Automated evaluation of diaphragm configuration based on chest CT in COPD patients.

BACKGROUND: Severe chronic obstructive pulmonary disease (COPD) often results in hyperinflation and flattening of the diaphragm. An automated computed tomography (CT)-based tool for quantifying diaphragm configuration, a biomarker for COPD, was developed in-house and tested in a large cohort of COPD patients. METHODS: We used the LungQ platform to extract the lung-diaphragm intersection, as direct diaphragm segmentation is challenging. The tool computed the diaphragm index (surface area/projected surface area) as a measure of diaphragm configuration on inspiratory scans in a COPDGene subcohort. Visual inspection of 250 randomly selected segmentations served as a quality check. Associations between the diaphragm index, Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages, forced expiratory volume in 1 s (FEV1) % predicted, and CT-derived emphysema scores were explored using analysis of variance and Pearson correlation. RESULTS: The tool yielded incomplete segmentation in 9.2% (2.4% major defect, 6.8% minor defect) of 250 randomly selected cases. In 8431 COPDGene subjects (4240 healthy; 4191 COPD), the diaphragm index was increasingly lower with higher GOLD stages (never-smoked 1.83 ± 0.16; GOLD-0 1.79 ± 0.18; GOLD-1 1.71 ± 0.15; GOLD-2: 1.67 ± 0.16; GOLD-3 1.58 ± 0.14; GOLD-4 1.54 ± 0.11) (p < 0.001). Associations were found between the diaphragm index and both FEV1% predicted (r = 0.44, p < 0.001) and emphysema score (r = -0.36, p < 0.001). CONCLUSION: We developed an automated tool to quantify the diaphragm configuration in chest CT. The diaphragm index was associated with COPD severity, FEV1%predicted, and emphysema score. RELEVANCE STATEMENT: Due to the hypothesized relationship between diaphragm dysfunction and diaphragm configuration in COPD patients, automatic quantification of diaphragm configuration may prove useful in evaluating treatment efficacy in terms of lung volume reduction. KEY POINTS: Severe COPD changes diaphragm configuration to a flattened state, impeding function. An automated tool quantified diaphragm configuration on chest-CT providing a diaphragm index. The diaphragm index was correlated to COPD severity and may aid treatment assessment.

Abdominal aneurysm sac thrombus CT density and volume after EVAR: which association with underlying endoleak?

BACKGROUND: Our aim was to analyse abdominal aneurysm sac thrombus density and volume on computed tomography (CT) after endovascular aneurysm repair (EVAR). METHODS: Patients who underwent EVAR between January 2005 and December 2010 and had at least four follow-up CT exams available over the first five years of follow-up were included in this retrospective single-centre study. Thrombus density and aneurysm sac volume were calculated on unenhanced CT scans. Linear mixed models were used for data analysis. RESULTS: Out of 82 patients, 44 (54%) had an endoleak on post-EVAR contrast-enhanced CT. Thrombus density significantly increased over time in both the endoleak and non-endoleak groups, with a slope of 0.159 UH/month (95% confidence interval [CI] 0.115-0.202), p < 0.0001) and 0.052 UH/month (95% CI 0.002-0.102, p = 0.041). In patients without endoleak, a significant decrease in aneurysm sac volume was identified over time (slope -0.891 cc/month, 95% CI -1.200 to -0.581); p < 0.001) compared to patients with endoleak (slope 0.284 cc/month, 95% CI -0.031 to 0.523, p = 0.082). The association between thrombus density and aneurysm sac volume was positive in the endoleak group (slope 1.543 UH/cc, 95% CI 0.948-2.138, p < 0.001) and negative in the non-endoleak group (slope -1.450 UH/cc, 95% CI -2.326 to -0.574, p = 0.001). CONCLUSION: We observed a progressive increase in thrombus density of the aneurysm sac after EVAR in patients with and without endoleak, more pronounced in patients with endoleak. The association between aneurysm volume and thrombus density was positive in patients with and negative in those without endoleak. RELEVANCE STATEMENT: A progressive increase in thrombus density and volume of abdominal aortic aneurysm sac on unenhanced CT might suggest underlying endoleak lately after EVAR. KEY POINTS: Thrombus density of the aneurysm sac after EVAR increased over time. Progressive increase in thrombus density was significantly associated to the underlying endoleak. The association between aneurysm volume and thrombus density was positive in patients with and negative in those without endoleak.

