Chest CT Scan Research Articles

Elevated triglycerides and low triiodothyronine: Key risk factors for coronary artery calcification in patients with schizophrenia

ObjectiveCoronary artery calcification (CAC) is a well-established independent predictor of coronary heart disease, and patients with schizophrenia have significantly higher rates compared to the general population. We performed this study to examine the population-specific risk factors associated with CAC in patients with schizophrenia. MethodsIn this cross-sectional study, patients with schizophrenia who underwent low-dose chest CT scans between January 2020 and December 2021 were analyzed. Ordinary CAC scores and results of routine blood tests were obtained. Logistic regression was used to calculate the odds ratio (OR) for potential risk factors in patients with and without CAC, while the negative binomial additive model was used to explore the dose-response relationship between risk factors and CAC score. ResultsOf the 916 patients, 233 (25.4 %) had CAC, while 683 (74.6 %) did not. After adjusting for confounding factors, higher triglyceride levels (OR = 1.20, 95 % confidence interval (CI): 1.04 to 1.38, p = 0.013) and low triiodothyronine levels (OR = 0.50, 95 % CI: 0.29 to 0.84; p = 0.010) were identified as risk factors for CAC. Both triglycerides (p = 0.021) and triiodothyronine (p = 0.010) were also found to have significant dose-response relationships with CAC scores according to the negative binomial additive model in the exploratory analysis. ConclusionsThis study highlights elevated serum triglycerides and decreased triiodothyronine levels as population-specific risk factors for CAC in patients with schizophrenia, suggest the need for close monitoring of CAC in patients with schizophrenia and further prospective trials to provide additional evidence on this topic.

Analysis of associations between the TLR3 SNPs rs3775291 and rs3775290 and COVID-19 in a cohort of professionals of Belém-PA, Brazil.

The objective of this article was to verify associations between the SNPs rs3775291 (Cytosine [C]>Thymine [T]) and rs3775290 (C>T) of TLR3 in professionals from Health Institutions (HI) who worked during the first pandemic wave and COVID-19. A case-control study was carried out with workers from HI in Belém-PA, Brazil, divided into symptomatology groups (Asymptomatic-AS, n=91; and Symptomatic-SI, n=121), and severity groups, classified by Chest CT scan (symptomatic with lung involvement - SCP, n=34; symptomatic without lung involvement - SSP, n=8). Genotyping was performed by Sanger sequencing and statistical analysis was performed using the SPSS program. In the analysis of SNP rs3775291, the homozygous recessive genotype (T/T) was not found and the frequency of the mutant allele (T) was less than 2% in the cohort. For the rs3775290 SNP, the frequency of the mutant allele (T) was greater than 42% in the cohort. No significant associations were found for these SNPs in this cohort (N= 212 individuals). The scientific community and physicians can use these facts to find new methods of managing COVID-19.

Which Groups of Children Are at More Risk of Fatality during COVID-19 Pandemic? A Case-Control Study in Yazd, Iran.

The study aims to investigate the characteristics, comorbidities, laboratory findings, and clinical manifestations of under 18-year-old patients who died with the diagnosis of COVID-19 and determination of the most prevalent risk factors. This case-control study was performed at a referral hospital in Yazd from March 2020 to August 2021. All patients under 18 years who were diagnosed through real-time RT-PCR, chest computed tomography, and the World Health Organization definition were divided into deceased and survived groups. The characteristics (age and sex), disease severity, comorbidities, laboratory findings, and clinical manifestations of the two groups were compared and analyzed using SPSS, version 18 (SPSS Inc., Chicago, III., USA). A total of 24 patients in the deceased group and 167 patients in the survived group were compared. The highest mortality rate was observed in the age group of 1 month to 5 years, although no statistically significant relationship was found between age groups and the risk of mortality. Disease severity, dyspnea, low oxygen saturation on admission, length of hospital stays, and hospitalization history before the last admission were significantly correlated with mortality (P < 0.05). Lymphopenia increased the probability of mortality by more than two times (OR: 2.568; 95% CI (0.962-6.852)), but this was not the case for D-dimer and C-reactive protein. Furthermore, 27.5% of survived patients had normal chest CT scans, which was a statistically significant difference compared to the deceased patients (P: 0.031). Based on the findings of this study, dyspnea, low oxygen saturation, and lymphopenia are critical indicators for identifying high-risk children with COVID-19 and triaging them for better care and treatment.

