Predictors and pathways of in-hospital mortality in active vascular contrast extravasation detected on abdominopelvic CT.

OBJECTIVES: This study aimed to identify factors influencing in-hospital mortality in adult patients with active vascular contrast extravasation (AVCE) on abdominopelvic computed tomography (CT). METHODS: All consecutive patients with AVCE detected on CT between January 2019 and May 2022 were retrospectively included. Their data were compared through uni- and multivariable analyses between patients with and without in-hospital mortality. Path analysis was utilized to clarify the relationships among factors affecting mortality. RESULTS: There were 272 patients (60.2 ± 19.4 years, 150 men) included, of whom 70 experienced in-hospital mortality. Multivariable analysis revealed nonsurgery, chronic kidney disease (CKD) stage 4-5 or dialysis, prolonged partial thromboplastin time (PTT), minimum AVCE length > 8 mm, and a lower rate of packed red cell (PRC) transfusion were identified as independent predictors of in-hospital mortality (p = 0.005-0.048). Path analysis demonstrated direct influences of CKD4-5 or dialysis, prolonged PTT, and minimum AVCE length on mortality (coefficients 0.525-0.616; p = 0.009 to < 0.001). PRC transfusion impacted mortality through nonsurgery (coefficient 0.798, p = 0.003) and intensive care unit (ICU) admission (coefficients 0.025, p = 0.016), leading to subsequent death. Three AVCE spaces (free, loose, and tight) defined on CT were not directly associated with in-hospital mortality. CONCLUSION: In adults with AVCE on CT, AVCE size had a direct independent influence on mortality, highlighting the critical role of radiologists in detecting and characterizing this finding. Additionally, CKD4-5 or dialysis and prolonged PTT also directly influenced mortality, while the lower rate of PRC transfusion impacted mortality through nonsurgery and ICU admission. CLINICAL RELEVANCE STATEMENT: In patients with active vascular contrast extravasation (AVCE) on abdominopelvic CT, larger AVCE directly increased in-hospital mortality. Radiologists' detection and characterization of this finding is crucial, along with recognizing factors like CKD4-5, dialysis, and prolonged PTT to improve patient outcomes. KEY POINTS: Several factors independently predicted in-hospital mortality in patients with abdominopelvic AVCE. Extravasation length > 8 mm was the only imaging marker predictive of in-hospital mortality. Non-imaging factors correlated with in-hospital mortality, and PRC transfusion impacted mortality through nonsurgery and ICU admission pathways.

Reduced dose helical CT scout imaging on next generation wide volume CT system decreases scan length and overall radiation exposure.

Purpose: Traditional CT acquisition planning is based on scout projection images from planar anterior-posterior and lateral projections where the radiographer estimates organ locations. Alternatively, a new scout method utilizing ultra-low dose helical CT (3D Landmark Scan) offers cross-sectional imaging to identify anatomic structures in conjunction with artificial intelligence based Anatomic Landmark Detection (ALD) for automatic CT acquisition planning. The purpose of this study is to quantify changes in scan length and radiation dose of CT examinations planned using 3D Landmark Scan and ALD and performed on next generation wide volume CT versus examinations planned using traditional scout methods. We additionally aim to quantify changes in radiation dose reduction of scans planned with 3D Landmark Scan and performed on next generation wide volume CT. Methods: Single-center retrospective analysis of consecutive patients with prior CT scan of the same organ who underwent clinical CT using 3D Landmark Scan and automatic scan planning. Acquisition length and dose-length-product (DLP) were collected. Data was analyzed by paired t-tests. Results: 104 total CT examinations (48.1 % chest, 15.4 % abdomen, 36.5 % chest/abdomen/pelvis) on 61 individual consecutive patients at a single center were retrospectively analyzed. 79.8 % of scans using 3D Landmark Scan had reduction in acquisition length compared to the respective prior acquisition. Median acquisition length using 3D Landmark Scan was 26.7 mm shorter than that using traditional scout methods (p < 0.001) with a 23.3 % median total radiation dose reduction (245.6 (IQR 150.0-400.8) mGy cm vs 320.3 (IQR 184.1-547.9) mGy cm). CT dose index similarly was overall decreased for scans planned with 3D Landmark and ALD and performed on next generation CT versus traditional methods (4.85 (IQR 3.8-7) mGy vs. 6.70 (IQR 4.43-9.18) mGy, respectively, p < 0.001). Conclusion: Scout imaging using reduced dose 3D Landmark Scan images and Anatomic Landmark Detection reduces acquisition range in chest, abdomen, and chest/abdomen/pelvis CT scans. This technology, in combination with next generation wide volume CT reduces total radiation dose.

