This article describes a clinical trial designed to determine the best way to manage chest drains in patients withĚýpleural infections, which are infections in the space around the lungs. These infections are often treated by inserting a small tube (a chest drain) to remove infected fluid, along with antibiotics. Current guidelines recommend regularly flushing these tubes with saline (saltwater) to keep them from clogging, but this practice has not been tested in a rigorous randomized controlled trial, and approaches vary widely in real-world care.

To address this, researchers designed the RELIEF trial, a multi-center study in the United States. Patients with pleural infections who need a chest drain will be randomly assigned to one of two groups: one receivingĚýregular flushingĚý(every six hours) and the other receiving flushing onlyĚýas neededĚýif the tube becomes blocked. The main outcome being measured is how long it takes before the chest tube can be safely removed. Additional outcomes include length of hospital stay, improvement seen on imaging scans (such as X-rays or CT scans), the need for further procedures, and any complications. The researchers will also use advanced statistical modeling to track how patients progress over time in each group.

Overall, this study aims to provide clear evidence on whether routine flushing of chest drains improves patient outcomes, helping to standardize care and potentially improve treatment for pleural infections.

Fig 1.ĚýSchematic of study design.

Abbreviations: CPPE: Complicated parapneumonic effusion, IET: Intrapleural enzyme therapy (tPA/DNase), NS: Normal saline, CXR: Chest X-ray, US: Ultrasound, CT scan: Computed Tomography scan. *Chest drain blockage is assessed by lack of tidaling, lack of fluid drainage, and presence of pleural fluid on US exam.

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Robotic-assisted bronchoscopy is a newer method for taking tissue samples from hard-to-reach areas of the lungs, offering an alternative to the more established electromagnetic navigational bronchoscopy. Although both techniques are commonly used, there is limited data comparing their effectiveness.

In this study, we conducted a single-center, cluster-randomized trial where patients scheduled for biopsy of a peripheral lung lesion were assigned to either robotic-assisted or electromagnetic navigational bronchoscopy, with the operating room serving as the unit of randomization. The main goal was to see how often each procedure successfully obtained tissue from the lung lesion. Secondary outcomes included procedure time and complications.

Among 411 patients analyzed, robotic-assisted bronchoscopy successfully collected tissue in 77.8% of cases, compared with 75.5% for electromagnetic navigation, showing that robotic-assisted bronchoscopy was at least as effective. The procedure took a median of 37 minutes for robotic-assisted versus 32 minutes for electromagnetic navigation. Pneumothorax, a known complication, occurred in 4 patients in the robotic group and 6 in the electromagnetic group.

Overall, robotic-assisted bronchoscopy is similarly effective and safe as electromagnetic navigation for evaluating peripheral lung lesions.

Figure 1.Consort Diagram. ENB = electromagnetic navigational bronchoscopy; RAB = robotic-assisted bronchoscopy.

]]> /valiant/2025/09/26/post-thoracentesis-ultrasound-versus-chest-radiography-for-the-evaluation-of-effusion-evacuation-and-lung-reexpansion-a-multicenter-study/ Fri, 26 Sep 2025 19:54:42 +0000 /valiant/?p=5144 Ratwani, Ankush P., Grosu, Horiana B., Husnain, Shaikh Muhammad Noor Ul, Sanchez, Trinidad M., Yermakhanova, Gulmira, Pannu, Jasleen Kaur, Debiane, Labib Gilles, DePew, Zachary S., Yarmus, Lonny B., & Maldonado, Fabien. (2025). Annals of the American Thoracic Society, 22(9).

This study compared two imaging methods—ultrasound and chest X-ray (CXR)—to see how well they assess whether fluid has been fully removed from the pleural space after thoracentesis, a procedure that drains fluid around the lungs. Patients with free-flowing pleural effusions were enrolled, and after the procedure, an ultrasound was performed immediately, while a CXR was done within four hours. The main outcome was whether both methods agreed on complete pleural space evacuation, defined as no remaining fluid seen on ultrasound or no blunting of the costophrenic angle on CXR.

