Although kidney transplantation is the best therapeutic option for patients with chronic kidney disease, the immunosuppression required greatly increases susceptibility to infections that are responsible for high post-transplant mortality. Pulmonary tuberculosis (TB) represents a major cause of such infections, and its early diagnosis is therefore quite important. In view of that, we researched the manifestations of active pulmonary TB in kidney transplant recipients, through chest X-ray and computed tomography (CT), as well as determining the number of cases of active pulmonary TB occurring over a 3.5-year period at our institution. We identified four cases of active pulmonary TB in kidney transplant recipients. The CT scans provided information complementary to the chest X-ray findings in all four of those cases. We compared our CT findings with those reported in the literature. We analyzed our experience in conjunction with an extensive review of the literature that was nevertheless limited because few studies have been carried out in lowand middle-income countries, where the incidence of TB is higher.
Apesar de o transplante renal ser a melhor opção terapêutica para pacientes com doença renal crônica, a imunodepressão decorrente desse tratamento eleva muito a suscetibilidade desses pacientes a infecções, responsáveis por altas taxas de mortalidade pós-operatórias. A tuberculose (TB) pulmonar é uma significativa causa dessas infecções, sendo muito importante o seu diagnóstico precoce. Assim, nós pesquisamos as manifestações da TB pulmonar ativa nessa população de transplantados renais por meio de radiografias simples e tomografia computadorizada (TC) do tórax, também para estabelecer o número de casos de TB pulmonar ativa em nossa instituição após levantamento de 3,5 anos. Encontramos quatro casos de TB pulmonar ativa em pacientes transplantados renais. A TC forneceu informações adicionais em relação às radiografias de tórax em 100% dos casos analisados. Comparamos os nossos achados de TC com os relatados na literatura. Somamos a experiência obtida com extensa revisão da literatura, ainda limitada nessa questão, com poucos estudos realizados em países em desenvolvimento onde a incidência de TB é maior.

OBJECTIVE: The accurate detection and precise segmentation of lung nodules on computed tomography are key prerequisites for early diagnosis and appropriate treatment of lung cancer. This study was designed to compare detection and segmentation methods for pulmonary nodules using deep-learning techniques to fill methodological gaps and biases in the existing literature.
METHODS: This study utilized a systematic review with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, searching PubMed, Embase, Web of Science Core Collection, and the Cochrane Library databases up to May 10, 2023. The Quality Assessment of Diagnostic Accuracy Studies 2 criteria was used to assess the risk of bias and was adjusted with the Checklist for Artificial Intelligence in Medical Imaging. The study analyzed and extracted model performance, data sources, and task-focus information.
RESULTS: After screening, we included nine studies meeting our inclusion criteria. These studies were published between 2019 and 2023 and predominantly used public datasets, with the Lung Image Database Consortium Image Collection and Image Database Resource Initiative and Lung Nodule Analysis 2016 being the most common. The studies focused on detection, segmentation, and other tasks, primarily utilizing Convolutional Neural Networks for model development. Performance evaluation covered multiple metrics, including sensitivity and the Dice coefficient.
CONCLUSIONS: This study highlights the potential power of deep learning in lung nodule detection and segmentation. It underscores the importance of standardized data processing, code and data sharing, the value of external test datasets, and the need to balance model complexity and efficiency in future research.
CONCLUSIONS: Deep learning demonstrates significant promise in autonomously detecting and segmenting pulmonary nodules. Future research should address methodological shortcomings and variability to enhance its clinical utility.
CONCLUSIONS: Deep learning shows potential in the detection and segmentation of pulmonary nodules. There are methodological gaps and biases present in the existing literature. Factors such as external validation and transparency affect the clinical application.