Detection of subsolid nodules on chest CT scans during the COVID-19 pandemic.

To investigate the detection of subsolid nodules (SSNs) on chest CT scans of outpatients before and during the COVID-19 pandemic, as well as to correlate the imaging findings with epidemiological data. We hypothesized that (pre)malignant nonsolid nodules were underdiagnosed during the COVID-19 pandemic because of an overlap of imaging findings between SSNs and COVID-19 pneumonia. This was a retrospective study including all chest CT scans performed in adult outpatients (> 18 years of age) in September of 2019 (i.e., before the COVID-19 pandemic) and in September of 2020 (i.e., during the COVID-19 pandemic). The images were reviewed by a thoracic radiologist, and epidemiological data were collected from patient-filled questionnaires and clinical referrals. Regression models were used in order to control for confounding factors. A total of 650 and 760 chest CT scans were reviewed for the 2019 and 2020 samples, respectively. SSNs were found in 10.6% of the patients in the 2019 sample and in 7.9% of those in the 2020 sample (p = 0.10). Multiple SSNs were found in 23 and 11 of the patients in the 2019 and 2020 samples, respectively. Women constituted the majority of the study population. The mean age was 62.8 ± 14.8 years in the 2019 sample and 59.5 ± 15.1 years in the 2020 sample (p < 0.01). COVID-19 accounted for 24% of all referrals for CT examination in 2020. Fewer SSNs were detected on chest CT scans of outpatients during the COVID-19 pandemic than before the pandemic, although the difference was not significant. In addition to COVID-19, the major difference between the 2019 and 2020 samples was the younger age in the 2020 sample. We can assume that fewer SSNs will be detected in a population with a higher proportion of COVID-19 suspicion or diagnosis.

Robustness of Single- and Dual-Energy Deep-Learning-Based Scatter Correction Models on Simulated and Real Chest X-rays

Chest X-rays (CXRs) represent the first tool globally employed to detect cardiopulmonary pathologies. These acquisitions are highly affected by scattered photons due to the large field of view required. Scatter in CXRs introduces background in the images, which reduces their contrast. We developed three deep-learning-based models to estimate and correct scatter contribution to CXRs. We used a Monte Carlo (MC) ray-tracing model to simulate CXRs from human models obtained from CT scans using different configurations (depending on the availability of dual-energy acquisitions). The simulated CXRs contained the separated contribution of direct and scattered X-rays in the detector. These simulated datasets were then used as the reference for the supervised training of several NNs. Three NN models (single and dual energy) were trained with the MultiResUNet architecture. The performance of the NN models was evaluated on CXRs obtained, with an MC code, from chest CT scans of patients affected by COVID-19. The results show that the NN models were able to estimate and correct the scatter contribution to CXRs with an error of &lt;5%, being robust to variations in the simulation setup and improving contrast in soft tissue. The single-energy model was tested on real CXRs, providing robust estimations of the scatter-corrected CXRs.

Differential effect of atorvastatin and pravastatin on thoracic spine attenuation: A sub-analysis of a randomized clinical trial

