Frontiers in radiology最新文献

Idiopathic Intracranial hypertension (IIH), also referred to as pseudotumor cerebri, is a term used to describe increased intracranial pressure in the absence of a known identifiable secondary cause. Despite advancements of neuroimaging techniques, imaging of the pathological underpinnings in the diagnosis of IIH has been limited. Although the causation of IIH has been ascribed to increased Cerebrospinal Fluid production and disordered drainage through the dural sinuses, new evidence shows that the glymphatic system which is an alternate pathway of drainage is likely to play a pivotal role. In this review, we address the pathophysiological underpinnings in the causation of IIH and discusses characteristic anatomical imaging findings on conventional MRI and explore the role of advanced imaging techniques.

Virtual autopsy, an advanced forensic technique, utilizes cutting-edge imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) to investigate the cause and manner of death without the need for physical dissection. By creating detailed, three-dimensional data of the entire body or specific areas of interest, these post-mortem imaging modalities provide a comprehensive, non-invasive approach to examining decedents. This article explores the historical development of virtual autopsy, its current applications in forensic medicine, and its promising future. It highlights the crucial roles of CT and MRI in forensic death investigations, while also addressing the challenges and limitations associated with these imaging techniques in post-mortem examinations.

Objective: This study aimed to determine the diagnostic precision of a deep learning algorithm for the classificaiton of non-contrast brain CT reports.

Methods: A total of 1,861 non-contrast brain CT reports were randomly selected, anonymized, and annotated for urgency level by two radiologists, with review by a senior radiologist. The data, encrypted and stored in Excel format, were securely maintained on a university cloud system. Using Python 3.8.16, the reports were classified into four urgency categories: emergency, not emergency but needs timely attention, clinically non-significant and normal. The dataset was split, with 800 reports used for training and 200 for validation. The DistilBERT model, featuring six transformer layers and 66 million trainable parameters, was employed for text classification. Training utilized the Adam optimizer with a learning rate of 2e-5, a batch size of 32, and a dropout rate of 0.1 to prevent overfitting. The model achieved a mean F1 score of 0.85 through 5-fold cross-validation, demonstrating strong performance in categorizing radiology reports.

Results: Of the 1,861 scans, 861 cases were identified as fit for study through the senior radiologist and self-hosted Label Studio interpretations. It was observed that the algorithm achieved a sensitivity of 91% and a specificity of 90% in the measurements made on the test data. The F1 score was measured as 0.89 for the best fold. The algorithm most successfully distinguished emergency results with positive predictive values that were unexpectedly lower than in previously reported studies. Beam hardening artifacts and excessive noise, compromising the quality of CT scan images, were significantly associated with decreased model performance.

Conclusion: The proposed deep learning algorithm demonstrated high diagnostic accuracy, sensitivity, and specificity in classifying non-contrast brain CT reports. These results indicate the feasibility of automated identification of critical cases, which may support workflow efficiency and timely patient management in radiology practice.

Background and objective: COVID-19 has emerged as a global pandemic affecting individuals of all ages. The disease can lead to severe complications and even death, particularly due to pulmonary involvement. Contrary to popular belief, children can also experience significant complications from COVID-19. To date, there have been limited studies focusing on pulmonary manifestations in pediatric patients with COVID-19. This study aims to investigate the imaging patterns (CT scans) in children diagnosed with COVID-19 in Iran.

Materials and methods: This retrospective study analyzed data from hospitalized children with COVID-19 in Tehran from March 2020 to September 2020. Information collected included demographic details (sex and age), previous medical history, clinical manifestations, vital signs at admission, laboratory findings, and imaging results, including CT scan and chest x-ray.

Results: 252 patients were included, with a mean age of 71.2 ± 59.42 months; 58.3% were male. Fever was the most prevalent symptom, occurring in 67.4% of cases. The most common underlying condition was oncological disorders, present in 85% of patients. Notably, 52% required admission to the ICU, and 1.8% needed intubation. CT scans revealed that the most frequent lung involvement patterns were mixed patterns and consolidation, with bilateral involvement being the most common. The mean CT score was calculated at 3 ± 4. Abnormal CT findings were associated with a poorer prognosis, and correlations were observed between specific CT findings and clinical manifestations.

Conclusion: Chest CT manifestations offer valuable insights for assessing pediatric patients with COVID-19, especially in severe cases and those with pre-existing health conditions. Integrating clinical evaluations with radiological scoring systems facilitates early identification of disease severity.

