Lung Imaging Research Articles

Abstract B045: Requirement of MenaINV for metastatic colonization of the lungs by breast cancer stem cells

Abstract Background: Breast cancer is the most frequently diagnosed cancer, and the second leading cause of cancer-related death in women. Most breast cancer related deaths are due to metastasis to distant organs. The primary tumor and secondary site microenvironments provide cues that activate cancer cell plasticity programs such as those associated with invasion and stemness, which are needed for dissemination and the establishment of metastatic foci. We demonstrated that in the primary tumor >50% of cancer stem cells (CSCs) co-express the invasive phenotype marker, MenaINV. Given that ∼80% of newly arriving circulating tumor cells to the lung are CSCs, we questioned if extravasation of CSCs to the lung is mediated solely by MenaINV or if other compensatory pathways are at play. We describe here our discovery that CSCs require MenaINV for extravasation to the lung parenchyma. Methods: To visualize breast cancer stem cell extravasation into the lung parenchyma, we expressed the stemness biosensor, SORE6, in MDA-MB-231 breast cancer cells. SORE6 is composed of 6 tandem repeats of the Sox2 and Oct4 response elements placed upstream of destabilized Green Fluorescent Protein (GFP). To distinguish CSCs (green) from non-CSCs, all cells were also labeled with red tracker dye. Breast cancer cells transduced with lenti-SORE6 were FACS sorted into low SORE6-GFP cells (non- CSCs) and high SORE6-GFP (CSCs). Activation of the stem program in FACS sorted cells was validated by qRT-PCR analysis of the widely used stem cell markers CD44, CD133, P63, ALDH1, Sox9, Sox2, and Oct4. The extravasation efficiency of non-CSCs vs. CSCs was assessed in vitro by the extravasation directed transendothelial migration (eTEM) assay, which mimics extravasation by allowing migration of cancer cells through endothelial cells cultured on a porous membrane, as well as in vivo, by intravital imaging. The impact of MenaINV on CSC extravasation was evaluated through CRISPR/Cas9 mediated knockout of MenaINV. In in vivo experiments, a 50/50 mixture of non-CSCs and CSCs expressing either wild type or knocked-out MenaINV were injected into the vasculature of Rag2-/- MacBlue mice. in vivo visualization of cancer cell extravasation was accomplished by implantation of an indwelling lung imaging window prior to the injection of cancer cells, followed by high-resolution intravital imaging. Results: CSCs exhibit significantly higher expression of all stem cell markers and express more MenaINV compared to non-CSCs. Moreover, CSCs show significantly better transendothelial migration in eTME assay (p=0.03) and lung extravasation (p<0.0001) in intravital imaging, compared to non-CSCs. Finally, knocking out MenaINV reduced lung extravasation to the levels comparable to non-CSCs. Conclusions: This study highlights the crucial role of MenaINV in facilitating lung metastasis by CSCs. Thus, targeting MenaINV may represent a promising therapeutic strategy to reduce metastatic burden in breast cancer patients. Citation Format: Mohd Nauman, Yookyung Jung, Burcu Karadal-Ferrena, Camille Duran, Madeline Friedman-DeLuca, Nicole Barth, Prachiben Patel, John Condeelis, David Entenberg, Maja H. Oktay. Requirement of MenaINV for metastatic colonization of the lungs by breast cancer stem cells [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B045.

Building a DenseNet-Based Neural Network with Transformer and MBConv Blocks for Penile Cancer Classification

Histopathological analysis is an essential exam for detecting various types of cancer. The process is traditionally time-consuming and laborious. Taking advantage of deep learning models, assisting the pathologist in the diagnosis process is possible. In this work, a study was carried out based on the DenseNet neural network. It consisted of changing its architecture through combinations of Transformer and MBConv blocks to investigate its impact on classifying histopathological images of penile cancer. Due to the limited number of samples in this dataset, pre-training is performed on another larger lung and colon cancer histopathological image dataset. Various combinations of these architectural components were systematically evaluated to compare their performance. The results indicate significant improvements in feature representation, demonstrating the effectiveness of these combined elements resulting in an F1-Score of up to 95.78%. Its diagnostic performance confirms the importance of deep learning techniques in men’s health.

Comparison of AI software tools for automated detection, quantification and categorization of pulmonary nodules in the HANSE LCS trial.

Participant management in a lung cancer screening (LCS) depends on the assigned Lung Imaging Reporting and Data System (Lung-RADS) category, which is based on reliable detection and measurement of pulmonary nodules. The aim of this study was to compare the agreement of two AI-based software tools for detection, quantification and categorization of pulmonary nodules in an LCS program in Northern Germany (HANSE-trial). 946 low-dose baseline CT-examinations were analyzed by two AI software tools regarding lung nodule detection, quantification and categorization and compared to the final radiologist read. The relationship between detected nodule volumes by both software tools was assessed by Pearson correlation (r) and tested for significance using Wilcoxon signed-rank test. The consistency of Lung-RADS classifications between Software tool 1 (S1, Aview v2.5, Coreline Soft, Seoul, Korea) and Software tool 2 (S2, Prototype ''ChestCTExplore'', software version ToDo, Siemens Healthineers, Forchheim, Germany) was evaluated by Cohen's kappa (κ) and percentual agreement (PA).The derived volumes of true positive nodules were strongly correlated (r > 0.95), the volume derived by S2 was significantly higher than by S1 (P < 0.0001, mean difference: 6mm3). Moderate PA (62%) between S1 and S2 was found in the assignment of Lung-RADS classification (κ = 0.45). The PA of Lung-RADS classification to final read was 75% and 55% for S1 and S2, but the incorporation of S1 into the initial nodule detection and segmentation must be considered here. Significant nodule volume differences between AI software tools lead to different Lung-RADS scores in 38% of cases, which may result in altered participant management. Therefore, high performance and agreement of accredited AI software tools are necessary for a future national LCS program.

