Implications of unconventional histological subtypes on magnetic resonance imaging and oncological outcomes in patients who have undergone radical prostatectomy.

The prognostic significance of unconventional histology (UH) subtypes including intraductal carcinoma of the prostate (IDC-P), ductal adenocarcinoma, and cribriform pattern has been investigated for prostate cancer (PCa). However, little is known about magnetic resonance imaging (MRI) features and the oncological impact of tumor localization in localized PCa with UH. Clinical data of 211 patients with acinar adenocarcinoma (conventional histology [CH]) and 82 patients with UH who underwent robotic-assisted radical prostatectomy (RARP) were reviewed. Patients with UH are more likely to be older and have higher Gleason grade group, higher Prostate Imaging-Reporting and Data System (PI-RADS) v2.1 score, and larger tumor volume (TV) than those with CH. Multivariate analysis identified the presence of UH as an independent prognostic factor for progression-free survival (PFS) (hazard ration (HR) 2.41, 95% confidence interval (CI) 0.22-0.79, P = 0.0073). No significant difference in PFS was seen regarding tumor localization (transition zone [TZ] or peripheral zone [PZ]) in patients with UH (P = 0.8949), whereas PZ cancer showed shorter PFS in patients with CH (P = 0.0174). PCa with UH was associated with higher progression than PCa with CH among resection margin (RM)-negative cases (P < 0.0001). Further, increased PI-RADS v2.1 score did not correlate with larger TV in UH (P = 0.991), whereas a significant difference in TV was observed in CH (P < 0.0001). The prognostic significance of UH tumor was independent of tumor localization, and shorter PFS was observed even in RM-negative cases, indicating an aggressive subtype with micro-metastatic potential. Furthermore, UH tumors are more likely to harbor a large TV despite PI-RADS v2.1 score ≤ 3. These findings will help optimal perioperative management for PCa with UH.

Diagnostic Sensitivity and Symptomatic Relevance of Dopamine Transporter Imaging and Myocardial Sympathetic Scintigraphy in Patients with Dementia with Lewy Bodies.

Background: Dementia with Lewy bodies (DLB) presents with various symptoms, posing challenges for early diagnosis challenging. Dopamine transporter (123I-FP-CIT) single-photon emission tomography (SPECT) and 123I-meta-iodobenzylguanidine (123I-MIBG) imaging are crucial diagnostic biomarkers. Hypothesis about body- and brain-first subtypes of DLB indicate that some DLB may show normal 123I-FP-CIT or 123I-MIBG results; but the characteristic expression of these two subtypes remains unclear. Objective: This study aimed to evaluate the diagnostic sensitivity of 123I-FP-CIT and 123I-MIBG imaging alone, combined in patients with DLB and explore symptoms associated with the abnormal imaging results. Methods: Demographic data, clinical status, and imaging results were retrospectively collected from patients diagnosed with possible DLB. Both images were quantified using semi-automated software, and the sensitivity of each imaging modality and their combination was calculated. Demographic data, cognition, and motor and non-motor symptoms were compared among the subgroups based on the imaging results. Symptoms related to each imaging abnormality were examined using binomial logistic regression analyses. Results: Among 114 patients with DLB, 80 underwent 123I-FP-CIT SPECT (sensitivity: 80.3%), 83 underwent 123I-MIBG imaging (68.2%), and 66 both (sensitivity of either abnormal result: 93.9%). Visual hallucinations differed among the four subgroups based on imaging results. Additionally, nocturia and orthostatic hypotension differed between abnormal and normal 123I-MIBG images. Conclusions: Overall, 123I-FP-CIT SPECT was slightly higher sensitivity than 123I-MIBG imaging, with combined imaging increasing diagnostic sensitivity. Normal results of a single imaging test may not refute DLB. Autonomic symptoms may lead to abnormal 123I-MIBG scintigraphy findings indicating body-first subtype of patients with DLB.

Preoperative PI-RADS v2.1 Scoring System Improves Risk Classification in Patients Undergoing Radical Prostatectomy.

