SMARCA4-deficient non-small cell lung carcinoma: clinicodemographic, computed tomography, and positron emission tomography-computed tomography features.

Background: SMARCA4-deficient non-small cell lung carcinoma (SD-NSCLC) is a relatively rare tumor, which occurs in 5-10% of NSCLC. Based on World Health Organization thoracic tumor classification system, SMARCA4-deficient undifferentiated tumor (SD-UT) is recognized as a separate entity from SD-NSCLC. Differentiation between SD-NSCLC and SD-UT is often difficult due to shared biological continuum, but often required for choosing appropriate treatment regimen. Therefore, the aim of our study was to identify the clinicopathologic, computed tomography (CT), and positron emission tomography (PET)-CT imaging features of SD-NSCLC. Methods: Nine patients of pathologically confirmed SD-NSCLC were included in our analysis. We reviewed electronic medical records for clinical information, demographic features, CT, and PET-CT imaging features were analyzed. Results: Smoking history and male predominance are observed in all patients with SD-NSCLC (n=9). On CT, SD-NSCLC appeared as relatively well-defined masses with lobulated contour (n=8) and peripheral location (n=7). Invasion of adjacent pleura or chest wall (n=7) were frequently observed, regardless of small tumor size. Four cases showed lymph node metastases. Among nine patients, three patients showed multiple bone metastases, and one patient showed lung-to-lung metastases. Conclusions: In patient with SD-NSCLC, there was tendency for male smokers, peripheral location and invasion of adjacent pleural or chest wall invasion regardless of small tumor size, when compared to SD-UT.

Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma.

Purpose: To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods: A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results: For the total patient group, the AUC of the 'total significant features model' (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the 'selected feature model' (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the 'selected feature model' (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion: Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

Differentiation Between Invasive Adenocarcinoma and Focal Interstitial Fibrosis among Persistent Pulmonary Part-solid Nodules: With Emphasis on the CT Morphologic Analysis.

PURPOSE: Focal interstitial fibrosis (FIF) manifesting as a persistent part-solid nodule (PSN) has been mistakenly treated surgically due to similar imaging features to invasive adenocarcinoma (ADC). The purpose of this study was to observe predictive imaging features correlated with FIF through CT morphologic analysis. MATERIALS AND METHODS: From January 2009 to December 2020, 44 patients with surgically proven FIF in a single institution were enrolled and compared with 88 ADC patients through propensity score matching. Patient characteristics and CT morphologic analysis of persistent PSNs were used to identify predictive imaging features of FIF. Receiver operating characteristic (ROC) curve analysis was used to quantify the performance of imaging features. RESULTS: A total of 132 patients with 132 PSNs (44 FIF, 88 ADC; mean age, 67.7±7.58; 75 females) were involved in our analysis. Multivariable analysis demonstrated that preserved peritumoral vascular margin (preserved vascular margin), preserved secondary pulmonary lobule margin (preserved lobular margin), and lower coronal to axial ratio (C/A ratio; cutoff: 1.005) were significant independent predictors of FIF (P<0.05). ROC curve analysis to evaluate the predictive value of the logistic model based on the imaging features of FIF, and the AUC value was 0.881. CONCLUSION: CT imaging features of preserved vascular margin, preserved lobular margin, and lower C/A ratio (cutoff, <1.005) might be helpful imaging features in discriminating FIF over ADC among persistent PSN in clinical practice.

AI-assisted Analysis to Facilitate Detection of Humeral Lesions on Chest Radiographs.

