We report a rare case of sporadic lymphangioleiomyomatosis (LAM) in a 51-year-old male patient presenting with pneumothorax. High-resolution CT revealed multiple thin-walled cysts throughout both lungs. A history of renal angiomyolipoma and histopathologic findings confirmed the diagnosis. Immunohistochemical staining was positive for HMB-45 and α-smooth muscle actin. This case underscores the importance of including sporadic LAM in the differential diagnosis of cystic lung disease in men, despite its rarity. Early radiologic recognition and tissue confirmation are essential for accurate diagnosis and appropriate management.

Although 16-cm wide-detector CT scanners with prospective ECG-gating enable coronary artery imaging within a single cardiac cycle at a low radiation dose, many institutions still rely on scanners with detector widths <16cm. These scanners typically use retrospective scanning, resulting in higher radiation exposure. This study tests the feasibility of lowering the radiation dose of ECG-gated retrospective CCTA on 8-cm detector scanners to the level of prospective acquisition on 16-cm ones, using deep learning reconstruction (DLR). This study involved 83 low-dose (group A) and 62 routine-dose (group B) retrospective CCTA cases on an 8-cm detector scanner, with the low-dose protocol targeting a radiation level comparable to prospective acquisitions on 16-cm scanners. Groups Ref1 and Ref2 were used as the dose and image quality benchmarks. They consisted of prospective CCTA cases acquired on 16-cm scanners from a shared vendor, and were extracted from published studies. Specific subsets (A1/B1 and A2/B2) were selected from group A/B based on weight (55 to 75kg) and HR (<75bpm) to match Ref1 and Ref2. DLR was used for group A. Image quality was evaluated using signal-to-noise ratio (SNR) and subjective scoring. No significant differences in demographics were noted among Ref1/A1/B1 or Ref2/A2/B2 (all P>0.05). CTDIvol for A1 was comparable to Ref1 (median: 13.6mGy vs. 13.2mGy, P>0.999), whereas A2 had lower CTDIvol than Ref2 (median: 13.3mGy vs. 16.1mGy, P=0.018). DLR improved SNRs in group A, with higher values than Ref and B (all P<0.05). With DLR, image quality scores were comparable between groups A and B (all P>0.05). DLR can lower the radiation dose in retrospective CCTA on 8-cm detector scanners to that of prospective CCTA on 16-cm ones, with non-inferior image quality.

There is growing global recognition of the importance of viruses as a cause of lower respiratory tract infections (LRIs) from widespread outbreaks such as the recent COVID-19 pandemic, to seasonal viruses such as those caused by influenza and adenovirus. Worldwide, viruses are a common cause of respiratory infections and mortality due to LRIs. Advances in rapid virus detection have led to a greater understanding of their prevalence and importance as etiological agents of illness. With multiple epidemics and pandemics of viral pneumonia over the past several decades, there is an increasing need to understand the global variations and impacts of these viruses. This review summarizes recent global trends in viral pneumonias and provides an overview of current diagnostic, treatment, and imaging challenges in the setting of viral infections.

While the primary goal of lung cancer screening CT is to detect early-stage lung cancer in high-risk populations, it often reveals asymptomatic cardiovascular abnormalities that can be clinically significant. These findings include coronary artery calcifications (CACs), myocardial pathologies, cardiac chamber enlargement, valvular lesions, and vascular disease. CAC, a marker of subclinical atherosclerosis, is particularly emphasized due to its strong predictive value for cardiovascular events and mortality. Guidelines recommend qualitative or quantitative CAC scoring on all noncontrast chest CTs. Other actionable findings include aortic aneurysms, pericardial disease, and myocardial pathology, some of which may indicate past or impending cardiac events. This article explores the wide range of incidental cardiovascular findings detectable during low-dose CT (LDCT) scans for lung cancer screening, as well as noncontrast chest CT scans. Distinguishing which findings warrant further evaluation is essential to avoid overdiagnosis, unnecessary anxiety, and resource misuse. The article advocates for a structured approach to follow-up based on the clinical significance of each finding and the patient's overall risk profile. It also notes the rising role of artificial intelligence in automatically detecting and quantifying these abnormalities, potentiating early behavioral modification or medical and surgical interventions. Ultimately, this piece highlights the opportunity to reframe LDCT as a comprehensive cardiothoracic screening tool.