BACKGROUND/AIM: The purpose of this study was to examine the prognostic value of Prostate imaging-reporting and data system (PI-RADS) v2.1 scoring system in patients who underwent radical prostatectomy (RP). PATIENTS AND METHODS: Clinical data of 294 patients who received RP between 2006 and 2018 were reviewed and multiple parameters including PI-RADS v2.1 score were employed to identify predictive factors for biochemical recurrence (BCR). Tumor volume was calculated from prostatectomy specimens. RESULTS: Median age at operation and initial PSA level were 67 years old and 7.68 ng/ml, respectively. 44.9 and 24.8% of patients were diagnosed with PI-RADS score 4 and 5 prior to biopsies, respectively. BCR was observed in 17% of patients and median observation period was 63.43 months. After multivariate analysis, PI-RADS v2.1 score 5 [hazard ratio (HR)=2.24, p=0.0124] was an independent predictive factor of BCR in addition to clinical T stage (≥2c) (HR=2.32, p=0.0093) and biopsy Gleason score (≥8) (HR=2.81, p=0.0007). Furthermore, PI-RADS score 5 significantly stratified the prognosis in D'Amico intermediate- and high-risk groups (p=0.0174 and p=0.0013, respectively). We established novel risk classifications including PI-RADS v2.1 score and found that prognostic capabilities were improved as compared to the D'Amico classification. CONCLUSION: The PI-RADS v2.1 score exhibited significant prognostic value in patients with localized prostate cancer following RP. Risk classifications based on PI-RADS v2.1 score might provide better ability for predicting oncological outcomes as compared to the D'Amico classification system.

Leg position effects on the femoral neurovascular bundle location during a direct anterior approach total hip arthroplasty: a radiographic study.

BACKGROUND: Femoral neurovascular injury is a serious complication in a direct anterior approach (DAA) total hip arthroplasty. However, dynamic neurovascular bundle location changes during the approach were not examined. Thus, this study aimed to analyze the effects of leg position on the femoral neurovascular bundle location using magnetic resonance imaging (MRI). METHODS: This study scanned 30 healthy volunteers (15 males and 15 females) with 3.0T MRI in a supine and 30-degree hip extension position with the left leg in a neutral rotation position and the right leg in a 45-degree external extension position. The minimum distance from the edge of the anterior acetabulum to the femoral nerve (dFN), artery, and vein were measured on axial T1-weighted images at the hip center level, as well as the angle to the horizontal line of the femoral nerve (aFN), artery (aFA), and vein from the anterior acetabulum. RESULTS: The dFN in the supine position with external rotation was significantly larger than supine with neutral and extension with external rotation position (20.7, 19.5, and 19.0; p = 0.031 and 0.012, respectively). The aFA in supine with external rotation was significantly larger than in other postures (52.4°, 34.2°, and 36.2°, p < 0.001, respectively). The aFV in supine with external rotation was significantly larger than in supine with a neutral position (52.3° versus 47.7°, p = 0.037). The aFN in supine and external rotation was significantly larger than other postures (54.6, 38.2, and 33.0, p < 0.001, respectively). CONCLUSIONS: This radiographic study revealed that the leg position affected the neurovascular bundle location. These movements can be the risk of direct neurovascular injury or traction.

Deep learning-based coronary computed tomography analysis to predict functionally significant coronary artery stenosis.