OBJECTIVE: To investigate dual-energy computed tomography\'s (DECT) diagnostic performance in detecting neurological complications following endovascular therapy (EVT) of acute ischaemic stroke (AIS).
METHODS: We performed the literature search using Web of Science, Scopus, PubMed, EBSCO, and Science Direct databases for published related studies. The selected studies estimated the validity of DECT in the detection of neurological complications after EVT for AIS. Study quality assessment was performed utilizing the Quality of Diagnostic Accuracy Studies-2 Tool. Our meta-analysis calculated the pooled sensitivity, negative likelihood ratio, specificity, and positive likelihood ratio for each detected complication. The summary receiver operating characteristics (sROC) curve was utilized to estimate the area under the curve (AUC).
RESULTS: Of 22 studies, 21 were included in the quantitative synthesis. In the detection of intracerebral haemorrhage (ICH), DECT pooled overall sensitivity and specificity were 69.9% (95% CI, 44.5%-86.8%) and 100% (95% CI, 92.1%-100%); whereas, in the detection of ischaemia, they were 85.9% (95% CI, 80.4%-90%) and 90.7% (95% CI, 87%-93.5%), respectively. On the sROC curve, AUC values of 0.954 and 0.952 were recorded for the detection of ICH and ischaemia, respectively.
CONCLUSIONS: DECT demonstrated high accuracy and specificity in the detection of neurological complications post-endovascular treatment of AIS. However, further prospective studies with a standardized reference test and a larger sample size are recommended to support these findings.
CONCLUSIONS: DECT is a rapid and valid imaging tool for the prediction of ICH and cerebral ischaemia after the EVT of AIS.

Drowning is a significant global cause of unintentional injury fatalities, and accurate forensic diagnosis of drowning remains a challenge due to the nonspecific nature of post-mortem findings obtained through classical autopsy methods. Our manuscript addresses this issue by focusing on the emerging use of paranasal sinus fluid as a valuable tool in determining the cause of death, specifically in distinguishing drowning from non-drowning cases. The study provided a comprehensive summary of available evidence from observational studies that compared findings in the paranasal sinuses between drowning and non-drowning victims, analyzing parameters such as the presence of fluid, fluid volume, and density. The study encompassed a total of 14 selected studies involving 1044 subjects and utilized rigorous risk of bias assessment and data synthesis techniques. The meta-analysis demonstrated a strong association between the presence of fluid in the paranasal sinuses and drowning (OR = 17.1; 95% CI 7.2 to 40.5; p < 0.001). In addition, drowning victims had a significantly greater volume of fluid (SMD = 0.8; 95% CI 0.5 to 1.2; p < 0.001) and lower fluid density (SMD = -1.4; 95% -2.5 to -0.4; p = 0.008) compared to non-drowning cases. The results support the utility of paranasal sinus fluid analysis as a valuable diagnostic method in cases where drowning is suspected but cannot be definitively confirmed through traditional approaches.

Investigators are increasingly measuring skeletal muscle (SM) and adipose tissue (AT) change during cancer treatment to understand impact on patient outcomes. Recent meta-analyses have reported high heterogeneity in this literature, representing uncertainty in the resulting estimates. Using the setting of palliative-intent chemotherapy as an exemplar, we aimed to systematically summarize the sources of variability among studies evaluating SM and AT change during cancer treatment and propose standards for future studies to enable reliable meta-analysis. Studies that measured computed tomography-defined SM and/or AT change in adult patients during palliative-intent chemotherapy for solid tumours were included, with no date or geographical limiters. Of 2496 publications screened by abstract/title, 83 were reviewed in full text and 38 included for extraction, representing 34 unique cohorts across 8 tumour sites. The timing of baseline measurement was frequently defined as prior to treatment, while endpoint timing ranged from 6 weeks after treatment start to time of progression. Fewer than 50% specified the actual time interval between measurements. Measurement error was infrequently discussed (8/34). A single metric (cm2 /m2 , cm2 or %) was used to describe SM change in 18/34 cohorts, while multiple metrics were presented for 10/34 and no descriptive metrics for 6/34. AT change metrics and sex-specific reporting were available for 10/34 cohorts. Associations between SM loss and overall survival were evaluated in 24 publications, with classification of SM loss ranging from any loss to >14% loss over variable time intervals. Age and sex were the most common covariates, with disease response in 50% of models. Despite a wealth of data and effort, heterogeneity in study design, reporting and statistical analysis hinders evidence synthesis regarding the severity and outcomes of SM and AT change during cancer treatment. Proposed standards for study design include selection of homogenous cohorts, clear definition of baseline/endpoint timing and attention to measurement error. Standard reporting should include baseline SM and AT by sex, actual scan interval, SM and AT change using multiple metrics and visualization of the range of change observed. Reporting by sex would advance understanding of sexual dimorphism in SM and AT change. Evaluating the impact of tissue change on outcomes requires adjustment for relevant covariates and concurrent disease response. Adoption of these standards by researchers and publishers would alter the current paradigm to enable meta-analysis of future studies and move the field towards meaningful application of SM and AT change to clinical care.