Purpose To develop an artificial intelligence (AI) system for humeral tumor detection on chest radiographs (CRs) and evaluate the impact on reader performance. Materials and Methods In this retrospective study, 14 709 CRs (January 2000 to December 2021) were collected from 13 468 patients, including CT-proven normal (n = 13 116) and humeral tumor (n = 1593) cases. The data were divided into training and test groups. A novel training method called false-positive activation area reduction (FPAR) was introduced to enhance the diagnostic performance by focusing on the humeral region. The AI program and 10 radiologists were assessed using holdout test set 1, wherein the radiologists were tested twice (with and without AI test results). The performance of the AI system was evaluated using holdout test set 2, comprising 10 497 normal images. Receiver operating characteristic analyses were conducted for evaluating model performance. Results FPAR application in the AI program improved its performance compared with a conventional model based on the area under the receiver operating characteristic curve (0.87 vs 0.82, P = .04). The proposed AI system also demonstrated improved tumor localization accuracy (80% vs 57%, P < .001). In holdout test set 2, the proposed AI system exhibited a false-positive rate of 2%. AI assistance improved the radiologists' sensitivity, specificity, and accuracy by 8.9%, 1.2%, and 3.5%, respectively (P < .05 for all). Conclusion The proposed AI tool incorporating FPAR improved humeral tumor detection on CRs and reduced false-positive results in tumor visualization. It may serve as a supportive diagnostic tool to alert radiologists about humeral abnormalities. Keywords: Artificial Intelligence, Conventional Radiography, Humerus, Machine Learning, Shoulder, Tumor Supplemental material is available for this article. © RSNA, 2024.

CT evaluation of systemic artery to pulmonary artery fistula: an underdiagnosed disease in patients with hemoptysis.

Background: Systemic artery to pulmonary artery fistula (SA-PAF) is an uncommon disease which is often incidentally diagnosed during evaluation of hemoptysis patients. The aim of our study was to describe the cases of SA-PAF in our institution and to report the correlating clinical and radiological findings. Methods: We reviewed 231 chest computed tomography (CT) scans performed in our institution due to hemoptysis from January 2020 to February 2023. In patients diagnosed with SA-PAF had their electronic medical records and CT images analyzed. Results: In 231 patients, 19 (8.2%) of them had SA-PAF findings which was characterized by a peripheral nodular soft tissue opacity in the subpleural lung and traceable vascular structure in continuity with one or more peripheral pulmonary artery branches in CT. Etiology of each patient was categorized as either congenital (7, 36.8%), and acquired (12, 63.2%). The origins of SA-PAFs were 16 intercostal, two anterior mediastinal, and one costocervical artery. Eight of 19 patients did not show any associated intralobar imaging abnormalities, while bronchiectasis, cellular bronchiolitis, centrilobular emphysema, and pleura effusion were observed in 11 patients. Conclusions: SA-PAF is a benign vascular anomaly which is frequently overlooked when evaluating hemoptysis by either clinician or radiologists but is an important factor in the differential diagnosis of patients with hemoptysis.

Automated precision localization of peripherally inserted central catheter tip through model-agnostic multi-stage networks.