Fractional flow reserve derived from coronary CT (FFR-CT) is a noninvasive physiological technique that has shown a good correlation with invasive FFR. However, the use of FFR-CT is restricted by strict application standards, and the diagnostic accuracy of FFR-CT analysis may potentially be decreased by severely calcified coronary arteries because of blooming and beam hardening artifacts. The aim of this study was to evaluate the utility of deep learning (DL)-based coronary computed tomography (CT) data analysis in predicting invasive fractional flow reserve (FFR), especially in cases with severely calcified coronary arteries. We analyzed 184 consecutive cases (241 coronary arteries) which underwent coronary CT and invasive coronary angiography, including invasive FFR, within a three-month period. Mean coronary artery calcium scores were 963 ± 1226. We evaluated and compared the vessel-based diagnostic accuracy of our proposed DL model and a visual assessment to evaluate functionally significant coronary artery stenosis (invasive FFR < 0.80). A deep neural network was trained with consecutive short axial images of coronary arteries on coronary CT. Ninety-one coronary arteries of 89 cases (48%) had FFR-positive functionally significant stenosis. On receiver operating characteristics (ROC) analysis to predict FFR-positive stenosis using the trained DL model, average area under the curve (AUC) of the ROC curve was 0.756, which was superior to the AUC of visual assessment of significant (≥ 70%) coronary artery stenosis on CT (0.574, P = 0.011). The sensitivity, specificity, positive and negative predictive value (PPV and NPV), and accuracy of the DL model and visual assessment for detecting FFR-positive stenosis were 82 and 36%, 68 and 78%, 59 and 48%, 87 and 69%, and 73 and 63%, respectively. Sensitivity and NPV for the prediction of FFR-positive stenosis were significantly higher with our DL model than visual assessment (P = 0.0004, and P = 0.024). DL-based coronary CT data analysis has a higher diagnostic accuracy for functionally significant coronary artery stenosis than visual assessment.

Tumor localization by Prostate Imaging and Reporting and Data System (PI-RADS) version 2.1 predicts prognosis of prostate cancer after radical prostatectomy.

An improved reading agreement rate has been reported in version 2.1 (v2.1) of the Prostate Imaging and Reporting and Data System (PI-RADS) compared with earlier versions. To determine the predictive efficacy of bi-parametric MRI (bp-MRI) for biochemical recurrence (BCR), our study assessed PI-RADS v2.1 score and tumor location in Japanese prostate cancer patients who underwent radical prostatectomy. Retrospective analysis was performed on the clinical data of 299 patients who underwent radical prostatectomy at Chiba University Hospital between 2006 and 2018. The median prostate-specific antigen (PSA) level before surgery was 7.6 ng/mL. Preoperative PI-RADS v2.1 categories were 1-2, 3, 4, and 5 in 35, 56, 138, and 70 patients, respectively. Tumor location on preoperative MRI was 107 in the transition zone (TZ) and 192 in the peripheral zone (PZ). BCR-free survival was significantly shorter in the PZ group (p = 0.001). In the total prostatectomy specimens, preoperative PI-RADS category 5, radiological tumor location, pathological seminal vesicle invasion, and Grade Group ≥ 3 were independent prognostic factors of BCR. These four risk factors have significant potential to stratify patients and predict prognosis. Radiological tumor location and PI-RADS v2.1 category using bp-MRI may enable prediction of BCR following radical prostatectomy.

Prognosis prediction of uterine cervical cancer using changes in the histogram and texture features of apparent diffusion coefficient during definitive chemoradiotherapy.

OBJECTIVES: We investigated prospectively whether, in cervical cancer (CC) treated with concurrent chemoradiotherapy (CCRT), the Apparent diffusion coefficient (ADC) histogram and texture parameters and their change rates during treatment could predict prognosis. METHODS: Fifty-seven CC patients treated with CCRT at our institution were included. They underwent MRI scans up to four times during the treatment course (1st, before treatment [n = 41], 2nd, at the start of image-guided brachytherapy (IGBT) [n = 41], 3rd, in the middle of IGBT [n = 27], 4th, after treatment [n = 53]). The entire tumor was manually set as the volume of interest (VOI) manually in the axial images of the ADC map by two radiologists. A total of 107 image features (morphology features 14, histogram features 18, texture features 75) were extracted from the VOI. The recurrence prediction values of the features and their change rates were evaluated by Receiver operating characteristics (ROC) analysis. The presence or absence of local and distant recurrence within two years was set as an outcome. The intraclass correlation coefficient (ICC) was also calculated. RESULTS: The change rates in kurtosis between the 1st and 3rd, and 1st and 2nd MRIs, and the change rate in grey level co-occurrence matrix_cluster shade between the 2nd and 3rd MRIs showed particularly high predictive powers (area under the ROC curve = 0.785, 0.759, and 0.750, respectively), which exceeded the predictive abilities of the parameters obtained from pre- or post-treatment MRI only. The change rate in kurtosis between the 1st and 2nd MRIs had good reliability (ICC = 0.765). CONCLUSIONS: The change rate in ADC kurtosis between the 1st and 2nd MRIs was the most reliable parameter, enabling us to predict prognosis early in the treatment course.

