward assessing hepatic fibrosis in patients with nonalcoholic fatty liver disease (NAFLD). Methods: A total of 100 patients with suspected NAFLD were retrospectively enrolled. All patients underwent a liver parenchymal biopsy. MRE was performed using a 3.0-T scanner. Following three-dimensional (3D) segmentation of MRE images, 834 radiomic features were analyzed using a commercial program. Radiologic features, such as median and mean values of two-dimensional (2D) or 3D regions of interest (ROIs) and variable clinical features, were analyzed. A random forest regressor was employed to extract important radiomic, radiological, and clinical features. A random forest classifier model was trained to use these features to classify the fibrosis stage. The area under the receiver operating characteristic curve (AUC) was evaluated using a classifier for fibrosis stage diagnosis. Results: The pathological hepatic fibrosis stage was classified as low-grade fibrosis (stages F0–F1, n = 82) or clinically significant fibrosis (stages F2–F4, n = 18). Eight important features were extracted from radiomics analysis, with the two most important being wavelet-HHL gray level dependence matrix (GLDM)-dependence non-uniformity-normalized and wavelet-HHL GLDM-dependence entropy. The median value of the 2D ROI was identified as the most important radiologic feature. Platelet count was identified as an important clinical feature. The AUC of the classifier using radiomics was comparable to that of radiologic measures (0.97 ± 0.07 vs. 0.96 ± 0.06). Conclusions: MRE radiomics analysis provides diagnostic performance comparable to conventional MRE analysis for the assessment of clinically significant hepatic fibrosis in patients with NAFLD.

Background: To investigate the usefulness of body navigation-loaded ultrasound including a real time transducer location and the inspection site compared with conventional ultrasound images.Methods: Under the approval of institutional review board, we prospectively enrolled total 29 healthy adult volunteers. One gastrointestinal radiologist performed abdominal ultrasound simultaneously using Ultrasound Navigation Image Convergence System developed by researchers. Subsequently, an equivalent conventional ultrasound image set was prepared. Three radiologists independently evaluated the two ultrasound image sets regarding the recognition of the target organ (2-points), the transducer location (2-points), and the transducer orientation (1-point). At intervals of two-weeks, conventional ultrasound images were analyzed first, and body navigation-loaded images were later analyzed. The score differences between the first and second evaluations were compared using the Wilcoxon signed rank test. Inter-rater agreement of three reviewers was obtained by the Fleiss’ Kappa test.Results: A total of 1402 navigation-loaded ultrasound images were obtained. Ultrasound operator carefully selected a total of 203 images for analysis. In all three reviewers, the interpretation score of each evaluation was significantly increased in the second analysis using the body navigation-loaded ultrasound image (in reviewer A, from 4.07±1.56 to 4.79±0.69 points; in reviewer B, from 3.83±1.59 to 4.49±0.88 points; in reviewer C, from 3.43±1.60 to 4.19±1.01 points; PConclusion: The body navigation-loaded ultrasound imaging system allows other medical staffs to easily and accurately interpret ultrasound images.

To compare the effects of gadoxetic acid and gadoteric acid on the image quality of single-breath-hold, triple (first, second, and third) arterial hepatic magnetic resonance imaging (MRI). Two hundred and eleven patients were divided into two groups according to the contrast materials used (gadoxetic acid, 108 patients and gadoteric acid, 103 patients). All 3.0-T MR examinations included triple arterial phase acquisition using the 4D enhanced T1-weighted high-resolution isotropic volume examination (eTHRIVE) keyhole technique. The image qualities of the pre-contrast and triple arterial phases were assessed in terms of image artifacts, sharpness of the intrahepatic vessel and liver edge, and overall image quality with a 5-point scale for qualitative analysis. The image quality of gadoxetic acid-enhanced liver MRI in the triple arterial phases was significantly degraded compared with that of gadoteric acid-enhanced liver MRI, although better image scores were observed in the pre-contrast images in the gadoxetic acid group (P