Autopsy is generally regarded as the gold standard for cause of death determination, the most accurate contributor to mortality data. Despite this, autopsy rates have substantially declined, and death certificates are more frequently completed by clinicians. Substantial discrepancies between clinician-presumed and autopsy-determined cause of death impact quality control in hospitals, accuracy of mortality data, and, subsequently, the applicability and effectiveness of public health efforts. This problem is compounded by wavering support for the practice of autopsy by accrediting bodies and academic bodies governing pathology specialty training. In forensic settings, critical workforce shortages combined with increased workloads further threaten sustainability of the practice. Postmortem imaging (PMI) can help mitigate these ongoing problems. Postmortem computed tomography can help clarify manner and cause of death in a variety of situations and has undeniable advantages, including cost reduction, the potential to review data, expedient reporting, archived unaltered enduring evidence (available for expert opinion, further review, demonstrative aids, and education), and (when feasible) adherence to cultural and religious objections to autopsy. Integration of radiology and pathology is driving a transformative shift in medicolegal death investigations, enabling innovative approaches that enhance diagnostic accuracy, expedite results, and improve public health outcomes. This synergy addresses declining autopsy rates, the forensic pathologist shortage, and the need for efficient diagnostic tools. By combining advanced imaging techniques with traditional pathology, this collaboration elevates the quality of examinations and advances public health, vital statistics, and compassionate care, positioning radiology and pathology as pivotal partners in shaping the future of death investigations.

Background: Radiation necrosis is a significant late adverse effect of stereotactic radiotherapy (fSRT) for brain metastases, characterized by inflammatory processes and necrotic degeneration of healthy brain tissue.

Objective: To evaluate the relationship between the incidence of radiation necrosis and the distribution of lesions across different brain regions treated with fSRT, with a focus on the potential involvement of stem cell niches.

Methods: We conducted a post-hoc analysis of two separate prospective datasets consisting of data from 41 patients previously treated for brain metastases at Campus Bio-Medico University Hospital. Patients underwent fSRT using volumetric-modulated arc radiotherapy (VMAT), with MRI data collected pre- and post-treatment. Lesions were assessed for the presence of radiation necrosis based on radiological and clinical criteria, with a specific focus on their proximity to stem cell niches. A mixed-effects logistic regression model, including age and sex as covariates, was used to identify associations between brain region, stem cell niches, and the likelihood of radiation necrosis.

Results: Of 167 lesions observed, 42 (25.1%) were classified as radiation necrosis. The Deep-Periventricular region showed a significantly higher likelihood of radiation necrosis compared to other brain regions (log-OR: 1.25, 95% CI: 0.20-2.30, p = 0.02). Lesions in proximity to stem cell niches were significantly associated with an increased risk of radiation necrosis (log-OR: 1.61, 95% CI: 0.27-2.94, p = 0.018). These findings highlight the differential vulnerability of brain regions and suggest a potential role of stem cell niches in the pathogenesis of radiation necrosis.

Conclusion: This study underscores the importance of brain region and stem cell niche involvement in the development of radiation necrosis following stereotactic radiotherapy. These findings might have implications for optimizing radiotherapy planning and developing targeted strategies to mitigate the risk of radiation necrosis. Future research should focus on exploring the molecular mechanisms underlying these associations and evaluating potential neuroprotective interventions.

Background and aim: Lewy body diseases (LBD) include neurodegenerative diseases such as Parkinson's disease (PD), dementia with Lewy bodies (DLB), and Parkinson's disease dementia (PDD). Because DLB and Alzheimer's disease (AD) share similar neurological symptoms, DLB is frequently underdiagnosed. White Matter Hyperintensities (WMH) are associated with dementia risk and changes in both DLB and AD. In order to examine WMH discrepancies in DLB and AD patients and gain insight into their diagnostic utility and pathophysiological significance, this systematic review and meta-analysis is conducted.

Material and methods: Databases such as PubMed, Scopus, Google Scholar, and Web of Science were searched for studies reporting WMH in DLB and AD patients based on Preferred Reporting Items for Systematic Review (PRISMA) guideline. Stata version 15 US is used to analyze the extracted data.