DRD2 Taq1A Polymorphism-Related Brain Volume Changes in Parkinson's Disease: Voxel-Based Morphometry.

Taq1A polymorphism is a DRD2 gene variant located in an exon of the ANKK1 gene and has an important role in the brain's dopaminergic functions. Some studies have indicated that A1 carriers have an increased risk of developing Parkinson's disease (PD) and show poorer clinical performance than A2 homo carriers. Previous studies have suggested that A1 carriers had fewer dopamine D2 receptors in the caudate and increased cortical activity as a compensatory mechanism. However, there is little information about morphological changes associated with this polymorphism in patients with PD. The study's aim was to investigate the relationship between brain volume and Taq1A polymorphism in PD using voxel-based morphometry (VBM). Based on Taq1A polymorphism, 103 patients with PD were divided into two groups: A1 carriers (A1/A1 and A1/A2) and A2 homo carriers (A2/A2). The volume of the left prefrontal cortex (PFC) was significantly decreased in A2 homo carriers compared to A1 carriers. This finding supports the association between Taq1A polymorphism and brain volume in PD and may explain the compensation of cortical function in A1 carriers with PD.

Dopaminergic Correlates of Regional Cerebral Blood Flow in Parkinsonian Disorders.

BACKGROUND: Cerebral blood flow (CBF) and dopamine transporter (DAT) images are clinically used for the differential diagnosis of parkinsonian disorders. OBJECTIVES: This study aimed to examine the correlation of CBF with striatal DAT in patients with Parkinson's disease (PD) and atypical parkinsonian syndromes (APS) and evaluate the diagnostic power of DAT-correlated CBF in PD through machine learning with each imaging modality alone or in combination. METHODS: Fifty-eight patients with PD and 71 with APS (24 with multiple system atrophy, 21 with progressive supranuclear palsy, and 26 with corticobasal syndrome) underwent 123 I-IMP and 123 I-FP-CIT single-photon emission computed tomography. Multiple regression analyses for CBF and striatal DAT binding were conducted on each group. PD probability was predicted by machine learning and receiver operating characteristic curves. RESULTS: The PD group showed more affected striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the bilateral cerebellar perfusion. In corticobasal syndrome, striatal DAT binding positively correlated with the ipsilateral prefrontal perfusion and negatively with the contralateral precentral perfusion. In Richardson's syndrome, striatal DAT binding positively correlated with perfusion in the ipsilateral precentral cortex and basal ganglia. Machine learning showed that the combination of CBF and DAT was better for delineating PD from APS (area under the curve [AUC] = 0.87) than either CBF (0.67) or DAT (0.50) alone. CONCLUSIONS: In PD and four-repeat tauopathy, prefrontal perfusion was related to ipsilateral nigrostriatal dopaminergic function. This dual-tracer frontostriatal relationship may be effectively used as a diagnostic tool for delineating PD from APS. © 2022 International Parkinson and Movement Disorder Society.

Diagnostic value of peroral cholangioscopy in addition to computed tomography for indeterminate biliary strictures.

