Molecular Dynamics Simulation Study of the Selective Inhibition of Coagulation Factor IXa over Factor Xa.

Thromboembolic disorders, arising from abnormal coagulation, pose a significant risk to human life in the modern world. The FDA has recently approved several anticoagulant drugs targeting factor Xa (FXa) to manage these disorders. However, these drugs have potential side effects, leading to bleeding complications in patients. To mitigate these risks, coagulation factor IXa (FIXa) has emerged as a promising target due to its selective regulation of the intrinsic pathway. Due to the high structural and functional similarities of these coagulation factors and their inhibitor binding modes, designing a selective inhibitor specifically targeting FIXa remains a challenging task. The dynamic behavior of protein-ligand interactions and their impact on selectivity were analyzed using molecular dynamics simulation, considering the availability of potent and selective compounds for both coagulation factors and the co-crystal structures of protein-ligand complexes. Throughout the simulations, we examined ligand movements in the binding site, as well as the contact frequencies and interaction fingerprints, to gain insights into selectivity. Interaction fingerprint (IFP) analysis clearly highlights the crucial role of strong H-bond formation between the ligand and D189 and A190 in the S1 subsite for FIXa selectivity, consistent with our previous study. This dynamic analysis also reveals additional FIXa-specific interactions. Additionally, the absence of polar interactions contributes to the selectivity for FXa, as observed from the dynamic profile of interactions. A contact frequency analysis of the protein-ligand complexes provides further confirmation of the selectivity criteria for FIXa and FXa, as well as criteria for binding and activity. Moreover, a ligand movement analysis reveals key interaction dynamics that highlight the tighter binding of selective ligands to the proteins compared to non-selective and inactive ligands.

Deep learning-based image reconstruction for brain CT: improved image quality compared with adaptive statistical iterative reconstruction-Veo (ASIR-V).

PURPOSE: To compare the image quality of brain computed tomography (CT) images reconstructed with deep learning-based image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: Sixty-two patients underwent routine noncontrast brain CT scans and datasets were reconstructed with 30% ASIR-V and DLIR with three selectable reconstruction strength levels (low, medium, high). Objective parameters including CT attenuation, noise, noise reduction rate, artifact index of the posterior cranial fossa, and contrast-to-noise ratio (CNR) were measured at the levels of the centrum semiovale and basal ganglia. Subjective parameters including gray matter-white matter differentiation, sharpness, and overall diagnostic quality were also assessed and compared with the interobserver agreement. RESULTS: There was a gradual reduction in the image noise and artifact index of the posterior cranial fossa as the strength levels of DLIR increased (all P < 0.001) compared with that of ASIR-V. CNR in both the centrum semiovale and basal ganglia levels also improved from the low to high strength levels of DLIR compared with that of ASIR-V (all P < 0.001). DLIR images with medium and high strength levels demonstrated the best subjective image quality scores among the reconstruction datasets. There was moderate to good interobserver agreement for the subjective image quality assessments with ASIR-V and DLIR. CONCLUSION: On routine brain CT scans, optimized protocols with DLIR allowed significant reduction of noise and artifacts with improved subjective image quality compared with ASIR-V.

Imaging findings of Stenotrophomonas maltophilia pneumonia: emphasis on CT findings between immunocompromised and immunocompetent patients.

BACKGROUND: Stenotrophomonas maltophilia (S. maltophilia) is a globally emerging, rare, waterborne, aerobic, gram-negative, multiple-drug-resistant organism, most commonly associated with respiratory tract infection in humans. Computed tomography (CT) findings in patients with S. maltophilia pneumonia are rarely reported. PURPOSE: To compare CT findings between immunocompromised and immunocompetent patients, and to determine characteristic imaging findings of S. maltophilia pneumonia. MATERIAL AND METHODS: CT findings of eight immunocompromised and 29 immunocompetent patients with proven S. maltophilia pneumonia were reviewed retrospectively. Different patterns of CT abnormalities between immunocompromised and immunocompetent patients were compared by Fisher's exact test. RESULTS: Patchy ground-glass opacities (GGOs) were the most common CT findings, present in 36 (97.3%) of the 37 patients. Among the patients with patchy GGOs, consolidation was seen in 29 (78.4%) patients, and centrilobular nodules were noted in 15 (40.5%) patients. The transaxial distribution of the parenchymal abnormalities was predominantly randomly distributed in 30 (81.1%) cases. Regarding longitudinal plane involvement, the predominant zonal distributions were the diffuse distribution (n=23, 62.2%) and the lower lung zone (n=14, 37.8%). None of the patients showed upper lung zone predominance. The proportion of patients with parenchymal CT findings or associated findings in the immunocompromised patients was not significantly different from that of the immunocompetent patients. However, lower lung zone predominance on the longitudinal plane was significantly more common in immunocompetent patients than in immunocompromised patients (14/29 vs. 0/8, P=0.015). And diffuse distribution of parenchymal abnormalities on a longitudinal plane was significantly more frequent in immunocompromised patients than in immunocompetent patients (8/8 vs. 15/29, P=0.015). CONCLUSION: The most common CT patterns of S. maltophilia pneumonia in immunocompromised and immunocompetent patients were patchy GGOs and consolidation. However, in immunocompetent patients, parenchymal abnormalities were more predominately distributed in lower lung zone than in immunocompromised patients; and in immunocompromised patients, parenchymal abnormalities were more diffusely distributed than in immunocompetent patients.