Combined conventional factors and the radiomics signature of coronary plaque texture could improve cardiac risk prediction.

OBJECTIVES: This study aims to investigate how radiomics analysis can help understand the association between plaque texture, epicardial adipose tissue (EAT), and cardiovascular risk. Working with a Photon-counting CT, which exhibits enhanced feature stability, offers the potential to advance radiomics analysis and enable its integration into clinical routines. METHODS: Coronary plaques were manually segmented in this retrospective, single-centre study and radiomic features were extracted using pyradiomics. The study population was divided into groups according to the presence of high-risk plaques (HRP), plaques with at least 50% stenosis, plaques with at least 70% stenosis, or triple-vessel disease. A combined group with patients exhibiting at least one of these risk factors was formed. Random forest feature selection identified differentiating features for the groups. EAT thickness and density were measured and compared with feature selection results. RESULTS: A total number of 306 plaques from 61 patients (mean age 61 years +/- 8.85 [standard deviation], 13 female) were analysed. Plaques of patients with HRP features or relevant stenosis demonstrated a higher presence of texture heterogeneity through various radiomics features compared to patients with only an intermediate stenosis degree. While EAT thickness did not significantly differ, affected patients showed significantly higher mean densities in the 50%, HRP, and combined groups, and insignificantly higher densities in the 70% and triple-vessel groups. CONCLUSION: The combination of a higher EAT density and a more heterogeneous plaque texture might offer an additional tool in identifying patients with an elevated risk of cardiovascular events. CLINICAL RELEVANCE STATEMENT: Cardiovascular disease is the leading cause of mortality globally. Plaque composition and changes in the EAT are connected to cardiac risk. A better understanding of the interrelation of these risk indicators can lead to improved cardiac risk prediction. KEY POINTS: Cardiac plaque composition and changes in the EAT are connected to cardiac risk. Higher EAT density and more heterogeneous plaque texture are related to traditional risk indicators. Radiomics texture analysis conducted on PCCT scans can help identify patients with elevated cardiac risk.

Computed tomography and magnetic resonance imaging in Brazil: an epidemiological study on the distribution of equipment and frequency of examinations, with comparisons between the public and private sectors.

Objective: To compare information on highly complex radiological procedures-computed tomography (CT) and magnetic resonance imaging (MRI)-between the public and private health care systems, across the five regions of Brazil, in terms of the numbers of radiological devices and examinations performed, between 2015 and 2021. Materials and Methods: This was a descriptive time series analysis of secondary data in the public domain, available from the Information Technology Department of the Brazilian Unified Health Care System, an entity of the Brazilian National Ministry of Health (NMH) that is responsible for collecting and storing health-related information in Brazil. The analysis included the numbers of CT and MRI scanners; the volumes and types of examinations; the type of institution (public or private); the regions of the country; and the years (2015 to 2021). Results: Progressive increases in the numbers of CT and MRI devices, as well as in the volumes of examinations, were observed over the years in all regions of the country. The private sector showed higher rates of equipment acquisition and of growth in the number of examinations. However, the public health care system did not reach the equipment targets set by the NMH, whereas the private health care system surpassed those targets. A greater number of examinations were performed in the private sector than in the public sector. Conclusion: During the period evaluated, the public health care system did not meet the equipment or examination targets recommended by the NMH, in any of the regions of the country, unlike the private health care system, which exceeded both in all of the regions.