BackgroundStatins reduce cardiovascular events and may improve bone mineral density. MethodsWe conducted a sub-analysis of a randomized clinical trial that investigated the differential effect of moderate vs intensive low-density lipoprotein cholesterol (LDL-C) lowering therapies on coronary artery calcium (CAC) scores, and used the acquired images to assess the change in radiological attenuation of selected thoracic vertebrae. Baseline and 12-month unenhanced chest CT scans were performed in 420 hyperlipidemic, postmenopausal women randomized to atorvastatin (ATV) 80 mg/day or pravastatin (PRV) 40 mg/day in the Beyond Endorsed Lipid Lowering with Electron Beam Tomography Scanning (BELLES) trial. Bone attenuation was measured in three contiguous thoracic vertebrae at baseline and 12 months. ResultsThere were no differences in baseline demographic and clinical characteristics between treatment arms. The median percent lowering (interquartile range) in LDL-C was significantly greater with ATV than PRV [-53 (-69 to 20)% vs −28 (-55 to 74)%, p < 0.001], although the CAC score change was similar [12 (-63 to 208)% vs 13 (-75 to 358)%; p = 0.44]. At follow-up, the median bone attenuation loss was significantly greater with PRV than with ATV [-2.6 (-27 to 11)% vs 0 (-11 to 25)%; p < 0.001]. The attenuation loss in the PRV group was comparable to that of a historical untreated general population sample. In the entire cohort, the changes in LDL-C and total cholesterol were inversely correlated with bone attenuation change (p < 0.01). In adjusted multivariable linear regression analyses, race and percent change in LDL-C were independent predictors of bone attenuation change. Age, body mass index, history of smoking, diabetes mellitus, hypertension, peripheral vascular disease, or hormone replacement therapy did not affect percent change in BMD. ConclusionsThese findings support the hypothesis that there is an interaction between bone and cardiometabolic health and that intensive lipid lowering has a beneficial effect on bone health.

Attention-guided deep neural network with a multichannel architecture for lung nodule classification

Detecting and accurately identifying malignant lung nodules in chest CT scans in a timely manner is crucial for effective lung cancer treatment. This study introduces a deep learning model featuring a multi-channel attention mechanism, specifically designed for the precise diagnosis of malignant lung nodules. To start, we standardized the voxel size of CT images and generated three RGB images of varying scales for each lung nodule, viewed from three different angles. Subsequently, we applied three attention submodels to extract class-specific characteristics from these RGB images. Finally, the nodule features were consolidated in the model's final layer to make the ultimate predictions. Through the utilization of an attention mechanism, we could dynamically pinpoint the exact location of lung nodules in the images without the need for prior segmentation. This proposed approach enhances the accuracy and efficiency of lung nodule classification. We evaluated and tested our model using a dataset of 1018 CT scans sourced from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The experimental results demonstrate that our model achieved a lung nodule classification accuracy of 90.11 %, with an area under the receiver operator curve (AUC) score of 95.66 %. Impressively, our method achieved this high level of performance while utilizing only 29.09 % of the time needed by the mainstream model.

Diagnosing disorders of respiratory mechanics in refractory workers

Introduction. The current decrease in occupational exposures complicates expert examination of the link between job and disease, thus requiring the use of more sensitive and high-tech diagnostic techniques. In addition, a low incidence rate of occupational diseases against the background of poor working conditions indicates a possible increase in the incidence of “hidden” occupational diseases mimicking common non-communicable diseases.&#x0D; The purpose of our study was to assess the respiratory function in refractory workers using body plethysmography and measurements of the diffusing capacity of the lungs.&#x0D; Materials and methods: One hundred six male workers aged of 45.62±0.8 years, all engaged in refractory manufacturing for the average of 15.5±0.88 years and at risk of dust-related respiratory diseases, were examined in our Occupational Health Center within the regular health check-up.&#x0D; Results. The results of body plethysmography (a pulmonary function test) showed a decrease in the FEV1/FVC ratio, i.e. the ratio of the forced expiratory volume over the first one second to the forced vital capacity of the lungs, to the group average of 77.87±0.76% of the normal value. We also observed increased bronchial resistance during exhalation and total bronchial resistance in 65% and 50.9% of the workers, respectively. We registered a decreased transfer factor in the examined subjects with the group average of 73.9±1.37%. Neither chest X-ray nor CT scan showed significant changes in most of workers. Signs of emphysema were registered in 5.7% of those examined cases. No significant differences in the prevalence of obstructive disorders were found between smoking and non-smoking workers. Bronchial obstructive syndrome in combination with impaired pulmonary gas exchange and respiratory muscle fatigue was registered &#x0D; in 16 workers (15%), thus necessitating specific therapy to interfere with disease progression.&#x0D; Limitations. Our findings cannot be extrapolated to the workforce as a whole, since only men included in the study cohort.&#x0D; Conclusions. Indicators of the diffusing capacity of the lungs, which simultaneously reflect both pulmonary ventilation and perfusion, in combination with findings of body plethysmography and chest CT scan, can provide important information about functional changes in the respiratory system. Early diagnosis of bronchial obstructive syndrome allows timely beginning therapy and prevention of disease progression.