Reproducibility of CT-based opportunistic vertebral volumetric bone mineral density measurements from an automated segmentation framework.

BACKGROUND: To investigate the reproducibility of automated volumetric bone mineral density (vBMD) measurements from routine thoracoabdominal computed tomography (CT) assessed with segmentations by a convolutional neural network and automated correction of contrast phases, on diverse scanners, with scanner-specific asynchronous or scanner-agnostic calibrations. METHODS: We obtained 679 observations from 278 CT scans in 121 patients (77 males, 63.6%) studied from 04/2019 to 06/2020. Observations consisted of two vBMD measurements from Δdifferent reconstruction kernels (n = 169), Δcontrast phases (n = 133), scan Δsessions (n = 123), Δscanners (n = 63), or Δall of the aforementioned (n = 20), and observations lacking scanner-specific calibration (n = 171). Precision was assessed using root-mean-square error (RMSE) and root-mean-square coefficient of variation (RMSCV). Cross-measurement agreement was assessed using Bland-Altman plots; outliers within 95% confidence interval of the limits of agreement were reviewed. RESULTS: Repeated measurements from Δdifferent reconstruction kernels were highly precise (RMSE 3.0 mg/cm3; RMSCV 1.3%), even for consecutive scans with different Δcontrast phases (RMSCV 2.9%). Measurements from different Δscan sessions or Δscanners showed decreased precision (RMSCV 4.7% and 4.9%, respectively). Plot-review identified 12 outliers from different scan Δsessions, with signs of hydropic decompensation. Observations with Δall differences showed decreased precision compared to those lacking scanner-specific calibration (RMSCV 5.9 and 3.7, respectively). CONCLUSION: Automatic vBMD assessment from routine CT is precise across varying setups, when calibrated appropriately. Low precision was found in patients with signs of new or worsening hydropic decompensation, what should be considered an exclusion criterion for both opportunistic and dedicated quantitative CT. RELEVANCE STATEMENT: Automated CT-based vBMD measurements are precise in various scenarios, including cross-session and cross-scanner settings, and may therefore facilitate opportunistic screening for osteoporosis and surveillance of BMD in patients undergoing routine clinical CT scans. KEY POINTS: Artificial intelligence-based tools facilitate BMD measurements in routine clinical CT datasets. Automated BMD measurements are highly reproducible in various settings. Reliable, automated opportunistic osteoporosis diagnostics allow for large-scale application.

The radiologist's role in detecting systemic anticancer therapy-related interstitial lung disease: an educational review.

Systemic anticancer therapies (SACTs) are the leading cause of drug-induced interstitial lung disease (ILD). As more novel SACTs become approved, the incidence of this potentially life-threatening adverse event (AE) may increase. Early detection of SACT-related ILD allows for prompt implementation of drug-specific management recommendations, improving the likelihood of AE resolution and, in some instances, widening the patient's eligibility for future cancer treatment options. ILD requires a diagnosis of exclusion through collaboration with the patient's multidisciplinary team to rule out other possible etiologies of new or worsening respiratory signs and symptoms. At Grade 1, ILD is asymptomatic, and thus the radiologist is key to detecting the AE prior to the disease severity worsening. Planned computed tomography scans should be reviewed for the presence of ILD in addition to being assessed for tumor response to treatment, and when ILD is suspected, a high-resolution computed tomography (HRCT) scan should be requested immediately. An HRCT scan, with < 2-mm slice thickness, is the most appropriate method for detecting ILD. Multiple patterns of ILD exist, which can impact patient prognosis. The four main patterns include acute interstitial pneumonia / acute respiratory distress syndrome, organizing pneumonia, hypersensitivity pneumonitis, and non-specific interstitial pneumonia; their distinct radiological features, along with rarer patterns, are discussed here. Furthermore, HRCT is essential for following the course of ILD and might help to determine the intensity of AE management and the appropriateness of re-challenging with SACT, where indicated by drug-specific prescribing information. ILD events should be monitored closely until complete resolution. CRITICAL RELEVANCE STATEMENT: The incidence of potentially treatment-limiting and life-threatening systemic anticancer therapy-related interstitial lung disease (SACT-related ILD) events is likely increasing as more novel regimens become approved. This review provides best-practice recommendations for the early detection of SACT-related ILD by radiologists. KEY POINTS: Radiologists are crucial in detecting asymptomatic (Grade 1) ILD before severity/prognosis worsens. High-resolution computed tomography is the most appropriate method for detecting ILD. Drug-induced ILD is a diagnosis of exclusion, involving a multidisciplinary team. Familiarity with common HRCT patterns, described here, is key for prompt detection. Physicians should highlight systemic anticancer therapies (SACTs) with a known risk for interstitial lung diseases (ILD) on scan requisitions.

