Investigative Radiology最新文献

Abstract: In children and adults, quantitative imaging examinations determine the effectiveness of treatment for liver disease. However, pediatric liver disease differs in presentation from liver disease in adults. Children also needed to be followed for a longer period from onset and have less control of their bodies, showing more movement than adults during imaging examinations, which leads to a greater need for sedation. Thus, it is essential to appropriately tailor and accurately perform noninvasive imaging tests in these younger patients. This article is an overview of updated imaging techniques used to assess liver disease quantitatively in children. The common initial imaging study for diffuse liver disease in pediatric patients is ultrasound. In addition to preexisting echo analysis, newly developed attenuation imaging techniques have been introduced to evaluate fatty liver. Ultrasound elastography is also now actively used to evaluate liver conditions, and the broad age spectrum of the pediatric population requires caution to be taken even in the selection of probes. Magnetic resonance imaging (MRI) is another important imaging tool used to evaluate liver disease despite requiring sedation or anesthesia in young children because it allows quantitative analysis with sequences such as fat analysis and MR elastography. In addition to ultrasound and MRI, we review quantitative imaging methods specifically for fatty liver, Wilson disease, biliary atresia, hepatic fibrosis, Fontan-associated liver disease, autoimmune hepatitis, sinusoidal obstruction syndrome, and the transplanted liver. Lastly, concerns such as growth and motion that need to be addressed specifically for children are summarized.

Abstract: Recent technological advancements have revolutionized routine brain magnetic resonance imaging (MRI) sequences, offering enhanced diagnostic capabilities in intracranial disease evaluation. This review explores 2 pivotal breakthrough areas: deep learning reconstruction (DLR) and quantitative MRI techniques beyond conventional structural imaging. DLR using deep neural networks facilitates accelerated imaging with improved signal-to-noise ratio and spatial resolution, enhancing image quality with short scan times. DLR focuses on supervised learning applied to clinical implementation and applications. Quantitative MRI techniques, exemplified by 2D multidynamic multiecho, 3D quantification using interleaved Look-Locker acquisition sequences with T2 preparation pulses, and magnetic resonance fingerprinting, enable precise calculation of brain-tissue parameters and further advance diagnostic accuracy and efficiency. Potential DLR instabilities and quantification and bias limitations will be discussed. This review underscores the synergistic potential of DLR and quantitative MRI, offering prospects for improved brain imaging beyond conventional methods.

Abstract: Immunotherapy is likely the most remarkable advancement in lung cancer treatment during the past decade. Although immunotherapy provides substantial benefits, their therapeutic responses differ from those of conventional chemotherapy and targeted therapy, and some patients present unique immunotherapy response patterns that cannot be judged under the current measurement standards. Therefore, the response monitoring of immunotherapy can be challenging, such as the differentiation between real response and pseudo-response. This review outlines the various tumor response patterns to immunotherapy and discusses methods for quantifying computed tomography (CT) and 18 F-fluorodeoxyglucose positron emission tomography (PET) in the field of lung cancer. Emerging technologies in magnetic resonance imaging (MRI) and non-FDG PET tracers are also explored. With immunotherapy responses, the role for imaging is essential in both anatomical radiological responses (CT/MRI) and molecular changes (PET imaging). Multiple aspects must be considered when assessing treatment responses using CT and PET. Finally, we introduce multimodal approaches that integrate imaging and nonimaging data, and we discuss future directions for the assessment and prediction of lung cancer responses to immunotherapy.

Abstract: Interstitial lung disease (ILD) encompasses a variety of lung disorders with varying degrees of inflammation or fibrosis, requiring a combination of clinical, imaging, and pathologic data for evaluation. Imaging is essential for the noninvasive diagnosis of the disease, as well as for assessing disease severity, monitoring its progression, and evaluating treatment response. However, traditional visual assessments of ILD with computed tomography (CT) suffer from reader variability. Automated quantitative CT offers a more objective approach by using computer-based analysis to consistently evaluate and measure ILD. Advancements in technology have significantly improved the accuracy and reliability of these measurements. Recently, interstitial lung abnormalities (ILAs), which represent potential preclinical ILD incidentally found on CT scans and are characterized by abnormalities in over 5% of any lung zone, have gained attention and clinical importance. The challenge lies in the accurate and consistent identification of ILA, given that its definition relies on a subjective threshold, making quantitative tools crucial for precise ILA evaluation. This review highlights the state of CT quantification of ILD and ILA, addressing clinical and research disparities while emphasizing how machine learning or deep learning in quantitative imaging can improve diagnosis and management by providing more accurate assessments, and finally, suggests the future directions of quantitative CT in this area.

Abstract: Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.

Abstract: Congenital lymphatic flow disorders collectively refer to a heterogeneous group of diseases that manifest as chylothorax, chylous ascites, intestinal lymphangiectasia, protein-losing enteropathy, and peripheral extremity or genital lymphedema, all in the absence of identifiable injury to the lymphatic system. We have only recently begun to understand congenital lymphatic flow disorders through the ability to image lymph flow dynamically. Intranodal dynamic contrast-enhanced magnetic resonance lymphangiography (DCMRL) is a crucial technique for imaging lymphatic flow in pediatric patients with congenital lymphatic flow disorders. However, as lymphatic imaging is still a nascent discipline with many uncertainties regarding optimal imaging and treatment, effective patient management requires a comprehensive understanding of imaging techniques, disease pathophysiology, and multidisciplinary treatment approaches. Above all, a fundamental understanding of the physiological lymphatic flow of the central conducting lymphatics is essential for the correct interpretation of DCMRL images. This knowledge helps to avoid unnecessary examinations, erroneous diagnoses, and potentially harmful treatment approaches. This review provides an overview of the methods, advantages, and precautions for interpreting the DCMRL examination, a state-of-the-art lymphatic system imaging technique, and shares various case studies.