OBJECTIVES The aim of this study was to evaluate the agreement and reproducibility of proton density fat fraction (PDFF) measurements using commercial magnetic resonance (MR) sequences across different imagers, vendors, and field strengths via a phantom experiment. MATERIALS AND METHODS Eleven fat-water emulsion phantoms of varying fat proportions (ie, 0-50 weight%) were constructed. Phantom PDFFs were estimated using commercial chemical shift-based MR imaging sequences with Siemens 1.5 T and 3.0 T, Philips 3.0 T, and GE 1.5 T and 3.0 T imagers, and MR spectroscopic sequences (HISTO) with Siemens 1.5 T and 3.0 T imagers. Agreement among the estimated PDFF values between commercial sequences was evaluated using Bland-Altman analysis. Reproducibility of the PDFF measurements across commercial sequences was evaluated using the reproducibility coefficient. The test-retest repeatability of the PDFF measurements was evaluated using the repeatability coefficient. RESULTS The repeatability coefficient of the PDFF measurements was 0.31% to 1.58% for the absolute PDFF value for commercial sequences. Statistically significant biases in the estimated PDFF were noted in 19 of 21 pairwise comparisons of commercial sequences (range of mean biases, -4.48% to 8.15% for the absolute PDFF value). The reproducibility coefficient of PDFF measurements was 9.0% for the absolute PDFF value over all commercial sequences and 10.6% for the absolute PDFF value over all chemical shift-based MR imaging sequences. CONCLUSIONS The measurement of the PDFF is highly repeatable with commercial MR sequences but is not reproducible across different sequences, imager vendors, and field strengths. The use of the same sequence and imager is therefore recommended for the longitudinal follow-up of hepatic steatosis using commercial MR sequences for PDFF measurements.

Gastrointestinal tract involvement in undifferentiated pleomorphic sarcoma (UPS) is extremely rare. To the authors’ knowledge, only 21 cases of primary small bowel UPS have been reported in the literature available in English. Reported CT findings in primary small bowel UPS have been nonspecific, and MRI findings have been reported in only one case. The present article describes a case involving a 72-year-old male with histologically confirmed primary UPS arising from the ileum, focusing on both CT and magnetic resonance enterography findings. On CT, primary small bowel UPS was noted as a heterogeneously enhanced small bowel mass without obstruction. Magnetic resonance enterography revealed heterogeneous intermediate T1 and T2 signal intensity, with hemorrhagic or necrotic foci within the mass and heterogeneous enhancement. The differential diagnosis included malignant gastrointestinal tumor; however, the prognosis of UPS is worse, with higher incidences of extra-abdominal metastasis.

Background: Adherent perinephric fat affects operative complexity during partial nephrectomy (PN) and it could be predicted using computed tomography (CT) based on the Mayo adhesive probability (MAP) score. Objectives: To investigate reproducible measurement methods of perinephric fat with comparison of two tailored methods for measurement of posterior perinephric fat thickness (PPFT) on preoperative CT and examine the association between the methods and operative complexity in PN. Patients and Methods: This cross-sectional study included 72 consecutive patients who underwent robotic-assisted or open PN. The data on operative time, ischemia time, and pathologic results were obtained. Two radiologists independently assessed PPFT based on the MAP system in the first session, and subsequently by using two tailored methods in the second session and scored for perinephric fat stranding. The nephrometry scoring system was used for stratifying the complexity of renal masses. Multiple linear regression was used to evaluate the determinants of operative time and ischemia time. Results: For measurement of PPFT, intraclass correlation coefficients between the reviewers using two detailed methods showed no statistical difference (P = 0.173) but were significantly higher than the coefficients scored in the first session (P < 0.001). Nephrometry score was a determinant of ischemia time (P < 0.001 and 0.001 for two reviewers) and PPFT was identified as a determinant of operative time (P ≤ 0.023 in all the analysis using two different methods for both the reviewers) in robotic-assisted PN. Nephrometry score was identified as a determinant of ischemia time in open PN as per one of the reviewers (P = 0.006). Conclusion: The tailored methods presented herein were more reproducible than the MAP score and demonstrated that increased PPFT was related to longer operative time in robotic-assisted, and not in open PN.

