Characterizing Hemodynamic Alterations in Glioma: Insights From Resting-State Functional MRI.

Background And Purpose: This study aims to characterize hemodynamic alterations in gliomas by analyzing global signal metrics and amplitude of low-frequency fluctuations (ALFF).

Methods: We analyzed resting-state functional MRI data from an open dataset. A total of 29 glioma patients (17 men; mean age: 44.

Development of a Large-Scale Dataset of Chest Computed Tomography Reports in Japanese and a High-Performance Finding Classification Model: Dataset Development and Validation Study.

Background: Recent advances in large language models have highlighted the need for high-quality multilingual medical datasets. Although Japan is a global leader in computed tomography (CT) scanner deployment and use, the absence of large-scale Japanese radiology datasets has hindered the development of specialized language models for medical imaging analysis. Despite the emergence of multilingual models and language-specific adaptations, the development of Japanese-specific medical language models has been constrained by a lack of comprehensive datasets, particularly in radiology.

Pulmonary Toxicity of Ethiodized Oil in the Venous Circulation in Mice: Radiological Findings and Pathological Correlation.

Purpose: To evaluate the acute and chronic pulmonary toxicity and determine the median lethal dose (LD50) of intravenously administered ethiodized oil, given the limited data on its systemic toxicity despite its known risk of inadvertent venous entry during lymphangiography.

Materials And Methods: Twenty female BALB/c mice received tail vein injections of saline (n=4) or ethiodized oil (Lipiodol, Guerbet, France) at 10 μL (n=4), 30 μL (n=8), or 50 μL (n=4). Survival outcomes, CT imaging findings, and histopathological features including hemosiderin deposition were evaluated.

Fine-Tuned Large Language Model for Extracting Pretreatment Pancreatic Cancer According to Computed Tomography Radiology Reports.

This study aimed to examine the performance of a fine-tuned large language model (LLM) in extracting pretreatment pancreatic cancer according to computed tomography (CT) radiology reports and to compare it with that of readers. This retrospective study included 2690, 886, and 378 CT reports for the training, validation, and test datasets, respectively. Clinical indication, image finding, and imaging diagnosis sections of the radiology report (used as input data) were reviewed and categorized into groups 0 (no pancreatic cancer), 1 (after treatment for pancreatic cancer), and 2 (pretreatment pancreatic cancer present) (used as reference data).

Reducing motion artifacts in the aorta: super-resolution deep learning reconstruction with motion reduction algorithm.

Purpose: To assess the efficacy of super-resolution deep learning reconstruction (SR-DLR) with motion reduction algorithm (SR-DLR-M) in mitigating aorta motion artifacts compared to SR-DLR and deep learning reconstruction with motion reduction algorithm (DLR-M).

Materials And Methods: This retrospective study included 86 patients (mean age, 65.0 ± 14.

The association between plasma betaine level and brain structural changes in schizophrenia.

Previous studies on metabolic profiling have shown decreased plasma levels of betaine in schizophrenia. Betaine serves as a substrate of betaine-homocysteine methyltransferase, which converts homocysteine to methionine. A decrease in betaine may lead to the elevation of homocysteine and an insufficient supply of methyl donors, which may affect DNA methylation.

Computational mechanisms of neuroimaging biomarkers uncovered by multicenter resting-state fMRI connectivity variation profile.

Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors.

N-Butyl-2-Cyanoacrylate-Assisted Balloon-Occluded Retrograde Transvenous Obliteration (NARTO) of Gastric Varices and Splenorenal Shunts.

Purpose: This retrospective study investigated the efficacy of n-butyl-2-cyanoacrylate (nBCA)-assisted balloon-occluded retrograde transvenous obliteration (NARTO) of gastric varices and splenorenal shunts.

Materials And Methods: Between November 2021 and February 2024, 16 patients (3 females, 13 males) underwent NARTO, consisting of balloon-occluded retrograde transvenous obliteration (BRTO) followed by nBCA embolization of the efferent draining vein without the balloon catheter remaining indwelling afterward, to treat gastric varices in 12 patients with liver cirrhosis or to improve portal vein flow or hepatic encephalopathy in four patients with splenorenal shunts after liver transplantation.

Results: NARTO was technically successful in all 16 patients without major procedure-related complications.