A total of 145 patients were analyzed, with a median age of 64 years, and cancer was the most common cause of fluid buildup. The lung was considered trapped in half of the patients. More than 800 ultrasound images were reviewed independently by pulmonologists and radiologists who did not know the patient or procedure details. The results showed very strong agreement between ultrasound and CXR (agreement coefficient 0.93, 95% CI 0.83–1.00). When comparing based on effusion size, there was substantial agreement (kappa 0.64, 95% CI 0.51–0.77). Agreement between proceduralists and blinded ultrasound reviewers was also strong (kappa 0.81, 95% CI 0.71–0.90).

In conclusion, post-thoracentesis ultrasound is an equally effective alternative to chest X-ray for evaluating pleural space evacuation in patients with simple pleural effusions.

Figure 1.Pre and postthoracentesis evaluation of pleural effusion using ultrasound and chest radiography (CXR). (A) Large free-flowing simple pleural effusion visible in the posterior view. (B,ĚýC) Effusion size measured in the anterior and midaxillary views. (D) Prethoracentesis CXR with a pleural effusion (arrow). (·ˇâ€“G) Post-thoracentesis images showing resolution of the effusion and complete pleural evacuation in the midaxillary, anterior, and posterior views. (H) Post-thoracentesis CXR with complete pleural evacuation.

In respiratory medicine, diseases affecting the central airways—the trachea and main bronchi—have received less attention than diseases of the lung tissue itself. This may be due to the mistaken belief that these conditions are rare and usually not serious. Another common misconception is that severe cases can only be treated with complex, high-risk surgery, which can be dangerous for many patients.

Tracheal diseases can arise on their own or as a result of other underlying conditions, including inflammation, infections, or tumors. Today, many central airway diseases can be effectively treated using a variety of endoscopic procedures—minimally invasive techniques that have grown dramatically in number and sophistication over the past decade.

Because of these developments, central airway diseases present both unique challenges and opportunities for respiratory doctors, and they have played a major role in the growth of the subspecialty of Interventional Pulmonary Medicine.

Fig.6.1. The flow volume loop in fixed central airway obstruction reveals blunting of both the inspiratory and expiratory portions of the loop

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]]> /valiant/2025/07/28/bronchoscopic-lung-volume-reduction-model-for-assisted-target-lobe-selection/ Mon, 28 Jul 2025 15:58:37 +0000 /valiant/?p=4862 Hostetter, Logan, Brown, Leah M., Rajagopalan, Srinivasan, Dulohery-Scrodin, Megan M., Edell, Eric S., Lester, Michael G., Maldonado, Fabien, Lentz, Robert, Bartholmai, Brian J., & Peikert, Tobias. (2025). *BMJ Open Respiratory Research, 12*(1), e002903.

Bronchoscopic lung volume reduction using endobronchial valves (EBV) is an effective treatment for patients with severe emphysema, helping to improve lung function, exercise ability, breathlessness, and quality of life. Choosing the right patients and the specific part of the lung (called the treatment lobe) to treat is very important for success. Although doctors use strict clinical guidelines, many hospitals rely on teams of specialists and past experience to make these decisions. To make this process more objective and easier, we developed a mathematical model to help select patients and the best lung lobe for treatment.

A team of specialists reviewed detailed lung scans (high-resolution CT scans) from 119 patients to decide who should get EBV treatment and which lung lobe to treat. Using four key measurements from these scans—how complete the lung fissures are, how much of the lung tissue is very damaged (measured by specific density values), and the size of each lung lobe—we created two prediction models: one to identify who is a good candidate for EBV, and another to select the target lung lobe. We then tested these models on a separate group of 50 patients to see how well they worked.

The models performed very well, matching the decisions made by specialist teams with about 80-85% accuracy and strong ability to correctly identify suitable patients and lobes. This shows our model can support doctors by providing an objective tool to guide patient and lung lobe selection for EBV treatment.

In conclusion, EBV remains a valuable way to improve life for patients with severe emphysema. Our mathematical model, based on expert team experience and lung scan data, helps make patient and treatment decisions more objective. Future research is needed to see how well the model can predict lung lobe collapse and functional improvement after treatment.

Fig 1

Receiver operating characteristic of sensitivity and specificity for the training and validation cohorts. AUCs were 0.91 and 0.89, respectively. AUC, area under the curve.