Differential diagnosis of oligodendroglial and astrocytic tumors using imaging results: the added value of perfusion MR imaging.

PURPOSE: The purposes of the present study are to assess whether different characteristics of oligodendrogliomas and astrocytic tumors are visible on MR imaging and to determine the added value of perfusion imaging in conventional MR imaging when differentiating oligodendrogliomas from astrocytic tumors. METHODS: We retrospectively studied 22 oligodendroglioma and 54 astrocytic tumor patients, including glioblastoma multiforme (GBM). The morphological tumor characteristics were evaluated using MR imaging. The rCBV, K trans, and V e values were recorded. All imaging and clinical values were compared. The ability to discriminate between the two entities was evaluated using receiver operating characteristic curve analyses. Separate comparison analysis between oligodendroglioma and astrocytic tumors excluding GBM was also performed. RESULTS: The presence of calcification, higher cortex involvement ratio, and lower V e value were more representative of oligodendrogliomas than astrocytic tumors (P = <0.001, 0.038, and <0.001, respectively). The area under the curve (AUC) value of a combination of calcification and cortex involvement ratio was 0.796. The combination of all three parameters, including V e, further increased the diagnostic performance (AUC = 0.881). Comparison test of the two AUC areas revealed significant difference (P = 0.0474). The presence of calcification and higher cortex involvement ratio were the only findings suggestive of oligodendrogliomas than astrocytic tumors with exclusion of GBMs (P = 0.014 and <0.001, respectively). CONCLUSION: Cortex involvement ratio and the presence of calcification with V e values were diagnostically accurate in identifying oligodendrogliomas. The V e value calculated from dynamic contrast-enhanced MR imaging could be a supportive tool for differentiating between oligodendrogliomas and astrocytic tumors including GBMs.

Devastating metabolic brain disorders of newborns and young infants.

Metabolic disorders of the brain that manifest in the neonatal or early infantile period are usually associated with acute and severe illness and are thus referred to as devastating metabolic disorders. Most of these disorders may be classified as organic acid disorders, amino acid metabolism disorders, primary lactic acidosis, or fatty acid oxidation disorders. Each disorder has distinctive clinical, biochemical, and radiologic features. Early diagnosis is important both for prompt treatment to prevent death or serious sequelae and for genetic counseling. However, diagnosis is often challenging because many findings overlap and may mimic those of more common neonatal conditions, such as hypoxic-ischemic encephalopathy and infection. Ultrasonography (US) may be an initial screening method for the neonatal brain, and magnetic resonance (MR) imaging is the modality of choice for evaluating metabolic brain disorders. Although nonspecific imaging findings are common in early-onset metabolic disorders, characteristic patterns of brain involvement have been described for several disorders. In addition, diffusion-weighted images may be used to characterize edema during an acute episode of encephalopathy, and MR spectroscopy depicts changes in metabolites that may help diagnose metabolic disorders and assess response to treatment. Imaging findings, including those of advanced MR imaging techniques, must be closely reviewed. If one of these rare disorders is suspected, the appropriate biochemical test or analysis of the specific gene should be performed to confirm the diagnosis.

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.

Intraductal papillary neoplasm of the bile ducts: description of MRI and added value of diffusion-weighted MRI.