BACKGROUND: Peroral cholangioscopy (POCS) has been used to overcome the difficulty in diagnosing indeterminate biliary stricture or tumor spread. However, the value of adding POCS to computed tomography (CT) remains unclear. Our aim was to evaluate the diagnostic value of adding POCS to CT for indeterminate biliary stricture and tumor spread by interpretation of images focusing on the high diagnostic accuracy of visual findings in POCS. METHODS: We retrospectively identified 52 patients with biliary stricture who underwent endoscopic retrograde cholangiography (ERC) at our institution between January 2013 and December 2018. Two teams, each composed of an expert endoscopist and surgeon, performed the interpretation independently, referring to the CT findings of the radiologist. The CT + ERC + POCS images (POCS group) were evaluated 4 weeks after the evaluation of CT + ERC images (CT group). A 5-point scale (1: definitely benign to 5: definitely malignant) was used to determine the confident diagnosis rate, which was defined as an evaluation value of 1 or 5. Tumor spread was also evaluated. RESULTS: In the evaluation of 45 malignant diagnoses, the score was significantly closer to 5 in the POCS group than in the CT group in both teams (P < 0.001). The confident diagnosis rate was significantly higher for the POCS group (92% and 73%) than for the CT group (25% and 12%) in teams 1 and 2, respectively (P < 0.001). We found no significant difference in diagnostic accuracy for tumor spread between the groups. CONCLUSION: Visual POCS findings confirmed the diagnosis of biliary strictures. POCS was useful in cases of indefinite diagnosis of biliary strictures by CT.

Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units.

Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting. Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians. Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47). Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians.

Development of attenuation correction methods using deep learning in brain-perfusion single-photon emission computed tomography.

PURPOSE: Computed tomography (CT)-based attenuation correction (CTAC) in single-photon emission computed tomography (SPECT) is highly accurate, but it requires hybrid SPECT/CT instruments and additional radiation exposure. To obtain attenuation correction (AC) without the need for additional CT images, a deep learning method was used to generate pseudo-CT images has previously been reported, but it is limited because of cross-modality transformation, resulting in misalignment and modality-specific artifacts. This study aimed to develop a deep learning-based approach using non-attenuation-corrected (NAC) images and CTAC-based images for training to yield AC images in brain-perfusion SPECT. This study also investigated whether the proposed approach is superior to conventional Chang's AC (ChangAC). METHODS: In total, 236 patients who underwent brain-perfusion SPECT were randomly divided into two groups: the training group (189 patients; 80%) and the test group (47 patients; 20%). Two models were constructed using Autoencoder (AutoencoderAC) and U-Net (U-NetAC), respectively. ChangAC, AutoencoderAC, and U-NetAC approaches were compared with CTAC using qualitative analysis (visual evaluation) and quantitative analysis (normalized mean squared error [NMSE] and the percentage error in each brain region). Statistical analyses were performed using the Wilcoxon signed-rank sum test and Bland-Altman analysis. RESULTS: U-NetAC had the highest visual evaluation score. The NMSE results for the U-NetAC were the lowest, followed by AutoencoderAC and ChangAC (P < 0.001). Bland-Altman analysis showed a fixed bias for ChangAC and AutoencoderAC and a proportional bias for ChangAC. ChangAC underestimated counts by 30-40% in all brain regions. AutoencoderAC and U-NetAC produced mean errors of <1% and maximum errors of 3%, respectively. CONCLUSION: New deep learning-based AC methods for AutoencoderAC and U-NetAC were developed. Their accuracy was higher than that obtained by ChangAC. U-NetAC exhibited higher qualitative and quantitative accuracy than AutoencoderAC. We generated highly accurate AC images directly from NAC images without the need for intermediate pseudo-CT images. To verify our models' generalizability, external validation is required.

The distance between the femoral nerve and anterior acetabulum is significantly shorter in hip osteoarthritis than in non-osteoarthritis hip.