BACKGROUND: Peripherally inserted central catheters (PICCs) have been widely used as one of the representative central venous lines (CVCs) due to their long-term intravascular access with low infectivity. However, PICCs have a fatal drawback of a high frequency of tip mispositions, increasing the risk of puncture, embolism, and complications such as cardiac arrhythmias. To automatically and precisely detect it, various attempts have been made by using the latest deep learning (DL) technologies. However, even with these approaches, it is still practically difficult to determine the tip location because the multiple fragments phenomenon (MFP) occurs in the process of predicting and extracting the PICC line required before predicting the tip. OBJECTIVE: This study aimed to develop a system generally applied to existing models and to restore the PICC line more exactly by removing the MFs of the model output, thereby precisely localizing the actual tip position for detecting its misposition. METHODS: To achieve this, we proposed a multi-stage DL-based framework post-processing the PICC line extraction result of the existing technology. Our method consists of the following three stages: 1. Existing PICC line segmentation network for a baseline, 2. Patch-based PICC line refinement network, 3. PICC line reconnection network. The proposed second and third-stage models address MFs caused by the sparseness of the PICC line and the line disconnection due to confusion with anatomical structures respectively, thereby enhancing tip detection. RESULTS: To verify the objective performance of the proposed MFCN, internal validation and external validation were conducted. For internal validation, learning (130 samples) and verification (150 samples) were performed with 280 data, including PICC among Chest X-ray (CXR) images taken at our institution. External validation was conducted using a public dataset called the Royal Australian and New Zealand College of Radiologists (RANZCR), and training (130 samples) and validation (150 samples) were performed with 280 data of CXR images, including PICC, which has the same number as that for internal validation. The performance was compared by root mean squared error (RMSE) and the ratio of single fragment images (RatioSFI) (i.e., the rate at which model predicts PICC as multiple sub-lines) according to whether or not MFCN is applied to seven conventional models (i.e., FCDN, UNET, AUNET, TUNET, FCDN-HT, UNET-ELL, and UNET-RPN). In internal validation, when MFCN was applied to the existing single model, MFP was improved by an average of 45 %. The RMSE improved over 63% from an average of 27.54 mm (17.16 to 35.80 mm) to 9.77 mm (9.11 to 10.98 mm). In external validation, when MFCN was applied, the MFP incidence rate decreased by an average of 32% and the RMSE decreased by an average of 65%. Therefore, by applying the proposed MFCN, we observed the consistent detection performance improvement of PICC tip location compared to the existing model. CONCLUSION: In this study, we applied the proposed technique to the existing technique and demonstrated that it provides high tip detection performance, proving its high versatility and superiority. Therefore, we believe, in countries and regions where radiologists are scarce, that the proposed DL approach will be able to effectively detect PICC misposition on behalf of radiologists.

Placental transmogrification of the lung: CT findings with pathologic correlation in six patients.

Background: Placental transmogrification of the lung is a very rare benign lung disease with a characteristic finding being alveoli resembling chorionic villi of the placenta. The purpose of this study was to assess the computed tomography (CT) findings of placental transmogrification of the lung in six patients and their relation to the histopathologic findings. Methods: Six patients with histopathologically proven placental transmogrification of the lung from 2004 to 2021 were included. Their CT findings were analyzed and their imaging features were compared with pathology specimens. Results: In four of six cases, CT showed variable sized cystic lesions confined to a unilateral lung. One case presented nodule and cystic lesion together. The other case showed solitary pulmonary nodule without cystic lesion. Moreover, nodular interlobular septal thickening and clustered interstitial nodules were observed in all six cases. In four of the six cases, these nodules merged into dense nodular consolidation. Three cases showed dilated pulmonary vasculatures of the involved lung. Conclusions: On CT, placental transmogrification of the lung typically presents as cystic lesion confined to a unilateral lung. Pulmonary nodule with or without associated cystic lesion can also be seen. Nodular interlobular septal thickening and clustered interstitial nodules were observed in all cases. This might be attributable to the proliferation of chorionic villi-like structures in interstitium which are found in histopathologic specimens.

CT-Derived Deep Learning-Based Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease.

Purpose: Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD). Materials and Methods: A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRMemph), PRM-derived functional small airway disease (PRMfSAD), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson's correlation analysis. Results: The volume and area of muscle and subcutaneous fat were negatively associated with PRMemph and PRMfSAD (p < 0.05). Bone density at T12 was negatively associated with PRMemph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001). Conclusion: Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.

Application of Machine Learning Algorithm in Predicting Axillary Lymph Node Metastasis from Breast Cancer on Preoperative Chest CT.

Axillary lymph node (ALN) status is one of the most critical prognostic factors in patients with breast cancer. However, ALN evaluation with contrast-enhanced CT (CECT) has been challenging. Machine learning (ML) is known to show excellent performance in image recognition tasks. The purpose of our study was to evaluate the performance of the ML algorithm for predicting ALN metastasis by combining preoperative CECT features of both ALN and primary tumor. This was a retrospective single-institutional study of a total of 266 patients with breast cancer who underwent preoperative chest CECT. Random forest (RF), extreme gradient boosting (XGBoost), and neural network (NN) algorithms were used. Statistical analysis and recursive feature elimination (RFE) were adopted as feature selection for ML. The best ML-based ALN prediction model for breast cancer was NN with RFE, which achieved an AUROC of 0.76 ± 0.11 and an accuracy of 0.74 ± 0.12. By comparing NN with RFE model performance with and without ALN features from CECT, NN with RFE model with ALN features showed better performance at all performance evaluations, which indicated the effect of ALN features. Through our study, we were able to demonstrate that the ML algorithm could effectively predict the final diagnosis of ALN metastases from CECT images of the primary tumor and ALN. This suggests that ML has the potential to differentiate between benign and malignant ALNs.