PURPOSE: To evaluate MRI features of intraductal papillary neoplasm of the bile duct (IPNB) and to determine added value of diffusion-weighted MRI (DWI). METHODS: Twenty-three patients with surgically confirmed invasive (n = 12) and non-invasive (n = 11) IPNB, who underwent preoperative liver MRI were included. Two observers performed consensus review of gadoxetic acid-enhanced MRI and combined gadoxetic acid-enhanced MRI including DWI separately, with regard to conspicuity of intraductal tumor using five point scales, extent of tumors, and the presence of invasiveness. RESULTS: On MRI, there was no significant difference in the conspicuity of intraductal tumors between gadoxetic acid MRI (mean, 4.35) and combined MRI (mean, 4.65) (P = 0.09). However, addition of DWI led seven cases revealed excellent conspicuity as compared with good or moderate conspicuity on gadoxetic acid MRI. With regard to invasiveness, 11 cases (48 %) and 17 (74 %) were correctly diagnosed with gadoxetic acid MRI and combined MRI, respectively (P = 0.06). In invasive tumors, both of the two image sets did not help assess accurate extent of the tumor. CONCLUSIONS: The addition of DWI to gadoxetic acid-enhanced MRI has a potency to improve conspicuity for intraductal tumors of IPNB and is helpful in determining tumor invasiveness, but not tumor extent.

A multi-kernel and multi-scale learning based deep ensemble model for predicting recurrence of non-small cell lung cancer.

Predicting recurrence in patients with non-small cell lung cancer (NSCLC) before treatment is vital for guiding personalized medicine. Deep learning techniques have revolutionized the application of cancer informatics, including lung cancer time-to-event prediction. Most existing convolutional neural network (CNN) models are based on a single two-dimensional (2D) computational tomography (CT) image or three-dimensional (3D) CT volume. However, studies have shown that using multi-scale input and fusing multiple networks provide promising performance. This study proposes a deep learning-based ensemble network for recurrence prediction using a dataset of 530 patients with NSCLC. This network assembles 2D CNN models of various input slices, scales, and convolutional kernels, using a deep learning-based feature fusion model as an ensemble strategy. The proposed framework is uniquely designed to benefit from (i) multiple 2D in-plane slices to provide more information than a single central slice, (ii) multi-scale networks and multi-kernel networks to capture the local and peritumoral features, (iii) ensemble design to integrate features from various inputs and model architectures for final prediction. The ensemble of five 2D-CNN models, three slices, and two multi-kernel networks, using 5 × 5 and 6 × 6 convolutional kernels, achieved the best performance with an accuracy of 69.62%, area under the curve (AUC) of 72.5%, F1 score of 70.12%, and recall of 70.81%. Furthermore, the proposed method achieved competitive results compared with the 2D and 3D-CNN models for cancer outcome prediction in the benchmark studies. Our model is also a potential adjuvant treatment tool for identifying NSCLC patients with a high risk of recurrence.

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.

Repeat biopsy for mutational analysis of non-small cell lung cancers resistant to previous chemotherapy: adequacy and complications.

PURPOSE: To evaluate the feasibility and safety of repeat biopsy for mutational analysis in patients with non-small cell lung cancer (NSCLC) who have a resistance history to previous chemotherapy. MATERIALS AND METHODS: This prospective study was institutional review board approved, and written informed consent was obtained from all patients. Of 126 patients referred for repeat biopsy (hereafter, rebiopsy) with NSCLC that was resistant to conventional chemotherapy or epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors, 94 patients (31 men, 63 women; mean age ± standard deviation, 57 years ± 10.3) were selected for rebiopsy. Thirty-two patients were excluded for several reasons after strict review of the chest computed tomography (CT) images. Percutaneous transthoracic lung biopsy was performed with C-arm cone-beam CT guidance. The technical success rates for the rebiopsy and the adequacy rates of specimens for mutational analysis were evaluated. Any biopsy-related complications were recorded. RESULTS: The technical success rate for biopsy was 100%. In 75 (80%) of 94 patients, specimens were adequate for mutational analysis. Of 75 specimens, 35 were tested for EGFR mutation, 34 for anaplastic lymphoma kinase gene (ALK) rearrangement, and six for both. The results were positive for EGFR-sensitizing mutation (exon 19 or 21) in 20, for EGFR T790M mutation in five, and for ALK rearrangement in 11. Postprocedural complications occurred in 13 (14%) of 94 patients. CONCLUSION: When performed by employing rigorous CT criteria, rebiopsies for the mutational analysis of NSCLCs treated previously with chemotherapy are feasible in all patients and are adequate in approximately four-fifths of patients referred for gene analysis, with acceptable rates of complications.

