Pulmonary manifestations in VEXAS syndrome.

BACKGROUND: Vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic (VEXAS) syndrome is a recently recognized multisystem disorder caused by somatic mutations in the UBA1 gene. METHODS: A retrospective cohort study was conducted on all patients with VEXAS syndrome evaluated at our institution from June 2020 through May 2022. Medical records and chest imaging studies were reviewed. RESULTS: We identified 45 subjects with median age of 68 years (range, 57-89), all men. Prior to VEXAS diagnosis, most patients had been diagnosed with various hematologic, rheumatologic, and dermatologic disorders. Most patients (84%) demonstrated canonical UBA1 methionine-41 (p.Met41) somatic mutations in hematopoietic cells. Fever (82%), skin lesions (91%), and respiratory symptoms (93%) were common presenting features. Chest CT manifested abnormalities in 91% of patients including parenchymal opacities in 25 (74%), most commonly ground-glass opacities (47%), along with mediastinal lymphadenopathy (29%), airway abnormalities (29%), and pleural effusion (24%). Pulmonary function test results available in 18 (40%) patients demonstrated mild restrictive impairment or normal results. Bronchoalveolar lavage and lung biopsy performed in a minority of patients demonstrated neutrophilic alveolitis and parenchymal inflammation, respectively. All patients received glucocorticoid therapy with at least partial response, but relapses were common and other immunosuppressive agents were employed in most patients. Pulmonary involvement appeared to improve in patients who received tocilizumab and JAK inhibitors. CONCLUSION: The pulmonary manifestations in VEXAS are relatively nonspecific and nonsevere, occur in the context of systemic inflammation and are responsive to escalation in glucocorticoid dosing.

High-Pitch Multienergy Coronary CT Angiography in Dual-Source Photon-Counting Detector CT Scanner at Low Iodinated Contrast Dose.

OBJECTIVES: The aim of this study was to evaluate the high-helical pitch, multienergy (ME) scanning mode of a clinical dual-source photon-counting detector (PCD) computed tomography (CT) and the benefit of virtual monoenergetic images (VMIs) for low-contrast-dose coronary CT angiography (CTA). MATERIALS AND METHODS: High-pitch (3.2) ME coronary CTA was performed in PCD-CT in 27 patients using low contrast dose (30 mL of iohexol 350 mg/mL) and in 26 patients at routine contrast dose (60 mL). Low-energy-threshold 120 kV images (also known as T3D images) and 50 kiloelectron volts (50 keV) and 100 kiloelectron volts (100 keV) VMIs were reconstructed using a 1024 × 1024 matrix and 0.6-mm slices. The CT numbers, noise, and contrast-to-noise ratio (CNR) were measured in the ascending aorta (AA), left main coronary artery (LMCA), and distal left anterior descending (LAD) artery. Confidence in grading luminal stenosis with calcific plaque, noncalcific plaque, and stent was evaluated by 2 independent readers on a 0-100 scale (0 the lowest), and a CAD-RADS score was assigned. Image contrast enhancement, sharpness, noise, artifacts, and overall image quality were rated using a 5-point ordinal scale (1 the lowest). RESULTS: The radiation doses (CTDI) in low- and routine-contrast cohorts were 2.5 ± 0.6 mGy and 3.1 ± 1.7 mGy, respectively ( P = 0.12). At all measured locations, the mean CT number was >300 HU in 120 kV (LMCA 382.9 ± 76.2, distal LAD 341.0 ± 53.9, AA 399.5 ± 76.1) and 50 keV images (LMCA 667.5 ± 139.9, distal LAD 578.1 ± 121.5, AA 700.8 ± 142.5) in the low-contrast cohort, with a 96% increase in CT numbers for 50 keV over 120 kV. The CT numbers were significantly higher ( P < 0.0001) in 50 keV than 120 kV and 100 keV VMI. The CNR was also significantly ( P < 0.0001) higher in 50 keV than 120 kV and 100 keV images in all vessels. Confidence in the assessment of luminal stenosis in the presence of calcific plaque was significantly higher ( P = 0.001) with the addition of 100 keV VMI (median score, 100) than using 50 keV alone (median score, 70) and 120 kV (median score, 70) for reader 1, but no significant differences were seen for reader 2 who had same median scores of 100 for all image types. The confidence in the assessment of luminal stenosis within a stent improved with the use of 100 keV images for both readers (reader 1: median scores for 50 + 100 keV = 100, 50 keV = 82.5, 120 kV = 82.5; reader 2: 50 + 100 keV = 100, 50 keV = 90, 120 kV = 90). There were no significant differences in confidence scores for assessment of luminal stenosis from noncalcific plaques for both readers. The reader-averaged qualitative scores for vascular enhancement and overall image quality were significantly higher for 50 keV VMI than for 120 kV images in both low- and routine-contrast dose cohorts. The image sharpness was nonsignificantly higher at 50 keV VMI than 120 kV images, and the artifact score was comparable for 50 keV VMI and 120 kV images. The noise was higher in 50 keV VMI than in 120 kV images. CONCLUSIONS: High-pitch ME PCD-CT mode produced diagnostic quality coronary CTA images at low radiation and iodinated contrast doses. The availability of ME VMIs significantly improved the CNR, overall image quality, and confidence in assessment of luminal stenosis in the presence of calcific plaques and stents, and resulted in change of CAD-RADS categories in 9 patients.