The impact of deep learning reconstruction in low dose computed tomography on the evaluation of interstitial lung disease.

To evaluate the effect of the deep learning model reconstruction (DLM) method in terms of image quality and diagnostic agreement in low-dose computed tomography (LDCT) for interstitial lung disease (ILD), 193 patients who underwent LDCT for suspected ILD were retrospectively reviewed. Datasets were reconstructed using filtered back projection (FBP), adaptive statistical iterative reconstruction Veo (ASiR-V), and DLM. For image quality analysis, the signal, noise, signal-to-noise ratio (SNR), blind/referenceless image spatial quality evaluator (BRISQUE), and visual scoring were evaluated. Also, CT patterns of usual interstitial pneumonia (UIP) were classified according to the 2022 idiopathic pulmonary fibrosis (IPF) diagnostic criteria. The differences between CT images subjected to FBP, ASiR-V 30%, and DLM were evaluated. The image noise and BRISQUE scores of DLM images was lower and SNR was higher than that of the ASiR-V and FBP images (ASiR-V vs. DLM, p < 0.001 and FBP vs. DLR-M, p < 0.001, respectively). The agreement of the diagnostic categorization of IPF between the three reconstruction methods was almost perfect (κ = 0.992, CI 0.990-0.994). Image quality was improved with DLM compared to ASiR-V and FBP.

Estimating the Volume of Nodules and Masses on Serial Chest Radiography Using a Deep-Learning-Based Automatic Detection Algorithm: A Preliminary Study.

BACKGROUND: The purpose of this study was to assess the volume of the pulmonary nodules and masses on serial chest X-rays (CXRs) from deep-learning-based automatic detection algorithm (DLAD)-based parameters. METHODS: In a retrospective single-institutional study, 72 patients, who obtained serial CXRs (n = 147) for pulmonary nodules or masses with corresponding chest CT images as the reference standards, were included. A pre-trained DLAD based on a convolutional neural network was developed to detect and localize nodules using 13,710 radiographs and to calculate a localization map and the derived parameters (e.g., the area and mean probability value of pulmonary nodules) for each CXR, including serial follow-ups. For validation, reference 3D CT volumes were measured semi-automatically. Volume prediction models for pulmonary nodules were established through univariable or multivariable, and linear or non-linear regression analyses with the parameters. A polynomial regression analysis was performed as a method of a non-linear regression model. RESULTS: Of the 147 CXRs and 208 nodules of 72 patients, the mean volume of nodules or masses was measured as 9.37 ± 11.69 cm3 (mean ± standard deviation). The area and CT volume demonstrated a linear correlation of moderate strength (i.e., R = 0.58, RMSE: 9449.9 mm3 m3 in a linear regression analysis). The area and mean probability values exhibited a strong linear correlation (R = 0.73). The volume prediction performance based on a multivariable regression model was best with a mean probability and unit-adjusted area (i.e. , RMSE: 7975.6 mm3, the smallest among the other variable parameters). CONCLUSIONS: The prediction model with the area and the mean probability based on the DLAD showed a rather accurate quantitative estimation of pulmonary nodule or mass volume and the change in serial CXRs.

Missed and Detected Incidental Breast Cancers on Contrast Enhanced Chest CT: Detection Rates and CT Features.