Bell-Evans model and steered molecular dynamics in uncovering the dissociation kinetics of ligands targeting G-protein-coupled receptors.

Recently, academic and industrial scientific communities involved in kinetics-based drug development have become immensely interested in predicting the drug target residence time. Screening drug candidates in terms of their computationally predicted residence times, which is a measure of drug efficacy in vivo, and simultaneously assessing computational binding affinities are becoming inevitable. Non-equilibrium molecular simulation approaches are proven to be useful in this purpose. Here, we have implemented an optimized approach of combining the data derived from steered molecular dynamics simulations and the Bell-Evans model to predict the absolute residence times of the antagonist ZMA241385 and agonist NECA that target the A2A adenosine receptor of the G-protein-coupled receptor (GPCR) protein family. We have predicted the absolute ligand residence times on the timescale of seconds. However, our predictions were many folds shorter than those determined experimentally. Additionally, we calculated the thermodynamics of ligand binding in terms of ligand binding energies and the per-residue contribution of the receptor. Subsequently, binding pocket hotspot residues that would be important for further computational mutagenesis studies were identified. In the experiment, similar sets of residues were found to be in significant contact with both ligands under study. Our results build a strong foundation for further improvement of our approach by rationalizing the kinetics of ligand unbinding with the thermodynamics of ligand binding.

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.

Recurrence dynamics after curative surgery in patients with invasive mucinous adenocarcinoma of the lung.

BACKGROUND: We investigated the patterns and timing of recurrence and death as well as prognostic factors based on clinicopathological and radiological factors in patients who underwent surgical treatment for invasive mucinous adenocarcinoma (IMA). METHODS: We reviewed clinicopathological findings including spread through air spaces (STAS) and CT findings of IMA such as morphology, solidity, margin, well-defined heterogeneous ground-glass opacity, CT angiogram, and air bronchogram signs from 121 consecutive patients who underwent surgical resection. Prognostic factors for disease-free survival (DFS) and overall survival (OS) were identified. Hazard rate analyses were performed for the survival dynamics. RESULTS: T stage (hazard ratio [HR] = 4.102, p = 0.03), N stage (N2 vs. N0, HR = 7.653, p < 0.001), and consolidative CT morphology (HR = 3.556, p = 0.008) remained independent predictors for DFS. Age (HR = 1.110, p = 0.002), smoking (HR = 12.893, p < 0.001), T stage (HR = 13.005, p = 0.006), N stage (N2 vs. N0, HR = 7.653, p = 0.004), STAS (HR = 7.463, p = 0.008), and consolidative CT morphology (HR = 6.779, p = 0.007) remained independent predictors for OS. Consolidative morphology, higher T and N stage, and presence of STAS revealed initial sharp peaks after steep decline of the hazard rate curves for recurrence or death in follow-up period. CONCLUSIONS: Consolidative morphology, higher T and N stage, smoking, and STAS were indicators of significantly greater risk of early recurrence or death in patients with IMA. Thus, these findings could be incorporated into future surveillance strategies.

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.

Deep learning analysis to predict EGFR mutation status in lung adenocarcinoma manifesting as pure ground-glass opacity nodules on CT.

Background: Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) showed potency as a non-invasive therapeutic approach in pure ground-glass opacity nodule (pGGN) lung adenocarcinoma. However, optimal methods of extracting information about EGFR mutation from pGGN lung adenocarcinoma images remain uncertain. We aimed to develop, validate, and evaluate the clinical utility of a deep learning model for predicting EGFR mutation status in lung adenocarcinoma manifesting as pGGN on computed tomography (CT). Methods: We included 185 resected pGGN lung adenocarcinomas in the primary cohort. The patients were divided into training (n = 125), validation (n = 23), and test sets (n = 37). A preoperative CT-based deep learning model with clinical factors as well as clinical and radiomics models was constructed and applied to the test set. We evaluated the clinical utility of the deep learning model by applying it to 83 GGNs that received EGFR-TKI from an independent cohort (clinical validation set), and treatment response was regarded as the reference standard. Results: The prediction efficiencies of each model were compared in terms of area under the curve (AUC). Among the 185 pGGN lung adenocarcinomas, 122 (65.9%) were EGFR-mutant and 63 (34.1%) were EGFR-wild type. The AUC of the clinical, radiomics, and deep learning with clinical models to predict EGFR mutations were 0.50, 0.64, and 0.85, respectively, for the test set. The AUC of deep learning with the clinical model in the validation set was 0.72. Conclusions: Deep learning approach of CT images combined with clinical factors can predict EGFR mutations in patients with lung adenocarcinomas manifesting as pGGN, and its clinical utility was demonstrated in a real-world sample.