Objetivo: Comparar informações sobre procedimentos radiológicos de alta complexidade ­ tomografia computadorizada (TC) e ressonância magnética (RM) ­, considerando o número de aparelhos e o quantitativo de exames nas esferas pública e privada nas cinco regiões brasileiras entre 2015 e 2021. Materiais e Métodos: Trata-se de um estudo descritivo de série temporal que utilizou dados secundários do Departamento de Informática do Sistema Único de Saúde, órgão do Ministério da Saúde (MS) responsável pela coleta e armazenamento das informações relacionadas à saúde no Brasil. Analisamos os números de aparelhos e de exames de TC e RM, considerando os tipos de aparelhos e exames, instituição (pública ou privada), região brasileira e ano (2015 a 2021). Resultados: Houve aumento de aparelhos e exames de TC e RM em todas as regiões ao longo dos anos. A esfera privada apresentou maior aquisição desses aparelhos e crescimento no número de exames. O sistema público não atingiu o número de aparelhos preconizado pelo MS, enquanto o sistema privado superou a recomendação. Observou-se maior número de exames na esfera privada quando comparada à pública. Conclusão: O sistema público não atendeu aos números de aparelhos e exames realizados preconizados pelo MS, diferentemente da esfera privada, em todas as regiões no período estudado.

Deep learning in pulmonary nodule detection and segmentation: a systematic review.

OBJECTIVES: 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. CLINICAL RELEVANCE STATEMENT: 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. KEY POINTS: 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.

Virtual noncontrast images reveal gouty tophi in contrast-enhanced dual-energy CT: a phantom study.

BACKGROUND: Dual-energy computed tomography (DECT) is useful for detecting gouty tophi. While iodinated contrast media (ICM) might enhance the detection of monosodium urate crystals (MSU), higher iodine concentrations hamper their detection. Calculating virtual noncontrast (VNC) images might improve the detection of enhancing tophi. The aim of this study was to evaluate MSU detection with VNC images from DECT acquisitions in phantoms, compared against the results with standard DECT reconstructions. METHODS: A grid-like and a biophantom with 25 suspensions containing different concentrations of ICM (0 to 2%) and MSU (0 to 50%) were scanned with sequential single-source DECT using an ascending order of tube current time product at 80 kVp (16.5-220 mAs) and 135 kVp (2.75-19.25 mAs). VNC images were equivalently reconstructed at 80 and 135 kVp. Two-material decomposition analysis for MSU detection was applied for the VNC and conventional CT images. MSU detection and attenuation values were compared in both modalities. RESULTS: For 0, 0.25, 0.5, 1, and 2% ICM, the average detection indices (DIs) for all MSU concentrations (35-50%) with VNC postprocessing were respectively 25.2, 36.6, 30.9, 38.9, and 45.8% for the grid phantom scans and 11.7, 9.4, 5.5, 24.0, and 25.0% for the porcine phantom scans. In the conventional CT image group, the average DIs were respectively 35.4, 54.3, 45.4, 1.0, and 0.0% for the grid phantom and 19.4, 17.9, 3.0, 0.0, and 0.0% for the porcine phantom scans. CONCLUSIONS: VNC effectively reduces the suppression of information caused by high concentrations of ICM, thereby improving the detection of MSU. RELEVANCE STATEMENT: Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition. KEY POINTS: • Highly concentrated contrast media hinders monosodium urate crystal detection in CT imaging • Virtual noncontrast imaging redetects monosodium urate crystals in high-iodinated contrast media concentrations. • Contrast-enhanced DECT alone may suffice for diagnosing gout without a native acquisition.

Pulmonary Artery Periadventitial Hematoma in a Patient with Aortic Intramural Hematoma: A Case Report.

A pulmonary artery periadventitial hematoma is a rare complication of a Stanford type A intramural hematoma. As the proximal ascending aorta and pulmonary artery share a common adventitial layer, extravasated blood from the intramural hematoma in the ascending thoracic aorta may extend to beneath the adventitia of the pulmonary artery. The authors describe a case involving a 66-year-old male with acute chest pain who presented with a pulmonary artery periadventitial hematoma associated with a Stanford type A intramural hematoma.

Association between higher intermuscular adipose tissue and decreased renal function in patients with systemic lupus erythematosus mediated by insulin resistance.