Chest X-ray at Emergency Admission and Potential Association with Barotrauma in Mechanically Ventilated Patients: Experience from the Italian Core of the First Pandemic Peak.

Barotrauma occurs in a significant number of patients with COVID-19 interstitial pneumonia undergoing mechanical ventilation. The aim of the current study was to investigate whether the Brixia score (BS) calculated on chest-X-rays acquired at the Emergency Room was associated with barotrauma. We retrospectively evaluated 117 SARS-CoV-2 patients presented to the Emergency Department (ED) and then admitted to the intensive care unit (ICU) for mechanical ventilation between February and April 2020. Subjects were divided into two groups according to the occurrence of barotrauma during their hospitalization. CXRs performed at ED admittance were assessed using the Brixia score. Distribution of barotrauma (pneumomediastinum, pneumothorax, subcutaneous emphysema) was identified in chest CT scans. Thirty-eight subjects (32.5%) developed barotrauma (25 pneumomediastinum, 24 pneumothorax, 24 subcutaneous emphysema). In the barotrauma group we observed higher Brixia score values compared to the non-barotrauma group (mean value 12.18 vs. 9.28), and logistic regression analysis confirmed that Brixia score is associated with the risk of barotrauma. In this work, we also evaluated the relationship between barotrauma and clinical and ventilatory parameters: SOFA score calculated at ICU admittance and number of days of non-invasive ventilation (NIV) prior to intubation emerged as other potential predictors of barotrauma.

Spatial Transcriptomics Resolve an Emphysema-specific Lymphoid Follicle B Cell Signature in COPD.

Within chronic obstructive pulmonary disease (COPD), emphysema is characterized by a significant yet partially understood B cell immune component. To characterize the transcriptomic signatures from lymphoid follicles (LFs) in ever-smokers without COPD and COPD patients with varying degrees of emphysema. Lung sections from 40 COPD patients and ever-smokers were used for LF proteomic and transcriptomic spatial profiling. Formalin and OCT-fixed lung samples obtained from biopsies or lung explants, were assessed for LF presence. Emphysema measurements were obtained from clinical chest CT scans. High confidence transcriptional (HCT) target intersection analyses were conducted to resolve emphysema-induced transcriptional networks. Overall, 115 LFs from ever-smokers and GOLD 1-2 and GOLD 3-4 patients were analyzed. No LFs were found in never-smokers. Differential gene expression analysis revealed significantly increased expression of LF assembly and B cell markers genes in subjects with severe emphysema. HCT analysis revealed activation of abnormal B cell activity signature in LFs (q-value: 2.56E-111). LFs from GOLD 1-2 COPD patients with emphysema showed significantly increased expression of genes associated with antigen presentation, inflammation, and B cell activation and proliferation. LFs from GOLD 1-2 COPD patients without emphysema showed an anti-inflammatory profile. The extent of centrilobular emphysema was significantly associated with genes involved in B cell maturation and antibody production. Protein-RNA network analysis showed that LFs in emphysema have a unique signature skewed towards chronic B cell activation. An off-targeted B cell activation within LFs is associated with autoimmune-mediated emphysema pathogenesis.

Integration of bone turnover markers and radiomic features from chest CT imaging for osteoporosis diagnosis in patients with hip joint injury

PurposeThe objective of this study is to investigate whether the combination of bone turnover marker and radiomic features extracted from chest CT images can improve the diagnostic accuracy for predicting osteoporosis in patients with hip injury. MethodA group of 249 patients who suffered hip injury participated in this study. They underwent various diagnostic tests, including chest CT scans, bone density measurements, and bone turnover marker. The clinical data collected from these patients was used to create clinical models. To extract radiomics features, one thoracic vertebra was selected from each patient using ITK-SNAP. The feature extraction was conducted using a pyramid library tool that runs on the Python programming language. By randomly selecting patients, a training set consisting of 201 individuals and a testing set comprising of 48 participants were formed. A radiomics model was established after extracting radiomics features. The nomogram model was developed by combining radiomics models with clinical models. Finally, the sensitivity, specificity, accuracy, and area under the curve (AUC) of the three models were calculated separately. ResultThe clinical model developed by our research team incorporated variables such as gender, age, vitamin D, and β B-Cross Laps. In the training group, the accuracy of Clinic model recognition was 0.731, AUC was 0.735. In the test group, the accuracy of Clinic model recognition was 0.708, AUC was 0.769. In the training group, the accuracy of nomogram model recognition was 0.891, AUC was 0.943. In the test group, the accuracy of nomogram model recognition was 0.812, AUC was 0.862. ConclusionBy combining the radiological features extracted from thoracic CT scans with the clinical features of patients, it is possible to distinguish hip joint injury patients with or without osteoporosis before surgery.