To investigate the diagnostic efficacy of ZOOMit coronal diffusion-weighted imaging (Z-DWI) and MR texture analysis (MRTA) for differentiating benign from malignant distal bile duct strictures. We retrospectively enrolled a total of 71 patients with distal bile duct stricture who underwent magnetic resonance cholangiopancreatography (MRCP). For quantitative analysis, the average apparent diffusion coefficient (ADC) value at suspected stricture sites was assessed on both Z-DWI and conventional DWI (C-DWI). For qualitative analysis, two reviewers independently reviewed two image sets containing different diffusion-weighted images, and receiver operating characteristic (ROC) curve analysis was performed. Several MRTA parameters were extracted from the area of the stricture on the ADC map of the ZOOMit coronal diffusion-weighted images using commercially available software. Among 71 patients, 26 patients were diagnosed with malignant stricture. On quantitative analysis, the average ADC value of the malignant and benign strictures, using Z-DWI, was 1.124 × 10−3 mm2/s and 1.522 × 10−3 mm2/s, respectively (P

To determine an accurate method for localizing rectal cancer using the distance from the anal verge on preoperative MRI. This prospective study included 50 patients scheduled for MRI evaluation of rectal cancer. After rectal filling with gel, MRI was performed with two markers attached at the anal verge. The distance between the tumor and the anal verge on a sagittal T2-weighted image (T2WI) was measured independently by two radiologists using six methods divided into three groups of similar measurement approaches, and compared to those obtained on rigid sigmoidoscopy. The anal verge location relative to the external anal sphincter was assessed on oblique coronal T2WI in reference to the markers. Correlation analysis was performed using the intraclass correlation coefficient (ICC) for verification, and a paired t test was used to evaluate the mean differences. The highest correlation (ICC 0.797–0.815) and the least mean difference (0.74–0.85 cm) with rigid sigmoidoscopy, and the least standard deviation (3.12–3.17 cm) were obtained in the direct methods group using a straight line from the anal verge to the tumor. The anal verge was localized within a range of − 1.4 to 1.5 cm (mean − 0.31 cm and − 0.22 cm) from the lower end of the external anal sphincter. The direct methods group provided the most accurate tumor distance among the groups. Among the direct methods, we recommend the direct mass method for its simplicity. Despite minor differences in location, the lower end of the external anal sphincter was a reliable anatomical landmark for the anal verge.

Background Difficult cannulation during endoscopic retrograde cholangiopancreatography (ERCP) is associated with increased complications; therefore, its prediction is important. Purpose To identify radiologic risk factors of difficult cannulation during ERCP based on computed tomography (CT) findings and to develop a predictive model for a difficult cannulation. Material and Methods A total of 171 patients with native papilla who underwent both enhanced CT and ERCP were recruited. Two radiologists independently measured the distal common bile duct (CBD) diameter and choledochoduodenal (CD) angle and analyzed CT images for presence of CBD stone and papilla bulging, size and type of periampullary diverticulum (PAD), and duodenal segment in which major papilla was located. Multivariate logistic regression analysis and decision-tree analysis were performed to identify risk factors for difficult cannulation. Results Thirty-nine patients underwent a difficult cannulation. The multivariate logistic regression analysis revealed that a smaller CBD diameter, presence of papilla bulging, location of the major papilla other than the descending duodenum, a smaller CD angle, and a higher worrisome PAD score were statistically relevant factors for difficult cannulation ( P Conclusion The CT findings-based decision-tree analysis model showed a high accuracy in predicting cannulation difficulty and may be helpful for making pre-ERCP strategy.

