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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Prediction of tumor doubling time of lung adenocarcinoma using radiomic margin characteristics.

BACKGROUND: Because shape or irregularity along the tumor perimeter can result from interactions between the tumor and the surrounding parenchyma, there could be a difference in tumor growth rate according to tumor margin or shape. However, no attempt has been made to evaluate the correlation between margin or shape features and tumor growth. METHODS: We evaluated 52 lung adenocarcinoma (ADC) patients who had at least two computed tomographic (CT) examinations before curative resection. Volume-based doubling times (DTs) were calculated based on CT scans, and patients were divided into two groups according to the growth pattern (GP) of their ADCs (gradually growing tumors [GP I] vs. growing tumors with a temporary decrease in DT [GP II]). CT radiomic features reflecting margin characteristics were extracted, and radiomic features reflective of tumor DT were selected. RESULTS: Among the 52 patients, 41 (78.8%) were assigned to GP I and 11 (21.2%) to GP II. Of the 94 radiomic features extracted, eccentricity, surface-to-volume ratio, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5) were ultimately selected for tumor DT prediction. Selected radiomic features in GP I were surface-to-volume ratio, contrast, LoG uniformity (σ = 3.5), and LoG skewness (σ = 0.5), similar to those for total subjects, whereas the radiomic features in GP II were solidity, energy, and busyness. CONCLUSIONS: This study demonstrated the potential of margin-related radiomic features to predict tumor DT in lung ADCs. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: We found a relationship between margin-related radiomic features and tumor doubling time. WHAT THIS STUDY ADDS: Margin-related radiomic features can potentially be used as noninvasive biomarkers to predict tumor doubling time in lung adenocarcinoma and inform treatment strategies.

Deciphering the tumor microenvironment through radiomics in non-small cell lung cancer: Correlation with immune profiles.

Growing evidence suggests that the efficacy of immunotherapy in non-small cell lung cancers (NSCLCs) is associated with the immune microenvironment within the tumor. We aimed to explore radiologic phenotyping using a radiomics approach to assess the immune microenvironment in NSCLC. Two independent NSCLC cohorts (training and test sets) were included. Single-sample gene set enrichment analysis was used to determine the tumor microenvironment, where type 1 helper T (Th1) cells, type 2 helper T (Th2) cells, and cytotoxic T cells were the targets for prediction with computed tomographic (CT) radiomic features. Multiple algorithms were in the modeling followed by final model selection. The training dataset comprised 89 NSCLCs and the test set included 60 cases of lung squamous cell carcinoma and adenocarcinoma. A total of 239 CT radiomic features were used. A linear discriminant analysis model was selected for the final model of Th2 cell group prediction. The area under the curve value of the final model on the test set was 0.684. Predictors of the linear discriminant analysis model were skewness (total and outer pixels), kurtosis, variance (subsampled from delta [subtraction inner pixels from outer pixels]), and informational measure of correlation. The performances of radiomics on test set of Th1 and cytotoxic T cell were not accurate enough to be predictable. A radiomics approach can be used to interrogate an entire tumor in a noninvasive manner and provide added diagnostic value to identify the immune microenvironment of NSCLC, in particular, Th2 cell signatures.

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.

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.

Computed tomographic findings of macrolide-resistant Mycobacterium massiliense pulmonary disease and changes after antibiotic treatment.

The purpose of this study was to present the computed tomographic (CT) findings of lung abnormalities in macrolide-resistant Mycobacterium massiliense pulmonary disease and its changes in follow-up CT after antibiotic treatment.Chest CT scans of patients with macrolide-resistant M massiliense pulmonary disease (n = 19) were retrospectively reviewed. Patients were treated with multidrug therapy, and sputum examinations were performed. Follow-up CT scans obtained during antibiotic treatment after detection of macrolide resistance were also reviewed, if available (n = 13). The CT scores at detection of macrolide resistance and at the last follow-up periods were also compared.Of all patients with macrolide-resistant M massiliense pulmonary disease, 2 (11%) patients achieved sputum culture conversion during the follow-up period. The most common CT findings of M massiliense pulmonary disease at detection of macrolide resistance were bronchiectasis and bronchiolitis (n = 19, 100%), followed by consolidation (n = 16, 84%), cavities (n = 11, 58%), and nodules (n = 6, 32%). On the last follow-up CT, overall CT scores were increased in 8 (62%) of 13 patients, and total mean CT score was significantly increased (P = .021). For each CT pattern, the cavity showed the greatest increase in CT score (P = .027), followed by bronchiectasis (P = .038).Common CT findings of macrolide-resistant M massiliense pulmonary disease were similar to those of pulmonary disease caused by other species of nontuberculous mycobacteria at presentation. However, in macrolide-resistant M massiliense pulmonary disease, serial CT scans showed deterioration with cavitary and bronchiectatic change in most patients despite multidrug antibiotic therapy.

Network approach of the conformational change of c-Src, a tyrosine kinase, by molecular dynamics simulation.

Non-receptor tyrosine kinase c-Src plays a critical role in numerous cellular signalling pathways. Activation of c-Src from its inactive to the active state involves large-scale conformational changes, and is controlled by the phosphorylation state of two major phosphorylation sites, Tyr416 and Tyr527. A detailed mechanism for the entire conformational transition of c-Src via phosphorylation control of Tyr416 and Tyr527 is still elusive. In this study, we investigated the inactive-to-active conformational change of c-Src by targeted molecular dynamics simulation. Based on the simulation, we proposed a dynamical scenario for the activation process of c-Src. A detailed study of the conformational transition pathway based on network analysis suggests that Lys321 plays a key role in the c-Src activation process.

Role of N-terminal Pro-brain Natriuretic Peptide in Differentiating Node-first Presentations of Kawasaki Disease and Bacterial Cervical Lymphadenitis

PURPOSE: Kawasaki disease (KD) is an acute, self-limited, febrile disease. For cases of KD in which the first symptom is cervical lymphadenopathy (node-first presentations of KD, NFKD), it is frequently misdiagnosed as bacterial cervical lymphadenitis (BCL). Therefore, we evaluated the usefulness of N-terminal pro-brain natriuretic peptide (NT-proBNP) to differentiate between NFKD and BCL. METHODS: This is a retrospective, observational study. Patients were divided into three groups, KD as 1st diagnosis, NFKD, and BCL. The laboratory and demographic data, intravenous immunoglobulin (IVIG) administration time and total febrile duration, length of hospital stay, and number of coronary artery complications were then compared for each group. RESULTS: A total of 451 patients were diagnosed as KD and 45 patients as BCL. Of the 451 KD patients, 417 (92.5%) were KD as 1st diagnosis, and 34 (7.5%) were NFKD. White blood cell count, absolute neutrophil count, C-reactive protein, erythrocyte sedimentation rate, and NT-proBNP differed significantly between NFKD and BCL. Variables that differed significantly were analyzed using a receiver operating characteristic curve, which revealed that NT-proBNP had the largest area under curve (0.944). Additionally, IVIG administration time, total febrile duration and length of hospital stay differed between KD as 1st diagnosis and NFKD. CONCLUSION: It is difficult to differentiate NFKD from BCL, so proper treatment and length of hospital stay were delayed. NT-proBNP is very useful for differentiating NFKD and BCL. Therefore, in cases of BCL with a long febrile period without reacting general treatments, the NT-proBNP test can be considered.

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.