Results: Twelve studies with 906 AD and 499 DLB patients were considered in this analysis. Although not statistically significant, the WMH was 0.03 ml larger in AD patients than in DLB patients. The prevalence of hypertension varied, ranging from 21% to 56% in DLB patients and from 30% to 52% in AD patients. Different findings were found on the prevalence of diabetes; some research suggested that DLB patients had greater rates (18.7%-37%) than AD patients (9%-17.5%). The imaging modalities FLAIR, T2-weighted, and T1-weighted sequences were employed. Compared to DLB patients, AD patients had higher cortical and infratentorial infarcts.

Conclusion: Those with AD have greater WMH volumes than cases with DLB, suggesting that WMH can be a biomarker to help better differentiation between these neurodegenerative diseases; however, this difference is not significant. To better understand the therapeutic implications and options for reducing WMH-related cognitive loss in various patient populations, more research is necessary.

Introduction: Lumbar spine magnetic resonance imaging (MRI) plays a critical role in diagnosing and planning treatment for spinal conditions such as degenerative disc disease, spinal canal stenosis, and disc herniation. Measuring the cross-sectional area of the dural sac (DSCA) is a key factor in evaluating the severity of spinal canal narrowing. Traditionally, radiologists perform this measurement manually, which is both time-consuming and susceptible to errors. Advances in deep learning, particularly convolutional neural networks (CNNs) like the U-Net architecture, have demonstrated significant potential in the analysis of medical images. This study evaluates the efficacy of deep learning models for automating DSCA measurements in lumbar spine MRIs to enhance diagnostic precision and alleviate the workload of radiologists.

Methods: For algorithm development and assessment, we utilized two extensive, anonymized online datasets: the "Lumbar Spine MRI Dataset" and the SPIDER-MRI dataset. The combined dataset comprised 683 lumbar spine MRI scans for training and testing, with an additional 50 scans reserved for external validation. We implemented and assessed three deep learning models-U-Net, Attention U-Net, and MultiResUNet-using 5-fold cross-validation. The models were trained on T1-weighted axial MRI images and evaluated on metrics such as accuracy, precision, recall, F1-score, and mean absolute error (MAE).

Results: All models exhibited a high correlation between predicted and actual DSCA values. The MultiResUNet model achieved superior results, with a Pearson correlation coefficient of 0.9917 and an MAE of 23.7032 mm² on the primary dataset. This high precision and reliability were consistent in external validation, where the MultiResUNet model attained an accuracy of 99.95%, a recall of 0.9989, and an F1-score of 0.9393. Bland-Altman analysis revealed that most discrepancies between predicted and actual DSCA values fell within the limits of agreement, further affirming the robustness of these models.

Discussion: This study demonstrates that deep learning models, particularly MultiResUNet, offer high accuracy and reliability in the automated segmentation and calculation of DSCA in lumbar spine MRIs. These models hold significant potential for improving diagnostic accuracy and reducing the workload of radiologists. Despite some limitations, such as the restricted dataset size and reliance on T1-weighted images, this study provides valuable insights into the application of deep learning in medical imaging. Future research should include larger, more diverse datasets and additional image weightings to further validate and enhance the generalizability and clinical utility of these models.

Percutaneous ablation therapies currently play a major role in the management of T1a and T1b renal cell carcinoma (RCC). These therapies include thermal ablative technologies like radiofrequency (RFA), microwave (MWA) and cryoablation, as well as emerging techniques like irreversible electroporation (IRE) and high-intensity focused ultrasound (HIFU). These therapies are safe and effective, with their low complication rate being mostly related to the minimal invasive character. To increase the outcomes and safety of ablation, particularly in the setting of larger tumors, adjunctive techniques may be useful. These include pre-ablation trans-arterial embolization (TAE) and thermal protective measures. TAE is an endovascular procedure consisting of vascular access, catheterization and embolization of renal vessels supplying target tumor, with different embolic materials available. The purpose of combining TAE and ablation is manifold: to reduce vascularization and improve local tumor control, to reduce complications (including the risk of bleeding), to enhance tumor visibility and localization, as well as to improve cost-efficiency of the procedure. Thermal protective strategies are important to minimize damage to adjacent structures, requiring accurate knowledge of anatomy and proper patient positioning. In RCC ablation, strategies are needed to protect the adjacent nerves, as well as the visceral and muscular organs. These include placement of thermocouples, hydro- or gas-dissection, balloon interposition, pyeloperfusion and skin protection maneuvers. The purpose of this review article is to discuss the updated role of ablation in RCC management, to describe the status of adjunctive techniques for RCC ablation; in addition it will offer a review of the literature on adjunctive techniques for RCC ablation. and report upon future directions.