OBJECTIVE. The purposes of this study were to assess the performance of a 3D convolutional neural network (CNN) for automatic segmentation of prostates on MR images and to compare the volume estimates from the 3D CNN with those of the ellipsoid formula. MATERIALS AND METHODS. The study included 330 MR image sets that were divided into 260 training sets and 70 test sets for automated segmentation of the entire prostate. Among these, 162 training sets and 50 test sets were used for transition zone segmentation. Assisted by manual segmentation by two radiologists, the following values were obtained: estimates of ground-truth volume (VGT), software-derived volume (VSW), mean of VGT and VSW (VAV), and automatically generated volume from the 3D CNN (VNET). These values were compared with the volume calculated with the ellipsoid formula (VEL). RESULTS. The Dice similarity coefficient for the entire prostate was 87.12% and for the transition zone was 76.48%. There was no significant difference between VNET and VAV (p = 0.689) in the test sets of the entire prostate, whereas a significant difference was found between VEL and VAV (p < 0.001). No significant difference was found among the volume estimates in the test sets of the transition zone. Overall intraclass correlation coefficients between the volume estimates were excellent (0.887-0.995). In the test sets of entire prostate, the mean error between VGT and VNET (2.5) was smaller than that between VGT and VEL (3.3). CONCLUSION. The fully automated network studied provides reliable volume estimates of the entire prostate compared with those obtained with the ellipsoid formula. Fast and accurate volume measurement by use of the 3D CNN may help clinicians evaluate prostate disease.

Although the number of marine accidents has gradually decreased, fatal ones continue to occur. To prevent huge losses of life, environment and property, there are sufficient laws, rules and regulations, which include many technical solutions. Notably, many solutions for damaged ships are suggested to prevent capsizing and sinking. However, many pioneering ideas for damaged ships remain at the conception level because of the lack of technical and economic feasibility. To have both technical and economic feasibility in the design process, in this study, a business-model-adopted design process has been considered. Using this design process, a new concept for buoyancy support systems is studied, which considers the technical and economic matters in the design process. In addition, a fixed fire-fighting system and 3d laser scan data basis 3-dimensional buoyancy support system are been studied. To convince the usability of the suggested concept, computational simulation was used at the abstract level. Because there can be thousands of damage scenarios for a ship, sampled catastrophic damage scenarios are used in the case study to confirm the possibility of the suggested concept of buoyancy support systems.

To evaluate preoperative CT features that predict the development of postoperative inguinal hernia after robot-assisted laparoscopic radical prostatectomy (RALRP). We enrolled 160 men who underwent CT, subsequent RALRP, and at least three years of follow-up. CT was retrospectively reviewed in consensus by two radiologists for the presence of asymmetric spermatic cord fat and scrotal fluid collection. The diagnostic power of each CT feature for the development of postoperative inguinal hernia was calculated. Multiple logistic regression was performed to evaluate the association between CT features, clinical variables, and postoperative inguinal hernia. Surgically confirmed postoperative inguinal hernia developed in 17 patients. Asymmetric spermatic cord fat was demonstrated in 13 patients, and scrotal fluid collection was seen in 59 patients. The diagnostic values of asymmetric spermatic cord fat and scrotal fluid collection for postoperative inguinal hernia were 58.8% and 88.2% sensitivity, 97.9% and 69.2% specificity, 76.9% and 25.4% positive predictive value, 95.2% and 98.0% negative predictive value, and 93.8% and 71.3% accuracy, respectively. On multiple logistic regression analysis, asymmetric spermatic cord fat, scrotal fluid collection, and low BMI were significant predictive factors for the development of postoperative inguinal hernia (odds ratios: 135.8, 31.6, and 0.7, respectively). The presence of asymmetric spermatic cord fat or scrotal fluid collection was significantly associated with the development of postoperative inguinal hernia.

Accurate characterization of renal masses is essential for ensuring appropriate management. Low T2 signal intensity is a common feature of papillary renal cell carcinoma and fat-poor angiomyolipoma. Nonetheless, other types of renal cell carcinoma, oncocytoma, hemangioma, lymphoma, leiomyoma, and urothelial cell carcinoma also can show low signal intensities on T2-weighted imaging (T2WI). Histopathologic features that can lead to low T2 signal intensities in renal tumors include smooth muscle component, papillary architecture, a high nucleus-to-cytoplasm ratio, and hemorrhage. To establish an appropriate differential diagnosis for renal tumors on MRI, it is necessary to understand the relationship between the MR signal intensities and the histopathologic and morphologic features, in addition to contrast enhancement patterns and diffusion characteristics of the tumors.