OBJECTIVES: To quantify the relationship between abdominal computed tomography (CT)-based body composition parameters and renal function in systemic lupus erythematosus (SLE) patients and evaluate the potential effect of insulin resistance on this relationship. METHODS: SLE patients from institutions A and B between January 2017 and August 2023 were enrolled. Areas and attenuation values of subcutaneous adipose tissue, visceral adipose tissue, intermuscular adipose tissue (IMAT), and skeletal muscle index on CT images were measured at the L3 vertebral level. Logistic regression analysis was used to identify risk factors associated with decreased renal function. Linear regression models were used to describe the relationships between body composition parameters and estimated glomerular filtration rate (eGFR). Finally, we used a single-point insulin sensitivity estimator to indirectly reflect the degree of insulin resistance and assess its mediating effect on the association between IMAT area and decreased renal function. RESULTS: Three-hundred thirty-nine SLE patients from institution A (internal dataset) and 114 SLE patients from institution B (external validation dataset) were included. Multivariate logistic regression revealed that IMAT area (odds ratio (OR)institution A: 1.05 (95% confidence intervals (95% CI): 1.01, 1.10), and ORinstitution B: 1.19 (95% CI: 1.03, 1.39)) was an independent risk factor for decreased renal function in SLE patients. In the adjusted linear regression model, high IMAT area was significantly associated with reduced eGFR (ßinstitution A = -1.15, Pinstitution A = 0.005; ßinstitution B = -0.98, Pinstitution B = 0.049). Additionally, insulin resistance contributed a mediating role of 22.8% to the association. CONCLUSION: High IMAT area was associated with decreased renal function in SLE patients and insulin resistance mediated this relationship. CRITICAL RELEVANCE STATEMENT: High intermuscular adipose tissue area is associated with decreased renal function in systemic lupus erythematosus patients mediated by insulin resistance and is correlated with chronicity index in lupus nephritis patients. KEY POINTS: High intramuscular adipose tissue (IMAT) area was associated with decreased renal function in systemic lupus erythematosus (SLE) patients. Insulin resistance mediated the association between IMAT area and eGFR. IMAT area was associated with chronicity index in lupus nephritis patients.

CT and MRI radiomic features of lung cancer (NSCLC): comparison and software consistency.

BACKGROUND: Radiomics is a quantitative approach that allows the extraction of mineable data from medical images. Despite the growing clinical interest, radiomics studies are affected by variability stemming from analysis choices. We aimed to investigate the agreement between two open-source radiomics software for both contrast-enhanced computed tomography (CT) and contrast-enhanced magnetic resonance imaging (MRI) of lung cancers and to preliminarily evaluate the existence of radiomic features stable for both techniques. METHODS: Contrast-enhanced CT and MRI images of 35 patients affected with non-small cell lung cancer (NSCLC) were manually segmented and preprocessed using three different methods. Sixty-six Image Biomarker Standardisation Initiative-compliant features common to the considered platforms, PyRadiomics and LIFEx, were extracted. The correlation among features with the same mathematical definition was analyzed by comparing PyRadiomics and LIFEx (at fixed imaging technique), and MRI with CT results (for the same software). RESULTS: When assessing the agreement between LIFEx and PyRadiomics across the considered resampling, the maximum statistically significant correlations were observed to be 94% for CT features and 95% for MRI ones. When examining the correlation between features extracted from contrast-enhanced CT and MRI using the same software, higher significant correspondences were identified in 11% of features for both software. CONCLUSIONS: Considering NSCLC, (i) for both imaging techniques, LIFEx and PyRadiomics agreed on average for 90% of features, with MRI being more affected by resampling and (ii) CT and MRI contained mostly non-redundant information, but there are shape features and, more importantly, texture features that can be singled out by both techniques. RELEVANCE STATEMENT: Identifying and selecting features that are stable cross-modalities may be one of the strategies to pave the way for radiomics clinical translation. KEY POINTS: • More than 90% of LIFEx and PyRadiomics features contain the same information. • Ten percent of features (shape, texture) are stable among contrast-enhanced CT and MRI. • Software compliance and cross-modalities stability features are impacted by the resampling method.

Impact of reconstruction parameters on the accuracy of myocardial extracellular volume quantification on a first-generation, photon-counting detector CT.