Role of IN-1233 in the prevention of neointimal hyperplasia after stent placement in a rat artery model.

PURPOSE: To evaluate the efficacy of an activin receptor-like kinase (ALK) 5 inhibitor, IN-1233, for the prevention of neointimal hyperplasia after bare stent placement in a rat common iliac artery (CIA) model. MATERIALS AND METHODS: All experiments were approved by the committee of animal research. A self-expanding metallic bare stent (2 mm × 6 mm) was inserted into the left CIA of 26 Sprague-Dawley male rats (300-360 g) under fluoroscopic guidance. IN-1233 was injected via the intraperitoneal route daily in 13 rats for 8 weeks after stent placement (group A); the other 13 rats underwent stent placement only (group B). Angiography was performed immediately and 4 weeks and 8 weeks after stent placement. Rats were sacrificed at 8 weeks after stent placement, and histologic findings were obtained. The neointimal area (NA), percentage of neointimal hyperplasia (%NH), and neointimal-to-medial area ratio (N/M) were assessed and compared between the two groups. RESULTS: Stent placement was technically successful. In 25 rats, arteries with stent placement were angiographically patent, whereas 1 rat in group B had an occlusion. The NA (0.31 mm(2) ± 0.09 vs 0.56 mm(2) ± 0.17; P < .001), the %NH (26.16% ± 8.75 vs 44.71% ± 17.75; P < .001) and the N/M (1.93 ± 0.77 vs 4.77 ± 2.26; P < .001) were significantly decreased in group A compared with group B. CONCLUSIONS: IN-1233 was shown in this study to be effective for the prevention of neointimal hyperplasia after bare metallic stent placement in a rat CIA model.

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.

IN-1233, an ALK-5 inhibitor: prevention of granulation tissue formation after bare metallic stent placement in a rat urethral model.

PURPOSE: To evaluate the efficacy of an activin receptor-like kinase-5 inhibitor, IN-1233, for the prevention of tissue hyperplasia after bare stent placement in a rat urethral model. MATERIALS AND METHODS: Procedures were performed in accordance with the National Institutes of Health guidelines for humane handling of animals; approval of the committee of animal research was obtained. In 20 Sprague-Dawley male rats (weight range, 300-350 g), a self-expanding metallic bare stent was inserted in the urethra by using fluoroscopic guidance. One group of 10 rats (group A) was treated with IN-1233, the other group of 10 rats (group B) received no treatment. Retrograde urethrography was performed 4 and 8 weeks after stent placement. All rats were sacrificed at 8 weeks for histologic analysis. RESULTS: Stent placement was technically successful in all rats. The average stent diameter was significantly larger in group A compared with group B at follow-up retrograde urethrography performed 4 (P = .006) and 8 (P < .001) weeks after stent placement. At histologic analysis, the percentage of granulation tissue area (P < .001), thickness of submucosal fibrosis (P < .001), and number of epithelial layers (P < .001) were significantly decreased in group A compared with group B. Inflammatory cell infiltration (P < .001) was significantly increased in group A compared with group B. CONCLUSION: IN-1233 is effective for the prevention of granulation tissue formation after bare metallic stent placement in a rat urethral model.

Basaloid Squamous Cell Carcinoma of the Lung: Two Case Reports with CT Imaging Findings.

Basaloid squamous cell carcinoma of the lung is now considered a subtype of squamous cell carcinoma as per the 2015 WHO classification and remains a relatively unknown type of lung cancer due to its rarity. Here we report two cases of basaloid squamous cell carcinoma of the lung and their CT findings to clarify some of the radiologic features of this type of cancer. Two patients aged 85 and 68 years with lung basaloid squamous cell carcinoma visited our institution and underwent surgical resection. On CT, the lesions were 3.1 and 2.8 cm in size, respectively, well-defined, round in shape with lobulated margins and prominent intratumoral necrosis. The latter case was followed after operation for 20 months, and there was no recurrence of the disease on CT. Although very rare, basaloid squamous cell carcinoma should be considered a subtype of lung cancer in tumors sharing these CT findings.