Testicular cavernous hemangioma masquerading as testicular malignancy.

Cavernous hemangiomas are benign tumors of vascular origin that can develop in any part of the body. However, its occurrence in the testis is rare. To the best of our knowledge, we are reporting the first case of a patient with cavernous hemangioma with concern for an extracapsular extension on ultrasound imaging.

Right middle pulmonary vein classification by 3D-CT reconstruction and its clinical significance.

Background: A profound understanding of the type of right middle lobe (RML) vein return is crucial for thoracic surgeons to ensure safe anatomic lung resection. In this study, the type of venous return in the RML and its clinical significance were analyzed using the 3D computed tomography (3D-CT) reconstruction technique. Methods: This retrospective study analyzed the patients who underwent anatomical lobectomy or anatomical lung segment resection with preoperative 3D-CT reconstruction at the Department of Thoracic Oncology Surgery, Fujian Cancer Hospital from January 2020 to October 2022. The exclusion criteria were as follows: (I) blurred CT images and inadequate IQQA 3D reconstruction for clear visualization of bronchial and vascular subsegmental branches; (II) patients with a previous history of right middle lobectomy; (III) incomplete medical records. Results: This study included 608 eligible cases, 245 males (40.30%) and 363 females (59.70%), with an average age of 58.61 years. We identified four major types of RML venous return, including 407 cases (66.94%) of central vein type, 123 cases (20.23%) of isolated vein type, 11 cases (1.81%) of basal vein type, and 67 cases (11.02%) of combined vein type. Furthermore, the central venous type comprised four subtypes: 191 cases (27.3%) had one branch, 165 cases (27.14%) had two branches, 43 cases (7.07%) had three branches, and 8 cases (1.32%) had more than three branches. The combined venous type includes four subtypes: combination type A (n=37 cases, 6.09%), combination type B (n=7 cases, 1.15%), combination type C (n=21 cases, 3.45%), and combination type D (n=2 cases, 0.33%). Conclusions: Based on 3D-CT reconstruction, numerous types of venous return in the RML were found, highlighting the significance of 3D-CT reconstruction for thoracic surgeons. It plays a crucial role in understanding the type of RML venous return prior to surgery, providing invaluable information to ensure surgical safety and success.

Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification.