During the waves of the coronavirus disease (COVID-19) pandemic, emergency departments were overflowing with patients suffering with suspected medical or surgical issues. In these settings, healthcare staff should be able to deal with different medical and surgical scenarios while protecting themselves against the risk of contamination. Various strategies were used to overcome the most critical issues and guarantee quick and efficient diagnostic and therapeutic charts. The use of saliva and nasopharyngeal swab Nucleic Acid Amplification Tests (NAAT) in the diagnosis of COVID-19 was one of the most adopted worldwide. However, NAAT results were slow to report and could sometimes create significant delays in patient management, especially during pandemic peaks. On these bases, radiology has played and continues to play an essential role in detecting COVID-19 patients and solving differential diagnosis between different medical conditions. This systematic review aims to summarize the role of radiology in the management of COVID-19 patients admitted to emergency departments by using chest X-rays (CXR), computed tomography (CT), lung ultrasounds (LUS), and artificial intelligence (AI).

To reduce the number of missed or misdiagnosed lung nodules on CT scans by radiologists, many Artificial Intelligence (AI) algorithms have been developed. Some algorithms are currently being implemented in clinical practice, but the question is whether radiologists and patients really benefit from the use of these novel tools. This study aimed to review how AI assistance for lung nodule assessment on CT scans affects the performances of radiologists. We searched for studies that evaluated radiologists\' performances in the detection or malignancy prediction of lung nodules with and without AI assistance. Concerning detection, radiologists achieved with AI assistance a higher sensitivity and AUC, while the specificity was slightly lower. Concerning malignancy prediction, radiologists achieved with AI assistance generally a higher sensitivity, specificity and AUC. The radiologists\' workflows of using the AI assistance were often only described in limited detail in the papers. As recent studies showed improved performances of radiologists with AI assistance, AI assistance for lung nodule assessment holds great promise. To achieve added value of AI tools for lung nodule assessment in clinical practice, more research is required on the clinical validation of AI tools, impact on follow-up recommendations and ways of using AI tools.

Ureteral stone impaction is associated with unfavorable endourological outcomes; however, reliable predictors of stone impaction are limited. We aimed to assess the performance of ureteral wall thickness on noncontrast computed tomography as a predictor of ureteral stone impaction and failure rates of spontaneous stone passage, shock wave lithotripsy, and retrograde guidewire and stent passage.
This study was completed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A search was conducted in April 2022 for all adult, human, and English language studies investigating ureteral wall thickness using PROSPERO, OVID Medline, OVID EMBASE, Wiley Cochrane Library, Proquest Dissertations & Theses Global, and SCOPUS. A systematic review and meta-analysis using random effects model was conducted. Risk of bias was assessed using the MINORS (Methodological Index for Non-randomized Studies) score.
Fourteen studies with a pooled population of 2,987 patients were included for quantitative analysis, and 34 studies were included in our qualitative review. Meta-analysis findings suggest that a thinner ureteral wall thickness is associated with more favorable subgroup stone outcomes. Thinner ureteral wall thickness suggests a lack of stone impaction and was associated with improved rates of spontaneous stone passage, successful retrograde guidewire and stent placement, and improved shock wave lithotripsy outcomes. Studies lack a standardized ureteral wall thickness measurement protocol.
Ureteral wall thickness is a noninvasive measure that predicts ureteral stone impaction, and thin measurements are predictive of successful outcomes. Variability in measurement methods confirms that a standardized ureteral wall thickness protocol is needed, and the clinical utility of ureteral wall thickness is yet to be determined.