BACKGROUND: The potential role of cardiac computed tomography (CT) has increasingly been demonstrated for the assessment of diffuse myocardial fibrosis through the quantification of extracellular volume (ECV). Photon-counting detector (PCD)-CT technology may deliver more accurate ECV quantification compared to energy-integrating detector CT. We evaluated the impact of reconstruction settings on the accuracy of ECV quantification using PCD-CT, with magnetic resonance imaging (MRI)-based ECV as reference. METHODS: In this post hoc analysis, 27 patients (aged 53.1 ± 17.2 years (mean ± standard deviation); 14 women) underwent same-day cardiac PCD-CT and MRI. Late iodine CT scans were reconstructed with different quantum iterative reconstruction levels (QIR 1-4), slice thicknesses (0.4-8 mm), and virtual monoenergetic imaging levels (VMI, 40-90 keV); ECV was quantified for each reconstruction setting. Repeated measures ANOVA and t-test for pairwise comparisons, Bland-Altman plots, and Lin's concordance correlation coefficient (CCC) were used. RESULTS: ECV values did not differ significantly among QIR levels (p = 1.000). A significant difference was observed throughout different slice thicknesses, with 0.4 mm yielding the highest agreement with MRI-based ECV (CCC = 0.944); 45-keV VMI reconstructions showed the lowest mean bias (0.6, 95% confidence interval 0.1-1.4) compared to MRI. Using the most optimal reconstruction settings (QIR4. slice thickness 0.4 mm, VMI 45 keV), a 63% reduction in mean bias and a 6% increase in concordance with MRI-based ECV were achieved compared to standard settings (QIR3, slice thickness 1.5 mm; VMI 65 keV). CONCLUSIONS: The selection of appropriate reconstruction parameters improved the agreement between PCD-CT and MRI-based ECV. RELEVANCE STATEMENT: Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility. KEY POINTS: • CT is increasingly promising for myocardial tissue characterization, assessing focal and diffuse fibrosis via late iodine enhancement and ECV quantification, respectively. • PCD-CT offers superior performance over conventional CT, potentially improving ECV quantification and its agreement with MRI-based ECV. • Tailoring PCD-CT reconstruction parameters optimizes ECV quantification compared to MRI, potentially improving its clinical utility.

Utility of FDG PET-CT in CT Stage IA non-small cell lung cancer: The New Zealand Te Whatu Ora Northern region experience.

INTRODUCTION: Our objective was to investigate the utility of fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET-CT) in assessing CT Stage 1A non-small cell lung cancer (NSCLC) in patients under consideration for curative treatment. Performing FDG PET-CT in these patients may lead to unnecessary delays in treatment if it can be shown to provide no added value. METHODS: We retrospectively reviewed 735 lesions in 653 patients from the New Zealand Te Whatu Ora Northern region lung cancer database with suspected or pathologically proven Stage 1A NSCLC on CT scan who also underwent FDG PET-CT imaging. We determined how often FDG PET-CT findings upstaged patients and then compared to pathological staging where available. RESULTS: FDG PET-CT provided an overall upstaging rate of 9.7%. Category-specific rates were 0% in Tis, 0.9% in T1mi, 7.4% in T1a, 10% in T1b and 12% in T1c groups. The percentage of lesions upstaged on FDG PET-CT that remained Stage 1A was 100% in T1mi, 100% in T1a, 47.1% in T1b and 40.7% in T1c groups. The P value was statistically significant at 0.004, indicating upstaging beyond Stage 1A was dependent on T category. CONCLUSION: Our data suggests that FDG PET-CT is indicated for T1b and T1c lesions but is of limited utility in Tis, T1mi and T1a lesions. Adopting a more targeted approach and omitting FDG PET-CT in patients with Tis, T1mi, and T1a lesions may benefit all patients with lung cancer by improving accessibility and treatment timelines.

Predicting lymphovascular invasion in non-small cell lung cancer using deep convolutional neural networks on preoperative chest CT.