BACKGROUND: The appropriate position of retractors to minimize the risk of femoral nerve palsy remains uncertain. The purpose of this imaging study was to evaluate the distance between the femoral nerve (FN) and anterior acetabulum (AA) in hip osteoarthritis (OA). METHODS: Forty-one patients with unilateral hip OA underwent magnetic resonance imaging. Three measurement levels were defined and the minimum distance between the femoral nerve (FN) margin and anterior acetabulum (AA) rim was measured on axial T1-weighted images on the OA and normal sides at each level, with reference to an advanced neurography view. The cross-sectional area (CSA) of the iliopsoas muscle was also measured at each level bilaterally by three observers. Distances and CSAs were compared between the OA and normal side. Multiple regression analysis was performed to identify variables associated with the distance in OA. RESULTS: The mean minimum FN to AA distances in OA were 19.4 mm at the top of the anterior inferior iliac spine (AIIS), 24.3 mm at the bottom of the AIIS, and 21.0 mm at the tip of the greater trochanter. These distances were significantly shorter than in normal hips at the top and bottom of the AIIS, with mean differences of 1.6 and 5.8 mm, respectively (p = 0.012, p < 0.001). CSAs of the iliopsoas in OA were significantly smaller at all levels (all p < 0.001), with reductions of 10.5 to 17.9%. The CSA of the iliopsoas at the bottom of the AIIS was associated with the FN to AA distance at the same level (p = 0.026). Interobserver reliabilities for measurements were very good to perfect (intraclass correlation coefficients 0.897 to 0.966). CONCLUSIONS: To minimize the risk of femoral nerve palsy, surgeons should consider the change of the femoral nerve to anterior acetabulum distance in osteoarthritic hip surgery.

High prevalence of pulmonary embolism prior to cancer therapies in patients with ovarian and endometrial cancers detected by contrast-enhanced CT using D-dimer as an index.

OBJECTIVE: We aimed to evaluate the prevalence of pulmonary embolism (PE) before cancer therapies in patients with ovarian and endometrial cancers with enhanced computed tomography (CT) using D-dimer (DD), and determine the optimal cut-off level of DD. METHODS: Since 2009, we have performed preoperative venous thromboembolism (VTE) screening of patients with ovarian and endometrial cancer. For patients with DD levels of more than 1.0 µg/ml, enhanced CT images were obtained from the pulmonary apex to the foot to detect PE and deep venous thrombosis (DVT) simultaneously. RESULTS: Among patients with ovarian cancer, 84 of 413 (20.3%) had VTEs (DVT alone, n = 31 [7.5%]; PE with or without DVT, n = 53 [12.8%]; PE alone, n = 12 [2.9%]). Among patients with endometrial cancer, 50 of 455 (11.0%) had VTEs (DVT alone, n = 19 [4.2%]; PE with or without DVT, n = 31 [6.8%], PE alone, n = 14 [3.1%]). The optimal cut-off level of DD was estimated to be ≥1.5 and ≥1.2 µg/ml in ovarian and endometrial cancers, respectively. CONCLUSION: Our study revealed a high prevalence of PE before cancer therapies in patients with ovarian and endometrial cancers by enhanced CT using DD.

Impact of clinical information on CT diagnosis by radiologist and subsequent clinical management by physician in acute abdominal pain.

OBJECTIVE: The impact of clinical information on radiological diagnoses and subsequent clinical management has not been sufficiently investigated. This study aimed to compare diagnostic performance between radiological reports made with and without clinical information and to evaluate differences in the clinical management decisions based on each of these reports. METHODS: We retrospectively reviewed 410 patients who presented with acute abdominal pain and underwent unenhanced (n = 248) or enhanced CT (n = 162). Clinical information including age, sex, current and past history, physical findings, and laboratory tests were collected. Six radiologists independently interpreted CTs that were randomly assigned with or without clinical information, made radiological diagnoses, and scored the diagnostic confidence level. Four general and emergency physicians simulated clinical management (i.e., followed up in the outpatient clinic, hospitalized for conservative therapy, or referred to other departments for invasive therapy) based on reports made with or without the clinical information. Reference standards for the radiological diagnoses and clinical management were defined by an independent expert panel. RESULTS: The radiological diagnoses made with clinical information were more accurate than those made without clinical information (93.7% vs. 87.8%, p = 0.008). Median interpretation time for radiological reporting with clinical information was significantly shorter than that without clinical information (median 122.0 vs. 139.0 s, p < 0.001). Clinical simulation better matched the reference standard for clinical management when radiological diagnoses were made with reference to clinical information (97.3% vs. 87.8%, p < 0.001). CONCLUSION: Access to adequate clinical information enables accurate radiological diagnoses and appropriate subsequent clinical management of patients with acute abdominal pain. KEY POINTS: • Radiological interpretation improved diagnostic accuracy and confidence level when clinical information was provided. • Providing clinical information did not extend the interpretation time required by radiologists. • Radiological interpretation with clinical information led to correct clinical management by physicians.