An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction

Lung cancer is one of the most serious and prevalent diseases, causing many deaths each year. Though CT scan images are mostly used in the diagnosis of cancer, the assessment of scans is an error-prone and time-consuming task. Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. The proposed model was trained on 1000 CT Scan Images of cancerous and non-cancerous cells to find the best combination of parameters in CNN to predict lung cancer accurately. The proposed system recorded the highest accuracy of 92.79%. In addition to that, the paper addresses 192 observations made using the CNN model.

Pembuatan In-House Phantom untuk Pengukuran Image Quality dan Dosimetri Sebagai Tool Optimasi Protokol Pemeriksaan CT Scan Thorak

The computed tomography scanner (CT scan) protocol used in patient examinations must be continuously reviewed and optimized. Medical physicists evaluate the quality of the resulting image using an image quality phantom and evaluate radiation dose using a dosimetry phantom separately. Procuring a dosimetry phantom and image quality phantom for CT scans separately also requires a lot of money. Therefore, developing a dosimetry phantom and image quality phantom combined in one phantom is expected to be a solution to overcome the mentioned problem. This research aims to create a phantom as a combination of a dosimetry phantom and an image quality phantom and to obtain an optimal CT scan protocol so that informative images can be obtained with minimal doses. The phantom that is made resembles the thoracic organs. The dose length product (DLP) and size-specific dose estimates (SSDE) measured from the phantom were compared with those from 100 patient images for chest CT scan examinations from January to December 2022 and measured using IndoseCT.The image quality of the phantom was measured using IndoQCT software. The estimated dosimetry data from the phantom are 122,5 mGy.cm and 10,94 mGy for DLP and SSDE, respectively, while the DLP and SSDE from patients are 143,5 mGy.cm and 9 mGy. Image quality test results for uniformity with a maximum difference in CT number of 4 HU. Maximum noise is 38,1 HU, and the minimum is 30 HU. The geometric accuracy measurement results are 0,4 mm from the actual size. Spatial Resolution of 0,8 lp/mm. The R2 value of contrast linearity is 0,9972. The Modulation Transfer Function (MTF10) measurement results are 0,63 mm--1. The results of the slice thickness test show the suitability between the measured results and the slice thickness setting on the CT Scan. An in-house phantom can be used in dosimetry and image quality measurements to optimize the thoracic CT scan protocol. Keywords: In-house phantom, Image quality, Dosimetry, Optimization

Rheumatoid arthritis, quantitative parenchymal lung features, and mortality among smokers.

There have been limited investigations of the prevalence and mortality impact of quantitative computed tomography (QCT) parenchymal lung features in rheumatoid arthritis (RA). We examined the cross-sectional prevalence and mortality associations of QCT features, comparing RA and non-RA participants. We identified participants with and without RA in COPDGene, a multicentre cohort study of current or former smokers. Using a k-nearest neighbor quantifier, high resolution CT chest scans were scored for percentage of normal lung, interstitial changes, and emphysema. We examined associations between QCT features and RA using multivariable linear regression. After dichotomizing participants at the 75th percentile for each QCT feature among non-RA participants, we investigated mortality associations by RA/non-RA status and quartile 4 vs quartiles 1-3 of QCT features using Cox regression. We assessed for statistical interactions between RA and QCT features. We identified 82 RA cases and 8820 non-RA comparators. In multivariable linear regression, RA was associated with higher percentage of interstitial changes (β = 1.7 ± 0.5, p= 0.0008) but not emphysema (β = 1.3 ± 1.7, p= 0.44). Participants with RA and >75th percentile of emphysema had significantly higher mortality than non-RA participants (HR 5.86, 95%CI 3.75-9.13) as well as RA participants (HR 5.56, 95%CI 2.71-11.38) with ≤75th percentile of emphysema. There were statistical interactions between RA and emphysema for mortality (multiplicative p= 0.014; attributable proportion 0.53, 95%CI 0.30-0.70). Using machine learning-derived QCT data in a cohort of smokers, RA was associated with higher percentage of interstitial changes. The combination of RA and emphysema conferred >5-fold higher mortality.