Background: Bronchoscopy is a key step in the diagnosis and treatment of respiratory diseases. However, the level of expertise varies among different bronchoscopists. Artificial intelligence (AI) may help them identify bronchial lumens. Thus, a bronchoscopy quality-control system based on AI was built to improve the performance of bronchoscopists. Methods: This single-center observational study consecutively collected bronchoscopy videos from Shanghai Chest Hospital and segmented each video into 31 different anatomical locations to develop an AI-assisted system based on a convolutional neural network (CNN) model. We then designed a single-center trial to compare the accuracy of lumen recognition by bronchoscopists with and without the assistance of the AI system. Results: A total of 28,441 qualified images of bronchial lumen were used to train the CNNs. In the cross-validation set, the optimal accuracy of the six models was between 91.83% and 96.62%. In the test set, the visual geometry group 16 (VGG-16) achieved optimal performance with an accuracy of 91.88%, and an area under the curve of 0.995. In the clinical evaluation, the accuracy rate of the AI system alone was 54.30% (202/372). For the identification of bronchi except for segmental bronchi, the accuracy was 82.69% (129/156). In group 1, the recognition accuracy rates of doctors A, B, a and b alone were 42.47%, 34.68%, 28.76%, and 29.57%, respectively, but increased to 57.53%, 54.57%, 54.57%, and 46.24% respectively when combined with the AI system. Similarly, in group 2, the recognition accuracy rates of doctors C, D, c, and d were 37.90%, 41.40%, 30.91%, and 33.60% respectively, but increased to 51.61%, 47.85%, 53.49%, and 54.30% respectively, when combined with the AI system. Except for doctor D, the accuracy of doctors in recognizing lumen was significantly higher with AI assistance than without AI assistance, regardless of their experience (P<0.001). Conclusions: Our AI system could better recognize bronchial lumen and reduce differences in the operation levels of different bronchoscopists. It could be used to improve the quality of everyday bronchoscopies.

Impact of flexible noise control (FNC) image processing parameters on portable chest radiography.

There is a lack of understanding in the performance of flexible noise control (FNC) processing, which is used in digital radiography on a scanner vendor and has four parameters each involving multiple options. The aim of this study was to investigate the impact of FNC on portable chest imaging. An anthropomorphic chest phantom was imaged using a clinical chest program with 85 kV and five radiation dose levels at 40″ source-to-image distance with software-based scatter reduction method. All images were processed without and with FNC. Noise analysis was performed in two regions of interest (ROI) on subtracted noise-only images, and line profiles were generated through a lung-rib interface. In addition, noise power spectra (NPS) analysis was performed in solid water phantoms of 10 and 20 cm thicknesses, using the same acquisition program and a range of dose levels. Last, feedback on retrospectively deidentified, reprocessed, and randomized clinical images from 20 portable chest exams was gathered from two thoracic radiologists. Noise reduction performances of FNC were demonstrated, with the level depending on specific FNC parameters, dose levels, ROI placement, and phantom sizes. Higher frequency textural patterns were revealed through the NPS analysis, which varied based on FNC parameters, dose levels, and phantom sizes. Overall, the vendor default parameter FGA0.5 yielded the highest noise reduction and textural artifacts. Radiologist feedback showed consistent preference of no FNC due to the presence of textural artifacts in the FNC-processed images. An algorithm improvement to avoid introducing artifacts would be desired.

Imaging Patterns in Occupational Lung Disease-When Should I Consider?

Occupational lung diseases (OLDs) encompass a broad group of entities related to the inhalation of a variety of agents in the workplace. OLDs may affect the lung parenchyma, pleura, and/or airways. OLDs can pose a diagnostic challenge for radiologists due to a lack of exposure history and overlap in imaging findings with nonoccupational-related entities. For this reason, it is important for the radiologist to be familiar with the high-resolution computed tomography patterns associated with OLDs and consider OLDs when formulating a differential.

The incidence and risk factors of acute pain after preoperative needle localization of pulmonary nodules: a cross-sectional study.

Background: The incidence, severity and associated risk factors of acute pain after preoperative needle localization of pulmonary nodules are poorly characterized. We therefore conducted a cross-sectional study to quantify the acute pain induced by preoperative needle localization of small pulmonary nodules before video-assisted thoracoscopic surgery (VATS). Methods: We conducted this study at Shanghai Chest Hospital from September 2021 through December 2021. Eligible patients were between 18 and 75 years old and had small pulmonary nodules requiring preoperative CT-guided needle localization. The intensity of acute pain was assessed using the visual analogue scale (VAS) after preoperative needle localization. A VAS score ≥4 cm indicated moderate to severe pain. Patient demographics and CT-guided localization factors were collected to identify significant predictors associated with moderate to severe pain. Results: A total of 300 patients were included in the final analysis, with a mean (SD) age of 51 (SD =12) years old; 63% were female. Moderate to severe pain occurred in 50.8% of patients during deep breathing and 45.7% of patients during movement. Multivariate logistic regression analysis showed that multiple localization needles [multiple needle localizations vs. single needle localization, odds ratio (OR): 2.363, 95% confidence interval (CI): 1.157-4.825, P=0.018] and the specific location of needle puncture on the chest wall were significant predictors of moderate to severe pain after CT-guided needle localization (lateral chest wall vs. anterior chest wall OR: 2.235, 95% CI: 1.106-4.518, P=0.025; posterior chest wall vs. anterior chest wall OR: 1.198, 95% CI: 0.611-2.349, P=0.599). Conclusions: In adult patients receiving hookwire CT-guided localization, moderate to severe pain was common. Avoiding the localization route through lateral chest wall may be helpful and pharmacological medications or regional blockade is necessitated in high-risk population.