Multidimensional Deep Learning Reduces False-Positives in the Automated Detection of Cerebral Aneurysms on Time-Of-Flight Magnetic Resonance Angiography: A Multi-Center Study.

Current deep learning-based cerebral aneurysm detection demonstrates high sensitivity, but produces numerous false-positives (FPs), which hampers clinical application of automated detection systems for time-of-flight magnetic resonance angiography. To reduce FPs while maintaining high sensitivity, we developed a multidimensional convolutional neural network (MD-CNN) designed to unite planar and stereoscopic information about aneurysms. This retrospective study enrolled time-of-flight magnetic resonance angiography images of cerebral aneurysms from three institutions from June 2006 to April 2019. In the internal test, 80% of the entire data set was used for model training and 20% for the test, while for the external tests, data from different pairs of the three institutions were used for training and the remaining one for testing. Images containing aneurysms > 15 mm and images without aneurysms were excluded. Three deep learning models [planar information-only (2D-CNN), stereoscopic information-only (3D-CNN), and multidimensional information (MD-CNN)] were trained to classify whether the voxels contained aneurysms, and they were evaluated on each test. The performance of each model was assessed using free-response operating characteristic curves. In total, 732 aneurysms (5.9 ± 2.5 mm) of 559 cases (327, 120, and 112 from institutes A, B, and C; 469 and 263 for 1.5T and 3.0T MRI) were included in this study. In the internal test, the highest sensitivities were 80.4, 87.4, and 82.5%, and the FPs were 6.1, 7.1, and 5.0 FPs/case at a fixed sensitivity of 80% for the 2D-CNN, 3D-CNN, and MD-CNN, respectively. In the external test, the highest sensitivities were 82.1, 86.5, and 89.1%, and 5.9, 7.4, and 4.2 FPs/cases for them, respectively. MD-CNN was a new approach to maintain sensitivity and reduce the FPs simultaneously.

Comparison of Visceral Fat Reduction by Ipragliflozin and Metformin in Elderly Type 2 Diabetes Patients: Sub-Analysis of a Randomized-Controlled Study.

INTRODUCTION: To compare the effects of ipragliflozin, a sodium-glucose transporter 2 inhibitor, with those of metformin on visceral fat (as well as muscles and bones) in Japanese elderly patients with type 2 diabetes (T2D), we conducted a sub-analysis of a prospective, multicenter, blinded-endpoint randomized-controlled study. METHODS: In total, 103 patients with T2D (body mass index ≥ 22 kg/m2; glycated hemoglobin, 7-10%) and being treated with sitagliptin (a dipeptidyl peptidase-4 inhibitor) were included and randomized to receive ipragliflozin or metformin. The primary outcome was the change in visceral fat area measured using computed tomography 24 weeks following treatment. The secondary outcomes included changes in subcutaneous and total fat area, muscle volume, bone density measured using computed tomography, handgrip strength, bone markers, plasma glucose, insulin, homeostasis model assessment (HOMA)2-beta, HOMA2-R, glycated hemoglobin, lipid panel, uric acid, blood pressure, adiponectin, and high-sensitivity C-reactive protein. All patients aged 65-74 years were selected for sub-analysis. RESULTS: The sub-analysis included 15 and 14 patients in the ipragliflozin and metformin groups, respectively. The patients' backgrounds were well balanced. Visceral fat area reduction was greater in the ipragliflozin group than in the metformin group (- 10.58% vs. - 6.93%; P = 0.034). There were significant differences in the changes in bone absorption markers, uric acid, and total cholesterol levels between the groups. CONCLUSION: Ipragliflozin significantly reduced the visceral fat area compared with metformin when added to sitagliptin in elderly patients with T2D. Long-term and large-scale studies are required to elucidate whether ipragliflozin is suitable for elderly patients. TRIAL REGISTRATION: The study was registered at https://www.umin.ac.jp/ctr/ (UMIN-ID: UMIN 000015170).