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of "breast cancer" between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3-18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.3%) and detected in 30 (53.7%). The initial treatment was delayed in the missed breast lesions group (p = 0.004). Breast lesions of smaller sizes (<9.0 mm, p = 0.01), or with lower enhancement ratios (<1.4, p = 0.009), were more likely to be missed. When three radiologists re-read the CTs with more attention to breast area, they detected breast cancers with higher accuracies (90.1%, 87.9%, and 81.3%). In summary, this study revealed that radiologists miss 64.3% of incidental breast cancers on chest CT, especially those of sub-centimeter sizes and weak enhancements.

Completeness of Reporting of Systematic Reviews and Meta-Analysis of Diagnostic Test Accuracy (DTA) of Radiological Articles Based on the PRISMA-DTA Reporting Guideline.

RATIONALE AND OBJECTIVES: This study evaluated the completeness of systematic reviews and meta-analyses in radiology using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Diagnostic Test Accuracy (PRISMA-DTA) and PRISMA-DTA for Abstracts guidelines between articles published before and those published after the issuance of the guideline and identify areas that have been poorly reported. MATERIALS AND METHODS: PubMed were searched for systematic reviews on DTA with or without meta-analyses published in general radiology journals between January 1, 2016 and December 31, 2020. The identified articles were assessed for completeness of reporting according to the PRISMA-DTA. Subgroup analyses were performed for association of completeness of reporting with multiple cofactors. RESULTS: The search identified 183 reviews from 12 journals. The mean numbers (standard deviation) of reported PRISMA-DTA and PRISMA-DTA for Abstracts items in the full texts and abstracts were 18.45 (2.02) and 5.66 (1.28), respectively. Subgroup analysis showed that compared to the corresponding reference groups, a higher mean number of reported PRISMA-DTA items was associated with publication during July 2018-December 2020 [(17.82 (2.01) vs 18.89 (1.91); p = 0.034), citation of the PRISMA-DTA [17.62 (1.86) vs 20.27 (2.02); p < 0.001], and inclusion of supplementary materials [17.64 (2) vs 19.09 (1.8); p < 0.001] on multiple-linear regression analysis. CONCLUSION: Completeness of reporting with respect to the PRISMA-DTA and PRISMA-DTA for Abstracts has improved modestly since the publication of the PRISMA-DTA guideline; however, increasing awareness of the specific weakness provides the chance for completeness improvement.

Left Atrial Appendage Aneurysm: A Case Report.

Left atrial appendage aneurysm (LAAA) is a rare heart anomaly caused by congenital dysplasia of the pectinate muscle or by an acquired pathological condition of the mitral valve or cardiac muscle. It is often incidentally discovered during chest CT or echocardiography as an abnormal dilatation of the LAA. LAAA is associated with life-threatening complications and most patients require surgical treatment. Therefore, it is important to evaluate associated complications as well as precise diagnoses. This report presents the case of a surgically confirmed LAAA in a 53-year-old female. We also discuss the pathophysiology of LAAA and significant findings related to mortality that can be detected on CT and MRI.

A Pictorial Review of Radiologic Findings of Foreign Bodies in the Thorax.

Thoracic foreign bodies (FBs) are serious and relatively frequent in emergency departments. Thoracic FBs may occur in association with aspiration, ingestion, trauma, or iatrogenic causes. Imaging plays an important role in the identification of FBs and their dimensions, structures, and locations, before the initiation of interventional treatment. To guide proper clinical management, radiologists should be aware of the radiologic presentations and the consequences of thoracic FBs. In this pictorial essay, we reviewed the optimal imaging settings to identify FBs in the thorax, classified thoracic FBs into four types according to their etiology, and reviewed the characteristic imaging features and the possible complications.

Incidence and risk factors for sternal osteomyelitis after median sternotomy.