This study evaluated the diagnostic accuracy and differential diagnostic capabilities of 12 Large Language Models (LLMs), one cardiac radiologist, and 3 general radiologists in cardiac radiology. The impact of the ChatGPT-4o assistance on radiologist performance was also investigated. We collected publicly available 80 "Cardiac Case of the Month" from the Society of Thoracic Radiology website. LLMs and Radiologist-III were provided with text-based information, whereas other radiologists visually assessed the cases with and without the ChatGPT-4o assistance. Diagnostic accuracy and differential diagnosis scores (DDx scores) were analyzed using the χ 2 , Kruskal-Wallis, Wilcoxon, McNemar, and Mann-Whitney U tests. The unassisted diagnostic accuracy of the cardiac radiologist was 72.5%, general radiologist-I was 53.8%, and general radiologist-II was 51.3%. With ChatGPT-4o, the accuracy improved to 78.8%, 70.0%, and 63.8%, respectively. The improvements for general radiologists-I and II were statistically significant ( P ≤0.006). All radiologists' DDx scores improved significantly with ChatGPT-4o assistance ( P ≤0.05). Remarkably, Radiologist-I's GPT-4o-assisted diagnostic accuracy and DDx score were not significantly different from the Cardiac Radiologist's unassisted performance ( P >0.05).Among the LLMs, Claude 3 Opus and Claude 3.5 Sonnet had the highest accuracy (81.3%), followed by Claude 3 Sonnet (70.0%). Regarding the DDx score, Claude 3 Opus outperformed all models and radiologist-III ( P <0.05). The accuracy of the general radiologist-III significantly improved from 48.8% to 63.8% with GPT4o assistance ( P <0.001). ChatGPT-4o may enhance the diagnostic performance of general radiologists in cardiac imaging, suggesting its potential as a diagnostic support tool. Further studies are required to assess the clinical integration.

Acute respiratory distress syndrome (ARDS) is a life-threatening condition characterized by widespread inflammation in the lungs. It is associated with high mortality and morbidity in critically ill patients. ARDS are conditions that cause acute respiratory failure due to noncardiogenic pulmonary edema, leading to severe hypoxemia and diffuse, bilateral lung injury. These conditions represent a spectrum of lung injury with varying severity and complexity. ARDS is a more severe form of ALI. ALI can also describe a range of clinical and paraclinical findings that include one or both pathologic patterns of organizing pneumonia (OP) or diffuse alveolar damage (DAD). The pathologic correlate of ARDS is DAD. This damage can be triggered by various risk factors, including pneumonia, sepsis, trauma, and the inhalation of harmful substances. The alveolar capillary damage that accompanies DAD leads to a loss in barrier function and is associated with the accumulation of fluid into the alveolar space. This fluid accumulation (pulmonary edema), along with subsequent organization and scarring, impairs gas exchange, which leads to hypoxemia and respiratory failure. Despite advances in understanding the pathophysiology of ARDS and improvements in supportive care, the mortality rates from ARDS still range from 25% to 45%. It is crucial to recognize that radiographic and histologic findings in a patient with ARDS can vary significantly depending on the phase of the disease. This is because the pathophysiological processes underlying these conditions evolve over time, leading to changes in both clinical presentation and imaging findings. Misinterpretation of these findings could lead to incorrect diagnoses and inappropriate treatment strategies. Therefore, understanding the temporal evolution of this condition is essential for accurate diagnosis and effective management. Our paper seeks to examine the existing literature focusing on radiology and pathology at different phases of injury and resolution to enhance management of ARDS.