Effects of ipragliflozin versus metformin in combination with sitagliptin on bone and muscle in Japanese patients with type 2 diabetes mellitus: Subanalysis of a prospective, randomized, controlled study (PRIME-V study).

AIMS/INTRODUCTION: Recent randomized clinical trials have suggested that sodium-glucose cotransporter 2 inhibitors might reduce cardiovascular events and heart failure, and have renal protective effects. Despite these remarkable benefits, the effects of sodium-glucose cotransporter 2 inhibitors on bone and muscle are unclear. MATERIALS AND METHODS: A subanalysis of a randomized controlled study was carried out to evaluate the effects of the sodium-glucose cotransporter 2 inhibitor, ipragliflozin, versus metformin on bone and muscle in Japanese patients with type 2 diabetes mellitus (baseline body mass index ≥22 kg/m2 and hemoglobin A1c 7-10%) who were already receiving sitagliptin. These patients were randomly administered ipragliflozin 50 mg or metformin 1,000-1,500 mg daily. The effects of these medications on the bone formation marker, bone alkali phosphatase; the bone resorption marker, tartrate-resistant acid phosphatase 5b (TRACP-5b); handgrip strength; abdominal cross-sectional muscle area; and bone density of the fourth lumbar vertebra were evaluated. RESULTS: After 24 weeks of treatment, the changes in bone density of the fourth lumbar vertebra, handgrip strength and abdominal cross-sectional muscle area were not significantly different between the two groups. However, TRACP-5b levels increased in patients treated with ipragliflozin compared with patients treated with metformin (median 11.94 vs -10.30%, P < 0.0001), showing that ipragliflozin can promote bone resorption. CONCLUSIONS: There were no adverse effects on bone or muscle when sitagliptin was used in combination with either ipragliflozin or metformin. However, ipragliflozin combination increased the levels of TRACP-5b. A long-term study is required to further understand the effects of this TRACP-5b increase caused by ipragliflozin.

The Neuropsychological Correlates of Brain Perfusion and Gray Matter Volume in Alzheimer's Disease.

BACKGROUND: Neuropsychological tests, structural neuroimaging, and functional neuroimaging are employed as diagnostic and monitoring biomarkers of patients with Alzheimer's disease (AD)Objective:We aimed to elucidate the similarities and differences in neuropsychological tests and neuroimaging with the use of the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale cognitive subscale (ADAS-cog), structural magnetic resonance image (MRI), and perfusion single photon emission computed tomography (SPECT), and parametric image analyses to understand its role in AD. METHODS: Clinically-diagnosed AD patients (n = 155) were scanned with three-dimensional T1-weighted MRI and N-isopropyl-p-[123I] iodoamphetamine SPECT. Statistical parametric mapping 12 was used for preprocessing images, statistical analyses, and voxel-based morphometry for gray matter volume analyses. Group comparison (AD versus healthy controls), multiple regression analyses with MMSE, ADAS-cog total score, and ADAS-cog subscores as variables, were performed. RESULTS: The AD group showed bilateral hippocampal volume reduction and hypoperfusion in the bilateral temporo-parietal lobe and posterior midline structures. Worse MMSE and ADAS-cog total score were associated with bilateral temporo-parietal volume loss and hypoperfusion. MMSE, but not ADAS-cog, was associated with the posterior midline structures. The ADAS-cog subscores were associated with the temporal volume, while perfusion analyses were linked to the left temporo-parietal region with the language function and right analogous region with the constructional praxis subscore. CONCLUSION: MMSE and ADAS-cog are associated with temporo-parietal regions, both in volume and perfusion. The MMSE score is associated with posterior midline structures and linked to an abnormal diagnostic AD pattern. Perfusion image analyses better represents the cognitive function in AD patients.