RATIONALE AND OBJECTIVES: Lymphovascular invasion (LVI) plays a significant role in precise treatments of non-small cell lung cancer (NSCLC). This study aims to build a non-invasive LVI prediction diagnosis model by combining preoperative CT images with deep learning technology. MATERIALS AND METHODS: This retrospective observational study included a series of consecutive patients who underwent surgical resection for non-small cell lung cancer (NSCLC) and received pathologically confirmed diagnoses. The cohort was randomly divided into a training group comprising 70 % of the patients and a validation group comprising the remaining 30 %. Four distinct deep convolutional neural network (DCNN) prediction models were developed, incorporating different combination of two-dimensional (2D) and three-dimensional (3D) CT imaging features as well as clinical-radiological data. The predictive capabilities of the models were evaluated by receiver operating characteristic curves (AUC) values and confusion matrices. The Delong test was utilized to compare the predictive performance among the different models. RESULTS: A total of 3034 patients with NSCLC were recruited in this study including 106 LVI+ patients. In the validation cohort, the Dual-head Res2Net_3D23F model achieved the highest AUC of 0.869, closely followed by the models of Dual-head Res2Net_3D3F (AUC, 0.868), Dual-head Res2Net_3D (AUC, 0.867), and EfficientNet-B0_2D (AUC, 0.857). There was no significant difference observed in the performance of the EfficientNet-B0_2D model when compared to the Dual-head Res2Net_3D3F and Dual-head Res2Net_3D23F. CONCLUSION: Findings of this study suggest that utilizing deep convolutional neural network is a feasible approach for predicting pathological LVI in patients with NSCLC.

Ct-defined emphysema prevalence in a Chinese and Dutch general population.

PURPOSE: We determine and compare the prevalence, subtypes, severity, and risk factors for emphysema assessed by low-dose CT(LDCT) in Chinese and Dutch general populations. METHODS: This cross-sectional study included LDCT scans of 1143 participants between May and October 2017 from a Chinese Cohort study and 1200 participants with same age range and different smoking status between May and October 2019 from a Dutch population-based study. An experienced radiologist visually assessed the scans for emphysema presence (≥trace), subtype, and severity. Logistic regression analyses, overall and stratified by smoking status, were performed and adjusted for fume exposure, demographic and smoking data. RESULTS: The Chinese population had a comparable proportion of women to the Dutch population (54.9 % vs 58.9 %), was older (61.7 ± 6.3 vs 59.8 ± 8.1), included more never smokers (66.4 % vs 38.3 %), had a higher emphysema prevalence ([58.8 % vs 39.7 %], adjusted odds ratio, aOR = 2.06, 95 %CI = 1.68-2.53), and more often had centrilobular emphysema (54.8 % vs 32.8 %, p < 0.001), but no differences in emphysema severity. After stratification, only in never smokers an increased odds of emphysema was observed in the Chinese compared to the Dutch (aOR = 2.55, 95 %CI = 1.95-3.35). Never smokers in both populations shared older age (aOR = 1.59, 95 %CI = 1.25-2.02 vs 1.26, 95 %CI = 0.97-1.64) and male sex (aOR = 1.50, 95 %CI = 1.02-2.22 vs 1.93, 95 %CI = 1.26-2.96) as risk factors for emphysema. CONCLUSIONS: Only never smokers had a higher prevalence of mainly centrilobular emphysema in the Chinese general population compared to the Dutch after adjusting for confounders, indicating that factors other than smoking, age and sex contribute to presence of CT-defined emphysema.

Predictive value of radiomic features extracted from primary lung adenocarcinoma in forecasting thoracic lymph node metastasis: a systematic review and meta-analysis.