Differentiating lung neuroendocrine neoplasms from tumor-like infection using CT in patients with ectopic ACTH syndrome.

OBJECTIVES: Pulmonary neuroendocrine neoplasms (NENs) are the most frequent cause of ectopic adrenocorticotropic hormone syndrome (EAS); lung infection is common in EAS. An imaging finding of infection in EAS patients can mimic NENs. This retrospective study investigated EAS-associated pulmonary imaging indicators. METHODS: Forty-five pulmonary NENs and 27 tumor-like infections from 59 EAS patients (45 NEN and 14 infection patients) were included. Clinical manifestations, CT features, 18F-FDG, or 68Ga-DOTATATE-PET/CT images and pathological results were collected. RESULTS: High-sensitivity C-reactive protein (p < 0.001) and expectoration occurrence (p = 0.04) were higher, and finger oxygen saturation (p = 0.01) was lower in the infection group than the NENs group. Higher-grade NENs were underrepresented in our cohort. Pulmonary NENs were solitary primary tumors, 80% of which were peripheral tumors. Overlying vessel sign and airway involvement were more frequent in the NENs group (p < 0.001). Multifocal (p = 0.001) and peripheral (p = 0.02) lesions, cavity (p < 0.001), spiculation (p = 0.01), pleural retraction (p < 0.001), connection to pulmonary veins (p = 0.02), and distal atelectasis or inflammatory exudation (p = 0.001) were more frequent in the infection group. The median CT value increment between the non-contrast and arterial phases was significantly higher in NENs lesions (p < 0.001). Receiver operating characteristic curve analysis indicated a moderate predictive ability at 48.3 HU of delta CT value (sensitivity, 95.0%; specificity, 54.1%). CONCLUSION: Chest CT scans are valuable for localizing and characterizing pulmonary lesions in rare EAS, thereby enabling prompt differential diagnosis and treatment. CRITICAL RELEVANCE STATEMENT: Thin-slice CT images are valuable for the localization and identification of pulmonary ectopic adrenocorticotropic hormone syndrome lesions, leading to prompt differential diagnosis and effective treatment. KEY POINTS: Lung tumor-like infections can mimic neuroendocrine neoplasms (NENs) in ectopic adrenocorticotropic hormone syndrome (EAS) patients. NENs are solitary lesions, whereas infections are multiple peripheral pseudotumors each with identifying imaging findings. Typical CT signs aid in localization and creating an appropriate differential diagnosis.

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.

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.

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.

Radiomics-based prediction of recurrence for head and neck cancer patients using data imbalanced correction.