Compare the impact of photon-counting detector computed tomography (PCD-CT) to conventional CT on an interstitial lung disease (ILD) quantitative machine learning (QML) model. A QML model analyzed 52 CT exams from patients who underwent same-day conventional and PCD-CT for suspected ILD. Lin's concordance correlation coefficient (CCC) assessed agreement between conventional and PCD-CT QML results. A CCC >0.90 was regarded as excellent, 0.9 to 0.8 as good, and <0.80 as a poor concordance. Spearman rank correlation evaluated the association between pulmonary function test results (PFT) and QML features (reticulation [R], honeycombing [HC], ground glass [GG], interstitial lung disease [ILD], and vessel-related structures [VRS]). Correlations were statistically significant if the 95% CI did not include 0.00 and P value <0.05. Conventional and PCD-CT QML results had good to excellent concordance (CCC ≥0.8) except for total HC (CCC <0.8), likely related to better PCD-CT honeycombing delineation. Overall, compared with conventional CT, PCD-CT had consistently more statistically significant correlation with PFT for HC (9 PCD vs. 2 conventional of 28 total and regional associations), similar correlation for R (20 PCD vs. 18 conventional of 28 associations) and VRS (19 PCD vs. 23 conventional of 28 associations), and less correlation for GG extent (12 PCD vs. 20 conventional associations). There is strong agreement between conventional and PCD-CT QML ILD features except for HC. PCD-CT improved HC but decreased GG extent correlation with PFT. Therefore, even though most quantitative features were not impacted by the newer PCD-CT technology, model adjustment is necessary.

Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans. SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient. It is part of a fully automatic pipeline for the detection, segmentation, matching, and classification of metastatic lung lesions in longitudinal chest CT scans. A data set of 5040 metastatic lung lesions in 344 pairs of 208 prior and current chest CT scans from 79 patients was used for training/validation (173 scans, 65 patients) and testing (35 scans, 14 patients) of a standalone 3D U-Net models and 3 simultaneous SimU-Net models. Outcome measures were the lesion detection and segmentation precision, recall, Dice score, average symmetric surface distance (ASSD), lesion matching, and classification of lesion changes from computed versus manual ground-truth annotations by an expert radiologist. SimU-Net achieved a mean lesion detection recall and precision of 0.93±0.13 and 0.79±0.24 and a mean lesion segmentation Dice and ASSD of 0.84±0.09 and 0.33±0.22mm. These results outperformed the standalone 3D U-Net model by 9.4% in the recall, 2.4% in Dice, and 15.4% in ASSD, with a minor 3.6% decrease in precision. The SimU-Net pipeline achieved perfect precision and recall (1.0±0.0) for lesion matching and classification of lesion changes. Simultaneous deep learning analysis of metastatic lung lesions in prior and current chest CT scans with SimU-Net yields superior accuracy compared with individual analysis of each scan. Implementation of SimU-Net in the radiological workflow may enhance efficiency by automatically computing key metrics used to evaluate metastatic lung lesions and their temporal changes.

To perform a systematic review and meta-analysis of relevant studies to assess the diagnostic accuracy and safety outcomes of ultrasound (US)-guided transthoracic needle biopsy (TTNB) for peripheral lung and pleural lesions. A search was performed through Medline, Embase, Web of Science, and Cochrane Central from inception up to September 23, 2022 for diagnostic accuracy studies reporting US-guided TTNB (Prospero registration: CRD42021225168). The primary outcome was diagnostic accuracy, which was assessed by sensitivity, specificity, likelihood ratios (LR), and diagnostic odds ratio. Sensitivity and subgroup analyses were performed to evaluate inter-study heterogeneity. The secondary outcome was the frequency of complications. Random-effects models were used for the analyses. The risk of bias and the applicability of the included studies were assessed using the QUADAS-2 tool. Publication bias was assessed by testing the association between the natural logarithm of the diagnostic odds ratio and the effective sample size. Of the 7841 citations identified, 83 independent cohorts (11,767 patients) were included in the analysis. The pooled sensitivity of US-TTNB was 88% (95% CI: 86%-91%, 80 studies). Pooled specificity was 100% (95% CI: 99%-100%, 72 studies), resulting in positive LR, negative LR, and diagnostic odds ratio of 946 (-743 to 2635), 0.12 (0.09 to 0.14), and 8141 (1344 to 49,321), respectively. Complications occurred in 4% (95% CI: 3%-5%) of the procedures, with pneumothorax being the most frequent (3%; 95% CI: 2%-3%, 72 studies) and resulting in chest tube placement in 0.4% (95% CI: 0.2%-0.7%, 64 studies) of the procedures. US-TTNB is an effective and safe procedure for pleural lesions and peripheral lung lesions.