Idiopathic Chronic Eosinophilic Pneumonia Evolving to Pulmonary Fibrosis: A Retrospective Analysis.

Background: Patients with idiopathic chronic eosinophilic pneumonia (ICEP) may have pulmonary fibrosis. Objectives: To investigate the predictors of pulmonary fibrosis in ICEP, to describe the timeline of pulmonary fibrosis after ICEP diagnosis, and to detail the radiologic pattern of fibrosis. Methods: A retrospective computer-assisted search was performed to identify patients with ICEP seen at Mayo Clinic in Rochester, Minnesota, from January 1, 1997, through September 1, 2019. Patients with follow-up chest computed tomography (CT) beyond 12 months after the ICEP diagnosis were included in the study. Demographic, clinical, radiologic, and histopathologic characteristics were analyzed. Proportional hazards regression was used to assess the predictors of pulmonary fibrosis. Results: We identified 62 patients (mean [SD] age at ICEP diagnosis, 60 [13] years; female sex, 37 [60%]). Cough (87%) and shortness of breath (85%) were the most common presenting symptoms. Of patients, 27 (44%) had a history of smoking and 27 (44%) had a history of asthma. During follow-up, 23 patients (37%) had CT evidence of pulmonary fibrosis, of whom 16 patients (70%) had a CT pattern inconsistent with usual interstitial pneumonia. In 29% of the patients, the CT evidence of pulmonary fibrosis developed within 2 years after ICEP. Age and male sex were predictors of pulmonary fibrosis. Of note, a history of asthma decreased the likelihood of pulmonary fibrosis. Conclusions: Development of pulmonary fibro-sis is not uncommon in patients with ICEP, especially older men, and is associated with increased risk of death.

Imaging of the Left Atrial Appendage Before Occluder Device Placement: Evaluation of Virtual Monoenergetic Images in a Single-Bolus Dual-Phase Protocol.

PURPOSE: Preimplantation cardiac computed tomography (CT) for assessment of the left atrial appendage (LAA) enables correct sizing of the device and the detection of contraindications, such as thrombi. In the arterial phase, distinction between false filling defects and true thrombi can be hampered by insufficient contrast medium distribution. A delayed scan can be used to further differentiate both conditions, but contrast in these acquisitions is relatively lower. In this study, we investigated whether virtual monoenergetic images (VMI) from dual-energy spectral detector CT (SDCT) can be used to enhance contrast and visualization in the delayed phase. MATERIALS AND METHODS: Forty-nine patients receiving SDCT imaging of the LAA were retrospectively enrolled. The imaging protocol comprised dual-phase acquisitions with single-bolus contrast injection. Conventional images (CI) from both phases and 40-keV VMI from the delayed phase were reconstructed. Attenuation, signal-, and contrast-to-noise ratios (SNR/CNR) were calculated by placing regions-of-interest in the LAA, left atrium, and muscular portion of interventricular septum. Two radiologists subjectively evaluated conspicuity and homogeneity of contrast distribution within the LAA. RESULTS: Contrast of the LAA decreased significantly in the delayed phase but was significantly improved by VMI, showing comparable attenuation, SNR, and CNR to CI from the arterial phase (attenuation/SNR/CNR, CI arterial phase: 266.0 ± 117.0 HU/14.2 ± 7.2/6.6 ± 3.9; CI-delayed phase: 107.6 ± 35.0 HU/5.9 ± 3.0/1.0 ± 1.0; VMI delayed phase: 260.3 ± 108.6 HU/18.2 ± 10.6/4.8 ± 3.4). The subjective reading confirmed the objective findings showing improved conspicuity and homogeneity in the delayed phase. CONCLUSIONS: The investigated single-bolus dual-phase acquisition protocol provided improved visualization of the LAA. Homogeneity of contrast media was higher in the delayed phase, while VMI maintained high contrast.