Background: Sternal osteomyelitis (OM) after median sternotomy is the rarest form of deep sternal wound infections (DSWIs). A retrospective study was implemented to evaluate the incidence and potential risk factors of sternal OM after median sternotomy. Methods: We analyzed 3,410 consecutive patients who underwent cardiothoracic surgery via median sternotomy from January 2005 to December 2019 at our institution. A sternal OM and control group without any sign of wound infections after median sternotomy were selected. Comparisons of the variables between the two groups were performed using the Student's t-test and Fisher's exact tests. The association of potential risk factors with sternal OM was tested by logistic regression analysis. Results: A total of 16 patients (0.47%) had sternal OM after median sternotomy. None of the variables were different between the sternal OM patients and the control group including body mass index (BMI), diabetes mellitus (DM), hypertension (HTN), left ventricle (LV) function, transfusion, operation time, cardiopulmonary bypass (CPB) time and intensive care unit and ventilator days. By univariate analysis, none of the variables were associated with an increased risk of sternal OM. Conclusions: The incidence of sternal OM after median sternotomy in our institution was 0.47% and there was no correlation between the known risk factors of DSWI and sternal OM in our study.

CT findings of basaloid squamous cell carcinoma of the lung in 12 patients: A distinct category of squamous cell carcinoma in 2015 WHO classification of lung tumors.

ABSTRACT: Basaloid squamous cell carcinoma (SCC) is very rare subtype of SCC of the lung and it is important to distinguish basaloid to other subtypes of SCCs, since the prognosis of basaloid subtype is considered poorer than that of other non-basaloid subtypes of SCCs. Aim of this study was to assess computed tomography (CT) findings of basaloid SCC of the lung in 12 patients.From January 2016 to April 2021, 12 patients with surgically proven basaloid SCC of the lung were identified. CT findings were analyzed, and the imaging features were compared with histopathologic reports. Clinical and demographic features were also analyzed.Axial location of the tumor was central in 5 patients, while 7 was in peripheral. Of the 7 patients whose tumors were located in the peripheral, margin of the tumor were smooth (n  = 2), lobulated (n  = 2), or spiculated (n  = 3). After contrast injection, net enhancement value ranged from 15.8 to 71.8 HU (median, 36.4 HU). Endobronchial growth were seen in 5 patients and these patients accompanied obstructive pneumonia or atelectasis. Internal profuse necrosis, cavitation, or calcifications were not seen.On CT, basaloid squamous cell presents as solitary nodule or mass with moderate enhancement. Tumor was located either peripheral or central compartment of the lung and cavitation was absent.

Computed tomography-based visual assessment of chronic obstructive pulmonary disease: comparison with pulmonary function test and quantitative computed tomography.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) has variable subtypes involving mixture of large airway inflammation, small airway disease, and emphysema. This study evaluated the relationship between visually assessed computed tomography (CT) subtypes and clinical/imaging characteristics. METHODS: In total, 452 participants were enrolled in this study between 2012 and 2017. Seven subtypes were defined by visual evaluation of CT images using Fleischner Society classification: normal, paraseptal emphysema (PSE), bronchial disease, and centrilobular emphysema (trace, mild, moderate and confluent/advanced destructive). The differences in several variables, including clinical, laboratory, spirometric, and quantitative CT features among CT-based visual subtypes, were compared using the chi-square tests and one-way analysis of variance. RESULTS: Subjects who had PSE had better forced expiratory volume in 1 second (FEV1) (P=0.03) percentage and higher lung density (P<0.05) than those with moderate to confluent/advanced destructive centrilobular emphysema. As the visual grade of centrilobular emphysema worsened, pulmonary function declined and modified Medical Research Council, COPD assessment test (CAT) score, and quantitative assessment (emphysema index and air trapping) increased. The bronchial subtype was associated with higher body mass index (BMI), better lung function and higher lung density. Participants with trace emphysema showed a rapid increase in functional small airway disease. CONCLUSIONS: Classifying subtypes using visual CT imaging features can reflect heterogeneity and pathological processes of COPD.

Validation of Deep-Learning Image Reconstruction for Low-Dose Chest Computed Tomography Scan: Emphasis on Image Quality and Noise.

