Frontiers in radiology最新文献

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.

Background: Bilateral extended pelvic lymph node dissection (ePLNR) is used in high-risk prostate cancer for assessing metastatic involvement and lymph node staging. Nevertheless, in patients with localized or locally advanced prostate cancer, loco-regional lymph nodes are not always metastatic. Based on this assumption, the aim of this study is to evaluate the potential of ePLND performed under fluorescence guidance after administration of the Indocyanine green (ICG)-Lipiodol mixture via embolization of the prostate arteries in order to identify metastatic lymph nodes, that are then confirmed by histopathology analysis.

Materials and methods: All participants underwent selective embolization of the prostatic arteries 24-48 h before the scheduled surgery. The embolization procedure involved the injection of 25 mg/ml ICG, distilled water, and Lipiodol adequately mixed. During ePLND, the "Firefly" mode integrated into the Da Vinci robotic system was used to assess fluorescence in loco-regional lymph nodes. The lymph nodes were harvested and sent for histopathological examination. Intraoperative fluorescence results, histopathological findings, and short-term postoperative complications were recorded and classified according to the Clavien-Dindo system. For statistical analysis, the Phi coefficient was used to assess the correlation between categorical variables.

Results: Ten patients diagnosed with high-risk or unfavorable intermediate-risk PCa were included. All patients underwent radical robot assisted prostatectomy with ePLND within 48 h of prostate embolization using ICG-Lipiodol. Intraoperative fluorescence results, final histopathological findings and postoperative complications were recorded. The lymph nodes with positive fluorescence, after being analyzed separately, were confirmed to be as metastatic upon dedicated histopathological examination, while non-fluorescent lymph nodes were found to be negative for metastatic involvement. The phi coefficient was calculated to establish the degree of correlation between detection of green fluorescence by Firefly system and the positivity of lymph nodes for metastatic invasion at the histopathological analysis. The concordance assessed by phi correlation coefficient was 0.76, with a sensitivity of 100% (95% confidence interval).

Conclusion: Although preliminary, the results of this study demonstrate the potential of fluorescence-guided ePLND after ICG-Lipiodol administration for improving the identification of metastatic lymph nodes during Robotic-assisted radical prostatectomy RARP. Further studies are required to validate our findings with a larger group of patients.

Acute aortic intramural hematoma (IMH) is a relatively uncommon but potentially life-threatening aortic disease that can occur primarily in hypertensive and atherosclerotic patients. The course of IMH varies widely, with the condition either regressing, remaining stable, or progressing until it leads to outward rupture or intimal layer disruption, eventually resulting in overt aortic dissection. Therefore, poor prognostic computed tomography (CT) features must be promptly recognized and reported by the radiologist. In emergency departments, readily accessible non-invasive CT angiography is crucial for achieving a rapid and accurate diagnosis essential for appropriate management. For Type A and B aortic dissection, surgery is typically recommended in Western countries for patients with Stanford Type A IMH and those experiencing irrepressible pain. For Stanford Type B IMH patients without complications or incessant pain, medical treatment is suggested but with imaging follow-up. In complicated Stanford Type B situations, thoracic endovascular aortic repair (TEVAR) is currently indicated. This review aims to present pathophysiology, CT diagnosis, and IMH fate and provide the reader CT image-based review of the CT diagnostic criteria, complications, and associated critical prognostic findings of this rather rare aortic disease.

Degenerative disc disease (DDD) is a common spinal condition characterized by the deterioration of intervertebral discs, leading to chronic back pain and reduced mobility. While magnetic resonance imaging (MRI) has long been the standard for late-stage DDD diagnosis, its limitations in early-stage detection prompt the exploration of advanced imaging methods. Positron emission tomography/computed tomography (PET/CT) using ¹⁸F- fluorodeoxyglucose (FDG) and ¹⁸F-sodium fluoride (NaF) has shown promise in identifying metabolic imbalances and age-related spinal degeneration, thereby complementing CT grading of the disease. The novel hybrid imaging modality PET/MRI provides new opportunities and are briefly discussed. The complex pathophysiology of DDD is dissected to highlight the role of genetic predisposition and lifestyle factors such as smoking and obesity. These etiological factors significantly impact the lumbosacral region, manifesting in chronic low back pain (LBP) and potential nerve compression. Traditional grading systems, like the Pfirrmann classification for MRI, are evaluated for their limitations in capturing the full spectrum of DDD. The potential to identify early disease processes and predict patient outcomes by the use of artificial intelligence (AI) is also briefly mentioned. Overall, the manuscript aims to spotlight advancements in imaging technologies for DDD, emphasizing their implications in refining both diagnosis and treatment strategies. The role of ongoing and future research is emphasized to validate these emerging techniques and overcome current limitations for more effective early detection and treatment.

Nowadays, the genetic and biomolecular profile of neoplasms-related with their biological behaviour-have become a key issue in oncology, as they influence many aspects of both diagnosis and treatment. In the neuro-oncology field, neuroradiological research has recently explored the potential of non-invasively predicting the molecular phenotype of primary brain neoplasms, particularly gliomas, based on magnetic resonance imaging (MRI), using both conventional and advanced imaging techniques. Among these, diffusion-weighted imaging (DWI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS) and susceptibility-weighted imaging (SWI) and have been used to explore various aspects of glioma biology, including predicting treatment response and understanding treatment-related changes during follow-up imaging. Recently, intratumoral susceptibility signals (ITSSs)-visible on SWI-have been recognised as an important new imaging tool in the evaluation of brain gliomas, as they offer a fast and simple non-invasive window into their microenvironment. These intratumoral hypointensities reflect critical pathological features such as microhemorrhages, calcifications, necrosis and vascularization. Therefore, ITSSs can provide neuroradiologists with more biological information for glioma differential diagnosis, grading and subtype differentiation, providing significant clinical support in prognosis assessment, therapeutic management and treatment response evaluation. This review summarizes recent advances in ITSS applications in glioma assessment, emphasizing both its potential and limitations while referencing key studies in the field.