Autopsy study of fatal invasive pulmonary aspergillosis: Often undiagnosed premortem.

Invasive aspergillosis (IA) is a serious complication in immunocompromised and critically ill patients but is difficult to diagnose. We sought to examine how often cases go undiagnosed and to understand the presenting clinical and radiologic features associated with fatal IA. We reviewed cases of fatal IA confirmed at autopsy (N = 67) between 1999 and 2019 at a tertiary academic hospital. At autopsy, pulmonary involvement was present in 97% of cases--46% were limited to the lungs and 51% had concomitant extrapulmonary involvement. Immunosuppression with either glucocorticoids and/or other immunosuppressive agents was present in 85%. Among those not immunocompromised (15%), chronic lung disease was present in 70%, and a respiratory coinfection was found in 50%. Chest imaging abnormalities including consolidation, ground glass opacities, halo sign, cavitation, and air crescent sign were present in 49%, 49%, 37%, 22%, and 7% of cases, respectively. Diagnostic bronchoscopy was performed in 61% of cases and yielded aspergillus in 63% of those cases by either bronchoalveolar lavage (galactomannan and/or culture), bronchial washings, or transbronchial biopsy cultures. Either a respiratory coinfection or other systemic coinfection was diagnosed in 64%. The performance of diagnostic bronchoscopy was associated with accurate pre-mortem identification of IA (p = 0.001). Clinicians correctly identified IA as the cause of death in only 27% of fatal IA cases identified at autopsy. Complex presenting features, high rates of co-infections, and low rates of invasive diagnostic procedures may have led to missed diagnoses of IA.

Predicting Usual Interstitial Pneumonia Histopathology From Chest CT Imaging With Deep Learning.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, often fatal form of interstitial lung disease (ILD) characterized by the absence of a known cause and usual interstitial pneumonitis (UIP) pattern on chest CT imaging and/or histopathology. Distinguishing UIP/IPF from other ILD subtypes is essential given different treatments and prognosis. Lung biopsy is necessary when noninvasive data are insufficient to render a confident diagnosis. RESEARCH QUESTION: Can we improve noninvasive diagnosis of UIP be improved by predicting ILD histopathology from CT scans by using deep learning? STUDY DESIGN AND METHODS: This study retrospectively identified a cohort of 1,239 patients in a multicenter database with pathologically proven ILD who had chest CT imaging. Each case was assigned a label based on histopathologic diagnosis (UIP or non-UIP). A custom deep learning model was trained to predict class labels from CT images (training set, n = 894) and was evaluated on a 198-patient test set. Separately, two subspecialty-trained radiologists manually labeled each CT scan in the test set according to the 2018 American Thoracic Society IPF guidelines. The performance of the model in predicting histopathologic class was compared against radiologists' performance by using area under the receiver-operating characteristic curve as the primary metric. Deep learning model reproducibility was compared against intra-rater and inter-rater radiologist reproducibility. RESULTS: For the entire cohort, mean patient age was 62 ± 12 years, and 605 patients were female (49%). Deep learning performance was superior to visual analysis in predicting histopathologic diagnosis (area under the receiver-operating characteristic curve, 0.87 vs 0.80, respectively; P < .05). Deep learning model reproducibility was significantly greater than radiologist inter-rater and intra-rater reproducibility (95% CI for difference in Krippendorff's alpha did not include zero). INTERPRETATION: Deep learning may be superior to visual assessment in predicting UIP/IPF histopathology from CT imaging and may serve as an alternative to invasive lung biopsy.

A rare presentation of unilateral periureteral renal lymphangiomatosis.

Renal lymphangiomatosis is a rare developmental malformation of the perirenal lymphatic system. We report a unique case with unilateral massive periureteral involvement in addition to intrarenal and peripelvic lymphangiomatosis. Although this is a rare entity, it should be considered in patients with peripelvic or periureteric cystic lesions as it may affect appropriate management and follow-up. This case report reviews the imaging features of this entity and a comprehensive literature review and discussion about the entity will be provided.