OBJECTIVE: Iterative reconstruction degrades image quality. Thus, further advances in image reconstruction are necessary to overcome some limitations of this technique in low-dose computed tomography (LDCT) scan of the chest. Deep-learning image reconstruction (DLIR) is a new method used to reduce dose while maintaining image quality. The purposes of this study was to evaluate image quality and noise of LDCT scan images reconstructed with DLIR and compare with those of images reconstructed with the adaptive statistical iterative reconstruction-Veo at a level of 30% (ASiR-V 30%). MATERIALS AND METHODS: This retrospective study included 58 patients who underwent LDCT scan for lung cancer screening. Datasets were reconstructed with ASiR-V 30% and DLIR at medium and high levels (DLIR-M and DLIR-H, respectively). The objective image signal and noise, which represented mean attenuation value and standard deviation in Hounsfield units for the lungs, mediastinum, liver, and background air, and subjective image contrast, image noise, and conspicuity of structures were evaluated. The differences between CT scan images subjected to ASiR-V 30%, DLIR-M, and DLIR-H were evaluated. RESULTS: Based on the objective analysis, the image signals did not significantly differ among ASiR-V 30%, DLIR-M, and DLIR-H (p = 0.949, 0.737, 0.366, and 0.358 in the lungs, mediastinum, liver, and background air, respectively). However, the noise was significantly lower in DLIR-M and DLIR-H than in ASiR-V 30% (all p < 0.001). DLIR had higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) than ASiR-V 30% (p = 0.027, < 0.001, and < 0.001 in the SNR of the lungs, mediastinum, and liver, respectively; all p < 0.001 in the CNR). According to the subjective analysis, DLIR had higher image contrast and lower image noise than ASiR-V 30% (all p < 0.001). DLIR was superior to ASiR-V 30% in identifying the pulmonary arteries and veins, trachea and bronchi, lymph nodes, and pleura and pericardium (all p < 0.001). CONCLUSION: DLIR significantly reduced the image noise in chest LDCT scan images compared with ASiR-V 30% while maintaining superior image quality.

Computed tomographic findings of chest injuries following cardiopulmonary resuscitation: More complications for prolonged chest compressions?

Chest injuries are common and inevitable complications of chest compressions during cardiopulmonary resuscitation (CPR). This study aimed to investigate lung parenchymal and thoracic skeletal injuries after CPR by using computed tomography (CT) and to analyze the correlation between the duration of CPR and related complications.We examined 43 non-traumatic cardiac arrest patients who were successfully resuscitated after CPR and had chest CT scans within 24 hours of CPR. Lung parenchymal injuries were assessed by quantifying the lung contusion score (LCS) on the CT images, and each skeletal injury was investigated by classifying the location and the distribution. Other CPR-related chest injuries were also described, such as pleural effusion/hemothorax, pneumothorax, and retrosternal hematoma. Statistical analysis was conducted to determine whether the duration of CPR was correlated with each complication.Lung contusions were found in all of the patients (mean LCS: 22, range: 5-47). The distribution of lung contusions were predominantly in the bilateral dependent portions of the lungs (41 patients). All of the rib fractures occurred in the anterior arc (43 patients), and the sternal fractures occurred predominantly in the mid-sternal body (31 patients). In patients younger than 70 years old, the number of rib fractures significantly increased among those who underwent CPR for more than 25 minutes compared to those who received CPR for less than 25 minutes (median 4.5 vs 9; mean 8.3 vs 5.6 per person, respectively; P = .035). The risk of sternal fracture tended to be higher for patients who received CPR for more than 10 minutes compared to those who received CPR for less than 10 minutes (odds ratio: 3.60; 95% confidence interval: 0.86-15.06; P = .079). However, there was no statistically significant correlation between the duration of CPR and LCS or other CPR-related chest injuries.The duration of CPR was associated with the number of rib fractures and the occurrence of sternal fractures, but it did not affect the extent of CPR-related lung contusions or other CPR-related chest injuries. All of the rib fractures occurred in the anterior arc, while the sternal fractures occurred predominantly in the mid-sternal body. However, since this study was conducted in a single institution, the number of patients included was relatively small, thus limiting the statistical analysis.