Brain microstructure alterations in bipolar disorder subtypes revealed by diffusion kurtosis imaging.

Bipolar disorder (BD) is often associated with extensive changes in white matter (WM) microstructure. While diffusion tensor imaging (DTI) has been used in BD studies, diffusion kurtosis imaging (DKI) has rarely been explored, particularly concerning the different subtypes of BD. This cross-sectional study compared DTI and DKI metrics of the brain WM in participants with BD type I (BD-I) and II (BD-II) and in healthy controls (HC).

MRI-Derived Tumor Characteristics As Prognostic Indicators in Prostate Cancer With Bone Metastases: A Preliminary Study.

Background While magnetic resonance imaging (MRI) is well-established for local assessment of prostate cancer, its prognostic utility in the metastatic setting remains poorly defined. We investigated whether pretreatment MRI findings can predict survival and disease progression in prostate cancer patients with bone metastases. Methods Of the 2,314 patients with pathologically proven prostate cancer between 2014 and 2021 in our hospital, this retrospective study finally included 19 patients with bone metastases who underwent prostate MRI within six months before diagnosis.

Deep learning reconstruction enhances image quality in contrast-enhanced CT venography for deep vein thrombosis.

Purpose: This study aimed to evaluate and compare the diagnostic performance and image quality of deep learning reconstruction (DLR) with hybrid iterative reconstruction (Hybrid IR) and filtered back projection (FBP) in contrast-enhanced CT venography for deep vein thrombosis (DVT).

Methods: A retrospective analysis was conducted on 51 patients who underwent lower limb CT venography, including 20 with DVT lesions and 31 without DVT lesions. CT images were reconstructed using DLR, Hybrid IR, and FBP.

Using Segment Anything Model 2 for Zero-Shot 3D Segmentation of Abdominal Organs in Computed Tomography Scans to Adapt Video Tracking Capabilities for 3D Medical Imaging: Algorithm Development and Validation.

Background: Medical image segmentation is crucial for diagnosis and treatment planning in radiology, but it traditionally requires extensive manual effort and specialized training data. With its novel video tracking capabilities, the Segment Anything Model 2 (SAM 2) presents a potential solution for automated 3D medical image segmentation without the need for domain-specific training. However, its effectiveness in medical applications, particularly in abdominal computed tomography (CT) imaging remains unexplored.

Fine-tuned large language model for classifying CT-guided interventional radiology reports.

BackgroundManual data curation was necessary to extract radiology reports due to the ambiguities of natural language.PurposeTo develop a fine-tuned large language model that classifies computed tomography (CT)-guided interventional radiology reports into technique categories and to compare its performance with that of the readers.Material and MethodsThis retrospective study included patients who underwent CT-guided interventional radiology between August 2008 and November 2024.

Comparison of publicly available artificial intelligence models for pancreatic segmentation on T1-weighted Dixon images.

Purpose: This study aimed to compare three publicly available deep learning models (TotalSegmentator, TotalVibeSegmentator, and PanSegNet) for automated pancreatic segmentation on magnetic resonance images and to evaluate their performance against human annotations in terms of segmentation accuracy, volumetric measurement, and intrapancreatic fat fraction (IPFF) assessment.

Materials And Methods: Twenty upper abdominal T1-weighted magnetic resonance series acquired using the two-point Dixon method were randomly selected. Three radiologists manually segmented the pancreas, and a ground-truth mask was constructed through a majority vote per voxel.

The coupling of global brain activity and cerebrospinal fluid flow as a potential predictive marker of brain amyloid-β accumulation.

Background: Impaired cerebrospinal fluid (CSF) clearance is thought to contribute to amyloid-β (Aβ) accumulation in Alzheimer's disease (AD). Global brain activity-CSF flow coupling (gBOLD-CSF coupling), measured through resting-state functional MRI, reflects CSF clearance capacity. A higher coupling value indicates weaker coupling.

High visceral-to-subcutaneous fat area ratio is an unfavorable prognostic indicator in patients with uterine sarcoma.

Purpose: Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of body composition parameters on overall survival with uterine sarcoma.

Materials And Methods: This multicenter study included 52 patients with uterine sarcomas treated at three Japanese hospitals between 2007 and 2023.

Efficacy of a large language model in classifying branch-duct intraductal papillary mucinous neoplasms.