Nodule pulmonaire suspect chez un grand tabagique : faut-il faire une biopsie ?

Sclerotherapy is a widely used as a lifesaving therapeutic option in cases of upper gastrointestinal bleeding (UGB) due to ruptured gastro-esophageal varices (GOV) in cirrhotic patients, especially when there exists a portosystemic shunt. This endoscopic technique can entail many complications, including systemic and non-thrombotic pulmonary embolism (PE). While multiple pulmonary parenchymal manifestations have been described following sclerotherapy of GOV, to our knowledge no solitary suspicious pulmonary nodule has been described. We report the case of 55-year-old man with heavy smoking history who was referred to our pulmonary clinic for work-up of a solitary pulmonary nodule. He was known to have liver cirrhosis with history of massive UGB due to rupture of GOV two months before. He was treated with sclerotherapy by injecting a 3 cc of Histoacryl/lipiodole solution. The post- endoscopic phase was unremarkable. An enhanced CT scan of chest and abdomen performed two months later showed a right upper lobe nodule, even though at that point, the patient was completely asymptomatic. This was ascribed to non-thrombotic PE secondary to sclerotherapy due to complete resolution of the nodule on a CT scan carried out at 2-month follow-up. At that point, his condition did not require any further treatment. Solitary pulmonary nodule is one of the radiologic manifestations of PE subsequent to sclerotherapy of GOV. Awareness and radiologic follow-up of this unusual radiologic presentation may prevent unnecessary biopsies.

Study the variations in radiation doses in different multi-slice CT scan machines

Objectives: This study aimed to measure variations in dose output and patient’s effective dose across the different computed tomography scanners during standard CT examinations of head, chest and abdomen, so as to determine patient, and machine settings that contribute to variations in radiation dose to optimize patient effective dose. Methods: Retrospective, study performed in different hospitals, in Najran province (K.S.A) from October 2022 to June 2023. The study comprise 360 adults CT examinations. The mean values of CT dose index volume (CTDIvol) and dose length product (DLP) were measured. The patient effective dose (ED) were calculated for each protocol and compared among different CT scanners. Results: The mean values of CTDIvol , DLP, and ED, varied across the different CT scanners, Regarding abdomen CT, the CTDIvol mean values were (5.56 mGy to 18.655 mGy) , the DLP (289.78 to 968.241 mGy/cm), and mean effective doses were (4.34 to 14.5 mSv), in chest CTs the mean values of CTDIvol were (5.05 to 14.39mGy), the DLP were (202.84 to 499.098mGy/cm), and median effective doses were (2.85 to 6.97 mSv), in head CTs the mean values of CTDIvol were (42.4to 68.278mGy), the DLP were (781 to 1209.18mGy/cm), and effective doses were (1.636 to 2.528mSv). Conclusion: Regarding selected exams, CTDI, DLP and ED were found to be varied from low variations in head CT scan, to medium in chest CT scan and high variation in abdomen CTs among different CT scanners, The highest values of CTDI and DLP were noted in head CTs followed by abdomen, and the lowest were in chest CTs. The most factors affected the CT dose variations were the technical parameters settings, mainly the number of slices, however the scanner specifications reported some impact. Optimizing dose to a reliable standard for each anatomical part should be adjusting independently by each department according to the scanner characteristics, settings and patient factors.