OBJECTIVES: To propose a radiomics-based prediction model for head and neck squamous cell carcinoma (HSNCC) recurrence after radiation therapy using a novel data imbalance correction method known as Gaussian noise upsampling (GNUS). MATERIALS AND METHODS: The dataset includes 97 HNSCC patients treated with definitive radiotherapy alone or concurrent chemoradiotherapy at two institutions. We performed radiomics analysis using nine segmentations created on pretreatment positron emission tomography and computed tomography images. Feature selection was performed by the least absolute shrinkage and selection operator analysis via five-fold cross-validation. The proposed GNUS was compared with seven conventional data-imbalance correction methods. Classification models of HNSCC recurrence were constructed on oversampled features using the machine learning algorithms of linear regression. Their predictive performance was evaluated based on accuracy, sensitivity, specificity, and the area under the curve (AUC) of the receiver operating performance characteristic curve via five-fold cross-validation using the same combinations as for feature selection. RESULT: The prediction model without data imbalance correction shows sensitivity, specificity, accuracy, and AUC values of 83 %, 96 %, 92 %, and 0.96, respectively. The conventional model with the best performance is the random over-sampler model, which shows sensitivity, specificity, accuracy, and AUC values of 93 %, 91 %, 92 %, 0.97, respectively, whereas the GNUS model shows values of 93 %, 94 %, 94 %, 0.98, respectively. CONCLUSION: Oversampling methods can reduce sensitivity and specificity bias. The proposed GNUS can improve accuracy as well as reduce sensitivity and specificity bias.

Low-contrast lesion detection in neck CT: a multireader study comparing deep learning, iterative, and filtered back projection reconstructions using realistic phantoms.

BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS: Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS: DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT: Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS: Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.

Deep Cervical-thoracic Osteolipoma Near the Brachial Plexus - Case Report and Literature Review.

Osteolipoma is a rare benign variant of lipoma and constitutes less than 1% of all lipomas, presenting as a well-circumscribed painless mass. It is a tumor known to occur in several regions, usually intraosseous or adjacent to bone tissue, whose pathogenesis is still unclear. Imaging exams are useful in their evaluation and, mainly, in surgical planning, which consists of tumor excision. However, the definitive diagnosis of osteolipoma is made by histopathological examination. Although benign, osteolipomas can compress surrounding structures, leading to important symptomatology, as in this case reported in which it is in contact with the brachial plexus.

Prevalence and progression rate of interstitial lung abnormalities detected on thoracic CT: a systematic review and meta-analysis.

OBJECTIVES: To estimate the pooled prevalence and progression rate of ILAs and identify the risk factors for radiological progression. MATERIALS AND METHODS: An EMBASE and PubMed search was undertaken, identifying all studies meeting the inclusion criteria performed before May 10, 2023. Random effect models were used to estimate pooled prevalence, ILA progression rates, and odds ratio for radiological progression based on radiological subtype. Subgroup analyses were performed to compare the general and high-risk populations for lung cancer. The quality of the included studies was evaluated using the risk of bias assessment tool for non-randomized studies. RESULTS: We analyzed 19 studies (241,541 patients) and 10 studies (1317 patients) for the pooled prevalence and progression rate of ILA, respectively. The pooled ILA prevalence was 9.7% (95% CI, 6.1-13.9%). The pooled prevalence was 6.8% (95% CI, 3.1-11.6%) and 7.1% (95% CI, 2.2-14.4%) in the general (six studies) and high-risk population for lung cancer (six studies), respectively. The pooled progression rate was 47.1% (95% CI, 29.1-65.5%). The pooled progression rate was 64.2% (95% CI, 45.0-81.2%, five studies) and 31.0% (95% CI, 8.2-60.5%, five studies) for longer (≥ 4.5 years) and shorter follow-up periods (< 4.5 years), respectively (p = 0.009). Fibrotic ILAs were significantly associated with a higher progression probability (combined OR, 5.55; 95% CI, 1.95-15.82). CONCLUSIONS: The prevalence of ILAs was approximately 9.7%. Approximately half of the patients exhibited radiological progression, with the rate increasing over a longer follow-up period. Fibrotic ILA was a significant risk factor for radiological progression. CLINICAL RELEVANCE STATEMENT: The prevalence of interstitial lung abnormalities (ILAs) is approximately 9.7%, with about half exhibiting progression; a longer follow-up duration and fibrotic ILAs are associated with a higher progression rate. KEY POINTS: ILAs are increasingly recognized as important, but few population data are available. ILAs exhibited a pooled prevalence of 9.7% with a progression rate of 47.1%. Fibrotic ILAs were associated with increased progression likelihood.

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.

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.

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.

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.

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.

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.