BACKGROUND: The application of radiomics in thoracic lymph node metastasis (LNM) of lung adenocarcinoma is increasing, but diagnostic performance of radiomics from primary tumor to predict LNM has not been systematically reviewed. Therefore, this study sought to provide a general overview regarding the methodological quality and diagnostic performance of using radiomic approaches to predict the likelihood of LNM in lung adenocarcinoma. METHODS: Studies were gathered from literature databases such as PubMed, Embase, the Web of Science Core Collection, and the Cochrane library. The Radiomic Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were both used to assess the quality of each study. The pooled sensitivity, specificity, and area under the curve (AUC) of the best radiomics models in the training and validation cohorts were calculated. Subgroup and meta-regression analyses were also conducted. RESULTS: Seventeen studies with 159 to 1202 patients each were enrolled between the years of 2018 to 2022, of which ten studies had sufficient data for the quantitative evaluation. The percentage of RQS was between 11.1% and 44.4% and most of the studies were considered to have a low risk of bias and few applicability concerns in QUADAS-2. Pyradiomics and logistic regression analysis were the most commonly used software and methods for radiomics feature extraction and selection, respectively. In addition, the best prediction models in seventeen studies were mainly based on radiomics features combined with non-radiomics features (semantic features and/or clinical features). The pooled sensitivity, specificity, and AUC of the training cohorts were 0.84 (95% confidence interval (CI) [0.73-0.91]), 0.88 (95% CI [0.81-0.93]), and 0.93(95% CI [0.90-0.95]), respectively. For the validation cohorts, the pooled sensitivity, specificity, and AUC were 0.89 (95% CI [0.82-0.94]), 0.86 (95% CI [0.74-0.93]) and 0.94 (95% CI [0.91-0.96]), respectively. CONCLUSIONS: Radiomic features based on the primary tumor have the potential to predict preoperative LNM of lung adenocarcinoma. However, radiomics workflow needs to be standardized to better promote the applicability of radiomics. TRIAL REGISTRATION: CRD42022375712.

Improving image quality of sparse-view lung tumor CT images with U-Net.

BACKGROUND: We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence. METHODS: CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used. RESULTS: The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images. CONCLUSIONS: Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level. RELEVANCE STATEMENT: Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose. KEY POINTS: • Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

Intracranial arterial calcification in patients with unruptured and ruptured intracranial aneurysms.

OBJECTIVES: Arterial calcification is thought to protect against rupture of intracranial aneurysms, but studies in a representative population of intracranial aneurysm patients have not yet been performed. The aim was to compare the prevalence of aneurysm wall calcification and intracranial carotid artery calcification (ICAC) between patients with an unruptured intracranial aneurysm (UIA) and a ruptured intracranial aneurysm (RIA). MATERIALS AND METHODS: We matched 150 consecutive UIA patients to 150 RIA patients on age and sex. Aneurysm wall calcification and ICAC were quantified on non-contrast enhanced computed tomography images with the modified Agatston score. We compared the prevalence of aneurysm wall calcification, ICAC, and severe ICAC (defined as a modified Agatston score in the fourth quartile) between UIA and RIA patients using univariate and multivariate conditional logistic regression models adjusted for aneurysm characteristics and cardiovascular risk factors. RESULTS: Aneurysm wall calcification was more prevalent in UIA compared to RIA patients (OR 5.2, 95% CI: 2.0-13.8), which persisted after adjustment (OR 5.9, 95% CI: 1.7-20.2). ICAC prevalence did not differ between the two groups (crude OR 0.9, 95% CI: 0.5-1.8). Severe ICAC was more prevalent in UIA patients (OR 2.0, 95% CI: 1.1-3.6), but not after adjustment (OR 1.0, 95% CI: 0.5-2.3). CONCLUSIONS: Aneurysm wall calcification but not ICAC was more prevalent in UIAs than in RIAs, which corresponds to the hypothesis that calcification may protect against aneurysmal rupture. Aneurysm wall calcification should be further assessed as a predictor of aneurysm stability in prospective cohort studies. CLINICAL RELEVANCE STATEMENT: Calcification of the intracranial aneurysm wall was more prevalent in unruptured than ruptured intracranial aneurysms after adjustment for cardiovascular risk factors. Calcification may therefore protect the aneurysm against rupture, and aneurysm wall calcification is a candidate predictor of aneurysm stability. KEY POINTS: Aneurysm wall calcification was more prevalent in patients with unruptured than ruptured aneurysms, while internal carotid artery calcification was similar. Aneurysm wall calcification but not internal carotid artery calcification is a candidate predictor of aneurysm stability. Cohort studies are needed to assess the predictive value of aneurysm wall calcification for aneurysm stability.

Laterality of CT-measured hepatic extracellular volume fraction in patients with chronic thromboembolic pulmonary hypertension.