This study looked at how artificial intelligence (AI) can help doctors better manage lung nodules—small spots on the lungs that may be signs of something serious, like cancer. Researchers used a special AI tool that scans radiology reports from CT scans to flag any mention of a possible lung nodule that might need attention.Ěý

Over the course of one month, the AI reviewed more than 76,000 radiology reports and flagged 389 potentially important lung nodules. A lung specialist then checked these flagged reports. They found that 70% of the nodules were being properly followed up by doctors (through imaging, specialist referrals, etc.), but 30% were not.Ěý

Thanks to the AI tool, many of these overlooked cases were brought to the attention of doctors, which led to extra clinic visits and even some procedures. This shows that AI could play a big role in catching potentially serious lung issues early—especially those that might otherwise be missed.Ěý

Doctors use different types of computer models to help predict whether a lung nodule (a small growth in the lung) might be cancerous. However, these models don’t always work the same way in every situation or at every hospital.Ěý

This study looked at how well eight different lung cancer prediction models worked when applied to three common clinical scenarios: (1) lung nodules found through routine cancer screening, (2) nodules found by chance during unrelated scans, and (3) nodules suspicious enough to require a biopsy.Ěý

Researchers reviewed real patient data from nine groups (over 4,000 patients total) collected between 2002 and 2021 at various hospitals. The models included both traditional statistical approaches and more advanced methods using artificial intelligence (AI) to analyze chest scans. Some models used just one scan, while others used multiple scans over time or combined scan results with other clinical information.Ěý

No single model was the best across all groups. AI models that analyzed just one scan worked well for nodules found during routine lung cancer screening but didn’t perform as well in other clinical situations. Models that used information from scans taken over time or combined data from different sources did better for nodules found by chance. For nodules that had already been flagged as suspicious and biopsied, none of the models worked particularly well.Ěý

Overall, the performance of lung cancer prediction models varied depending on where and how they were used. Most did not work well outside of the specific conditions they were originally designed for, showing a need for better, more flexible tools that can work reliably in different medical settings.Ěý

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The reconstruction kernel in computed tomography (CT) generation determines the image texture, and consistency in reconstruction kernels is crucial because the underlying CT texture can affect quantitative image analysis measurements. Harmonization, or kernel conversion, aims to minimize measurement differences caused by inconsistent reconstruction kernels. Existing methods for CT scan harmonization across single or multiple manufacturers require paired scans of hard and soft reconstruction kernels that are spatially and anatomically aligned, necessitating the training of numerous models across different kernel pairs within manufacturers.

In this study, an unpaired image translation approach was adopted to investigate harmonization between and across reconstruction kernels from different manufacturers. A multipath cycle generative adversarial network (GAN) was constructed, utilizing hard and soft reconstruction kernels from Siemens and GE vendors, sourced from the National Lung Screening Trial dataset. Fifty scans from each reconstruction kernel were used to train the multipath cycle GAN. To evaluate the effect of harmonization on the reconstruction kernels, 50 scans each from Siemens hard kernel, GE soft kernel, and GE hard kernel were harmonized to a reference Siemens soft kernel (B30f), and the percent emphysema was assessed.

A linear model was fitted considering age, smoking status, sex, and vendor, followed by an analysis of variance (ANOVA) on the emphysema scores. The approach minimized differences in emphysema measurement and highlighted the impact of age, sex, smoking status, and vendor on emphysema quantification. This study demonstrates the effectiveness of using unpaired image translation with multipath cycle GANs for kernel harmonization across different manufacturers, improving the consistency and reliability of quantitative image analysis.

In a study focused on the harmonization of reconstruction kernels in CT imaging, researchers utilized the National Lung Screening Trial to examine the impact of kernel choice on CT image texture and quantitative assessments. The study involved comparing CT scans reconstructed with both soft and hard tissue kernels, using a deep learning model based on the pix2pix architecture to convert images between these kernel types. A total of 1000 paired scans were used, with each model trained and tested on 100 pairs. The effectiveness of the conversion was measured through image similarity metrics such as root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). The results showed significant improvements in these metrics post-conversion, indicating successful kernel harmonization. This conversion also led to more consistent measurements of emphysema, muscle area, and subcutaneous adipose tissue, reducing the variability caused by different kernel types. Additionally, radiomic features remained reproducible after harmonization, underscoring the potential of this deep learning approach to enhance the accuracy and reliability of quantitative CT-based assessments across different imaging settings.