Objectives: Appropriate categorization based on magnetic resonance imaging (MRI) findings is important for managing intraductal papillary mucinous neoplasms (IPMNs). In this study, a large language model (LLM) that classifies IPMNs based on MRI findings was developed, and its performance was compared with that of less experienced human readers.

Methods: The medical image management and processing systems of our hospital were searched to identify MRI reports of branch-duct IPMNs (BD-IPMNs).

Diagnostic Performance of a Large Language Model for Determining the Cause of Death: A Comparative Analysis of Clinical History, Postmortem Computed Tomography Findings, and Their Integration.

Introduction: This study evaluates the diagnostic performance of a large language model (LLM) in determining causes of death by comparing three different information sources.

Methods: A total of 150 consecutive adult in-hospital cadavers underwent postmortem CT and pathological autopsy (2009-2013). The diagnostic accuracy of Claude 3.

Bronchiectasis and airspace enlargement surrounding the lung nodule in dual-energy CT pulmonary angiography: comparison between iodine map and monochromatic image.

The purpose of the study is to investigate the degree and performance in the differential diagnosis of bronchiectasis/airspace enlargement in an iodine map obtainable from CT pulmonary angiography compared with monochromatic images. This retrospective study included 62 patients with a lung nodule who underwent CT pulmonary angiography. The iodine map and monochromatic image (70 keV) were reconstructed.

Fine-tuned large Language model for extracting newly identified acute brain infarcts based on computed tomography or magnetic resonance imaging reports.

Purpose: This study aimed to develop an automated early warning system using a large language model (LLM) to identify acute to subacute brain infarction from free-text computed tomography (CT) or magnetic resonance imaging (MRI) radiology reports.

Methods: In this retrospective study, 5,573, 1,883, and 834 patients were included in the training (mean age, 67.5 ± 17.

Postmortem computed tomography assessment of intracardiac and intravascular blood clots and gravitational sedimentation: clinical and laboratory associations in 114 in-hospital deaths.

Purpose: Postmortem computed tomography (PMCT) typically reveals blood clots and sedimentation in cardiac and vascular structures. We examined the associations between these postmortem findings and antemortem clinical and laboratory parameters in in-hospital death.

Material And Methods: This prospective study included 114 non-traumatic in-hospital deaths where PMCT was performed within 24 h postmortem.

Clinical and Imaging Features of Syphilis from Head to Toe.

Syphilis, a bacterial infection caused by is resurging globally. It is classified as acquired or congenital syphilis. Acquired syphilis progresses through primary, secondary, and tertiary stages.

Super-Resolution Deep Learning Reconstruction for T2*-Weighted Images: Improvement in Microbleed Lesion Detection and Image Quality.

Super-resolution deep learning reconstruction (SR-DLR) is a promising tool for improving image quality by enhancing spatial resolution compared to conventional deep learning reconstruction (DLR). This study aimed to evaluate whether SR-DLR improves microbleed detection and visualization in brain magnetic resonance imaging (MRI) compared to DLR. This retrospective study included 69 patients (66.

Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.

Prediction of severe disease (SVD) in patients with coronavirus disease (COVID-19) pneumonia at an early stage could allow for more appropriate triage and improve patient prognosis. Moreover, the visualization of the topological properties of COVID-19 pneumonia could help clinical physicians describe the reasons for their decisions. We aimed to construct predictive models of SVD in patients with COVID-19 pneumonia at an early stage on computed tomography (CT) images using SVD-specific features that can be visualized on accumulated Betti number (BN) maps.

Practical brain MRI guidelines for anti-Aβ antibody treatment in early symptomatic Alzheimer's disease.

Purpose: These guidelines aim to support magnetic resonance imaging (MRI) diagnosis in patients receiving anti-amyloid β (Aβ) antibody treatment without restricting treatment eligibility.

Materials And Methods: These guidelines were collaboratively established by Japan Radiological Society, The Japanese Society of Neuroradiology, and Japanese Society for Magnetic Resonance in Medicine by reviewing existing literature and the results of clinical trials.

Results: Facility standards should comply with the "Optimal Use Promotion Guidelines" of Japan, and physicians should possess comprehensive knowledge of amyloid-related imaging abnormalities (ARIA) and expertise in brain MRI interpretation.