Sclerosing Perivascular epithelioid cell neoplasms (PEComa), a rare variant in a unique family of tumors

Abstract Introduction/Objective Perivascular epithelioid cell neoplasms (PEComas) are a rare group of neoplasms that characteristically show both smooth muscle and melanocytic differentiation. The most common tumor in this family is renal angiomyolipoma but may occur in various sites. Sclerosing PEComa (S-PEComa) is an extremely rare variant. its only reported in females and it is usually an incidental finding in the retroperitoneum, in close proximity to the kidney. Methods/Case Report . We present a 52-year-old female, who had a chest CT scan for worsening cough. An - 4 cm exophytic renal mass in the left renal upper pole was found. Patient underwent left partial nephrectomy. Grossly the specimen consisted of 2.5 cm well-circumscribed, white tan mass, abutting the renal capsule, and extending into perinephric adipose tissue. Histology revealed uniform bland epithelioid cells with palely eosinophilic cytoplasm and round nuclei embedded in abundant densely sclerotic stroma. No fat was identified. Immunohistochemistry showed the tumor to be positive for desmin, HMB-45 and SMA with focal staining for AE1/AE3 and negative staining for EMA, cam 5.2, CD 117, calretinin, CD 34, S-100, PAX 8, and Melan A, which confirming the diagnosis of S-PEComa. Results (if a Case Study enter NA) NA Conclusion PEComas, excluding the epithelioid variant, usually has a benign course. In the 13 cases previously reported S-PEComa, 2 patients had metastasis. Our patient, 3-month post-op, has no evidence of disease, but close follow up still warranted.

Backlogs in formal interpretation of radiology examinations: a pilot global survey

ObjectiveAnecdotal reports from imaging facilities globally suggest growing radiology interpretation reporting delays. This pilot study's primary aim was to estimate the backlog of formal interpretation of imaging examinations. MethodsAn online survey was distributed to radiologists globally to gather practice-specific characteristics, imaging volumes, and reporting for 3 types of examinations (brain/head CT scans, chest CT scans, and chest radiographs) at 4 time points: 7, 30, 90 days, and 6 months. ResultsWe received responses from 49 radiologists in 16 countries on six continents. Unreported examinations (backlog) were present in thirty of 44 (68%) facilities. Backlogs for brain/head CT, chest CT, and chest radiographs were present in, respectively, 48%, 50%, and 59% of facilities at 7 days and 20%, 23%, and 32% of facilities at 6 months. When present, the mean proportion of backlog (range) at 7 days was 17% (1 to 96) for brain/head CT, 18% (3 to 82) for chest CT, and 22% (1 to 99) for chest radiographs. ConclusionsOur findings from this pilot study show a widespread global backlog in reporting common imaging examinations, and further research is needed on the issue and contributing factors.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening.

Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry. We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD. Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001). Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Three-Dimensional Imaging-Guided Lung Anatomic Segmentectomy: A Single-Center Preliminary Experiment.

Background and objectives: VATS segmentectomy has been proven to be effective in the treatment of stage I NSCLC, but its technical complexity remains one of the most challenging aspects for thoracic surgeons. Furthermore, 3D-CT reconstruction images can help in planning and performing surgical procedures. In this paper, we present our personal experience of 11 VATS anatomical resections performed after accurate pre-operative planning with 3D reconstructions. Materials and methods: A 3D virtual model of the lungs, airways, and vasculature was obtained, starting from a 1.25 mm 3-phase contrast CT scan, and the original images were used for the semi-automatic segmentation of the lung parenchyma, airways, and tumor. Results: Six males and five females were included in this study. The median diameter of the pulmonary lesion at the pre-operative chest CT scan was 20 mm. The surgical indication was confirmed in seven patients: in three cases, a lobectomy, instead of a segmentectomy, was needed due to intraoperative findings of nodal metastasis. Meanwhile, only in one case, we performed a lobectomy because of inadequate surgical resection margins. Skin-to-skin operative average time was 142 (IQR 1-3 105-182.5) min. The median post-operative stay was 6 (IQR 1-3 3.5-7) days. The mean value of the closest surgical margin was 13.7 mm. Conclusion: Image-guided reconstructions are a useful tool for surgeons to perform complex resections in order to spare healthy parenchyma and to ensure disease-free margins. Nevertheless, human skill and surgeon experience still remain fundamental for the final decisions regarding the proper resection to perform.