Objectives: Detecting premalignant lesions for pancreatic ductal adenocarcinoma, mainly pancreatic intraepithelial neoplasia (PanIN), is critical for early diagnosis and for understanding PanIN biology. Based on PanIN's histology, we hypothesized that diffusion tensor imaging (DTI) and T2* could detect PanIN.

Materials and methods: DTI was explored for the detection and characterization of PanIN in genetically engineered mice (KC, KPC). Following in vivo DTI, ex vivo ultrahigh-field (16.4 T) MR microscopy using DTI, T2* was performed with histological validation. Sources of MR contrasts and histological features were investigated, including histological scoring for disease burden (lesion span) and severity (adjusted score). To test if findings in mice can be translated to humans, human pancreas specimens were imaged.

Results: DTI detected PanIN and pancreatic ductal adenocarcinoma in vivo (6 KPC, 4 KC, 6 controls) with high discriminative ability: fractional anisotropy (FA) and radial diffusivity with area under the curve = 0.983 (95% confidence interval: 0.932-1.000); mean diffusivity and axial diffusivity (AD) with area under the curve = 1 (95% confidence interval: 1.000-1.000). MR microscopy with histological correlation (20 KC/KPC; 5 controls) revealed that sources of MR contrasts likely arise from microarchitectural signatures: high FA, AD in fibrotic areas surrounding lesions, high diffusivities within cysts, and high T2* within lesions' stroma. The strongest histological correlations for lesion span and adjusted score were obtained with AD (R = 0.708, P < 0.001; R = 0.789, P < 0.001, respectively). Ex vivo observations in 5 human pancreases matched our findings in mice, revealing substantial contrast between PanIN and normal pancreas.

Conclusions: DTI and T2* are useful for detecting and characterizing PanIN in genetically engineered mice and in the human pancreas, especially with AD and FA. These are encouraging findings for future clinical applications of pancreatic imaging.

Purpose: This study documents the gadolinium (Gd) content in urine over time after the administration of a single dose of Gd-based contrast agent (GBCA) in patients diagnosed with Gd deposition disease.

Materials and methods: In this retrospective observational study, 45 subjects with normal renal function who had performed 1 contrast-enhanced magnetic resonance imaging and had a nonprovoked (native) 24-hour urine test for Gd quantification after the examination were evaluated. The GBCA brand and the time interval in days between the GBCA administration and 24-hour urine Gd measurements were recorded. Log-log plot visualization of time points for urine Gd content was obtained.

Results: Time points collected for urine Gd content showed that Gd was above the reference levels for 3 months postinjection. The urinary concentration of Gd was similar for all agents, including linear and macrocyclic. The urinary content decreased in a dog-leg fashion. Gd urine content was substantially elevated at 1 month and decreased to remain above the accepted normal range by 3 months.

Conclusions: Gd is retained in the body and shows demonstrable continued spontaneous elimination in urine for at least several months after administration, including the most stable macrocyclic agents. The Gd elimination pattern shows a logarithmic decrease pattern between 1 and 3 months for all agents, regardless of their structure.

Objectives: Concern about contrast-induced acute kidney injury (CI-AKI) may delay the timely administration of contrast media for computed tomography (CT). The precise causative effect of iodinated contrast media on CI-AKI and its relevant risk factors remains an area of ongoing investigation. Therefore, this study aimed to determine the risk of CI-AKI following contrast-enhanced CT and its predisposing risk factors.

Materials and methods: This study employed a 1:1 propensity score matching analysis using electronic medical records gathered between January 2006 and December 2022 from 16 institutions in South Korea. Contrast-enhanced and nonenhanced CT scans in patients aged 18 years and above were matched for baseline estimated glomerular filtration rate (eGFR), demographic characteristics, and clinical variables to assess the risk of CI-AKI. Subgroup analyses were conducted to evaluate any significant risk factors for CI-AKI.

Results: A total of 182,170 CT scans with contrast were matched to 182,170 CT scans without contrast. The risk of CI-AKI in the entire study cohort was not statistically significant (odds ratio [OR], 1.036; 95% confidence interval [CI], 0.968-1.109; P = 0.34). Subgroup analyses revealed a significantly higher risk of CI-AKI in patients with eGFR <30 mL/min/1.73m 2 (OR, 1.176; 95% CI, 1.080-1.281; P = 0.011) or eGFR 30-45 mL/min/1.73m 2 (OR, 1.139; 95% CI, 1.043-1.244; P = 0.019), patients diagnosed with chronic kidney disease (OR, 1.215; 95% CI, 1.084-1.361; P = 0.011), and those administered with iso-osmolar contrast media (OR, 1.392; 95% CI, 1.196-1.622; P = 0.011).

Conclusions: The risk of CI-AKI following CT was minimal in the general population. However, caution is warranted for patients with chronic kidney disease and eGFR lower than 45 mL/min/1.73m 2 , or those administered with iso-osmolar contrast media.