PURPOSE: This study examines the hepatic extracellular volume fraction (ECV) disparity between the left and right lobes (ECV_left and ECV_right) in patients with chronic thromboembolic pulmonary hypertension (CTEPH), its association with right heart catheterization (RHC) metrics, and with intolerance to increased pulmonary hypertension (PH)-targeted medication dosages. METHODS: We retrospectively analyzed 72 CTEPH-diagnosed patients who underwent equilibrium-phase abdominal dual-energy CT (DECT) and RHC. Hepatic ECVs, derived from DECT's iodine maps using circular regions of interest in the liver and aorta, were correlated with RHC parameters via Spearman's rank correlation and lobe differences through the Wilcoxon signed-rank test. Logistic regression assessed cases with ECV_left exceeding ECV_right by > 0.05, while receiver operating characteristic curve analysis gauged ECVs' predictive power for medication intolerance. RESULTS: Of the 72 patients (57 females; median age 69), ECV_total (0.24, IQR 0.20-0.27) moderately correlated with RHC parameters (rs = 0.28, -0.24, 0.3 for mean pulmonary arterial pressure, cardiac index [CI], and pulmonary vascular resistance index, respectively). ECV_left significantly surpassed ECV_right (0.25 vs. 0.22, p < 0.001), with a greater ECV_left by > 0.05 indicating notably lower CI (p < 0.001). In 27 patients on PH medication, ECV_left effectively predicted medication intolerance (AUC = 0.84). CONCLUSION: In CTEPH patients, hepatic ECV correlated with RHC metrics, where elevated left lobe ECV suggested reduced CI and potential medication intolerance.

Reproducibility of a semiautomatic lobar lung tissue assignment technique on noncontrast CT scans: a study on swine animal model.

BACKGROUND: To evaluate the reproducibility of a vessel-specific minimum cost path (MCP) technique used for lobar segmentation on noncontrast computed tomography (CT). METHODS: Sixteen Yorkshire swine (49.9 ± 4.7 kg, mean ± standard deviation) underwent a total of 46 noncontrast helical CT scans from November 2020 to May 2022 using a 320-slice scanner. A semiautomatic algorithm was employed by three readers to segment the lung tissue and pulmonary arterial tree. The centerline of the arterial tree was extracted and partitioned into six subtrees for lobar assignment. The MCP technique was implemented to assign lobar territories by assigning lung tissue voxels to the nearest arterial tree segment. MCP-derived lobar mass and volume were then compared between two acquisitions, using linear regression, root mean square error (RMSE), and paired sample t-tests. An interobserver and intraobserver analysis of the lobar measurements was also performed. RESULTS: The average whole lung mass and volume was 663.7 ± 103.7 g and 1,444.22 ± 309.1 mL, respectively. The lobar mass measurements from the initial (MLobe1) and subsequent (MLobe2) acquisitions were correlated by MLobe1 = 0.99 MLobe2 + 1.76 (r = 0.99, p = 0.120, RMSE = 7.99 g). The lobar volume measurements from the initial (VLobe1) and subsequent (VLobe2) acquisitions were correlated by VLobe1 = 0.98VLobe2 + 2.66 (r = 0.99, p = 0.160, RSME = 15.26 mL). CONCLUSIONS: The lobar mass and volume measurements showed excellent reproducibility through a vessel-specific assignment technique. This technique may serve for automated lung lobar segmentation, facilitating clinical regional pulmonary analysis. RELEVANCE STATEMENT: Assessment of lobar mass or volume in the lung lobes using noncontrast CT may allow for efficient region-specific treatment strategies for diseases such as pulmonary embolism and chronic thromboembolic pulmonary hypertension. KEY POINTS: • Lobar segmentation is essential for precise disease assessment and treatment planning. • Current methods for segmentation using fissure lines are problematic. • The minimum-cost-path technique here is proposed and a swine model showed excellent reproducibility for lobar mass measurements. • Interobserver agreement was excellent, with intraclass correlation coefficients greater than 0.90.

Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard.

BACKGROUND: We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques. METHODS: Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used. RESULTS: Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001). CONCLUSIONS: Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes. KEY POINTS: • Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.