Gastrointestinal stromal tumors (GISTs) are rare mesenchymal neoplasms. Tyrosine kinase inhibitor (TKI) therapy is currently part of routine clinical practice for unresectable and metastatic disease. It is important to assess the efficacy of TKI treatment at an early stage to optimize therapy strategies and eliminate futile ineffective treatment, side effects and unnecessary costs. This systematic review provides an overview of the imaging features obtained from contrast-enhanced (CE)-CT and 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) PET/CT to predict and monitor TKI treatment response in GIST patients. PubMed, Web of Science, the Cochrane Library and Embase were systematically screened. Articles were considered eligible if quantitative outcome measures (area under the curve (AUC), correlations, sensitivity, specificity, accuracy) were used to evaluate the efficacy of imaging features for predicting and monitoring treatment response to various TKI treatments. The methodological quality of all articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies, v2 (QUADAS-2) tool and modified versions of the Radiomics Quality Score (RQS). A total of 90 articles were included, of which 66 articles used baseline [18F]FDG-PET and CE-CT imaging features for response prediction. Generally, the presence of heterogeneous enhancement on baseline CE-CT imaging was considered predictive for high-risk GISTs, related to underlying neovascularization and necrosis of the tumor. The remaining articles discussed therapy monitoring. Clinically established imaging features, including changes in tumor size and density, were considered unfavorable monitoring criteria, leading to under- and overestimation of response. Furthermore, changes in glucose metabolism, as reflected by [18F]FDG-PET imaging features, preceded changes in tumor size and were more strongly correlated with tumor response. Although CE-CT and [18F]FDG-PET can aid in the prediction and monitoring in GIST patients, further research on cost-effectiveness is recommended.

Computed tomography (CT) is commonly utilized in chronic obstructive pulmonary disease (COPD) for lung cancer screening and emphysema characterization. Computed tomography-morphometric analysis of body composition (muscle mass and adiposity) has gained increased recognition as a marker of disease severity and prognosis. This systematic review aimed to describe the CT-methodology used to assess body composition and identify the association of body composition measures and disease severity, health-related quality of life (HRQL), cardiometabolic risk factors, respiratory exacerbations, and survival in patients with COPD.
Six databases were searched (inception-September 2021) for studies evaluating adult COPD patients using thoracic or abdominal CT-muscle or adiposity body composition measures. The systematic review was conducted in accordance with the PRISMA guidelines.
Twenty eight articles were included with 15,431 COPD patients, across all GOLD stages with 77% males, age range (mean/median 59-78 years), and BMI range 19.8-29.3 kg/m2. There was heterogeneity in assessment of muscle mass and adiposity using thoracic (n = 22) and abdominal (n = 8) CT-scans, capturing different muscle groups, anatomic locations, and adiposity compartments (visceral, subcutaneous, and epicardial). Low muscle mass and increased adiposity were associated with increased COPD severity measures (lung function, exercise capacity, dyspnea) and lower HRQL, but were not consistent across studies. Increased visceral adiposity (n = 6) was associated with cardiovascular disease or risk factors (hypertension, hyperlipidemia, and diabetes). Low muscle CSA was prognostic of respiratory exacerbations or mortality in three of six studies, whereas the relationship with increased intermuscular adiposity and greater mortality was only observed in one of three studies.
There was significant variability in CT-body composition measures. In several studies, low muscle mass was associated with increased disease severity and lower HRQL, whereas adiposity with cardiovascular disease/risk factors. Given the heterogeneity in body composition measures and clinical outcomes, the prognostic utility of CT-body composition in COPD requires further study.