Machine Learning Based Framework for Lung Cancer Detection and Image Feature Extraction Using VGG16 with PCA on CT-Scan Images

Machine Learning Based Framework for Lung Cancer Detection and Image Feature Extraction Using VGG16 with PCA on CT-Scan Images

A vessel bifurcation liver CT landmark pair dataset for evaluating deformable image registration algorithms.

Evaluating deformable image registration (DIR) algorithms is vital for enhancing algorithm performance and gaining clinical acceptance. However, there is a notable lack of dependable DIR benchmark datasets for assessing DIR performance except for lung images. To address this gap, we aim to introduce our comprehensive liver computed tomography (CT) DIR landmark dataset library. This dataset is designed for efficient and quantitative evaluation of various DIR methods for liver CTs, paving the way for more accurate and reliable image registration techniques. Forty CT liver image pairs were acquired from several publicly available image archives and authors' institutions under institutional review board (IRB) approval. The images were processed with a semi-automatic procedure to generate landmark pairs: (1) for each case, liver vessels were automatically segmented on one image; (2) landmarks were automatically detected at vessel bifurcations; (3) corresponding landmarks in the second image were placed using two deformable image registration methods to avoid algorithm-specific biases; (4) a comprehensive validation process based on quantitative evaluation and manual assessment was applied to reject outliers and ensure the landmarks' positional accuracy. This workflow resulted in an average of ∼56 landmark pairs per image pair, comprising a total of 2220 landmarks for 40 cases. The general landmarking accuracy of this procedure was evaluated using digital phantoms and manual landmark placement. The landmark pair target registration errors (TRE) on digital phantoms were 0.37±0.26 and 0.55±0.34mm respectively for the two selected DIR algorithms used in our workflow, with 97% of landmark pairs having TREs below 1.5mm. The distances from the calculated landmarks to the averaged manual placement were 1.27±0.79mm. All data, including image files and landmark information, are publicly available at Zenodo (https://zenodo.org/records/13738577). Instructions for using our data can be found on our GitHub page at https://github.com/deshanyang/Liver-DIR-QA. The landmark dataset generated in this work is the first collection of large-scale liver CT DIR landmarks prepared on real patient images. This dataset can provide researchers with a dense set of ground truth benchmarks for the quantitative evaluation of DIR algorithms within the liver.

Multi-level digital-twin models of pulmonary mechanics: correlation analysis of 3D CT lung volume and 2D Chest motion

Creating multi-level digital-twin models for mechanical ventilation requires a detailed estimation of regional lung volume. An accurate generic map between 2D chest surface motion and 3D regional lung volume could provide improved regionalisation and clinically acceptable estimates localising lung damage. This work investigates the relationship between CT lung volumes and the forced vital capacity (FVC) a surrogate of tidal volume proven linked to 2D chest motion. In particular, a convolutional neural network (CNN) with U-Net architecture is employed to build a lung segmentation model using a benchmark CT scan dataset. An automated thresholding method is proposed for image morphology analysis to improve model performance. Finally, the trained model is applied to an independent CT dataset with FVC measurements for correlation analysis of CT lung volume projection to lung recruitment capacity. Model training results show a clear improvement of lung segmentation performance with the proposed automated thresholding method compared to a typically suggested fixed value selection, achieving accuracy greater than 95% for both training and independent validation sets. The correlation analysis for 160 patients shows a good correlation ofRsquared value of 0.73 between the proposed 2D volume projection and the FVC value, which indicates a larger and denser projection of lung volume relative to a greater FVC value and lung recruitable capacity. The overall results thus validate the potential of using non-contact, non-invasive 2D measures to enable regionalising lung mechanics models to equivalent 3D models with a generic map based on the good correlation. The clinical impact of improved lung mechanics digital twins due to regionalising the lung mechanics and volume to specific lung regions could be very high in managing mechanical ventilation and diagnosing or locating lung injury or dysfunction based on regular monitoring instead of intermittent and invasive lung imaging modalities.

Quantification of pulmonary edema using automated lung segmentation on computed tomography in mechanically ventilated patients with acute respiratory distress syndrome

BackgroundQuantification of pulmonary edema in patients with acute respiratory distress syndrome (ARDS) by chest computed tomography (CT) scan has not been validated in routine diagnostics due to its complexity and time-consuming nature. Therefore, the single-indicator transpulmonary thermodilution (TPTD) technique to measure extravascular lung water (EVLW) has been used in the clinical setting. Advances in artificial intelligence (AI) have now enabled CT images of inhomogeneous lungs to be segmented automatically by an intensive care physician with no prior radiology training within a relatively short time. Nevertheless, there is a paucity of data validating the quantification of pulmonary edema using automated lung segmentation on CT compared with TPTD.MethodsA retrospective study (January 2016 to December 2021) analyzed patients with ARDS, admitted to the intensive care unit of the Department of Anesthesiology and Critical Care Medicine, University Hospital Mannheim, who underwent a chest CT scan and hemodynamic monitoring using TPTD at the same time. Pulmonary edema was estimated using manually and automated lung segmentation on CT and then compared to the pulmonary edema calculated from EVLW determined using TPTD.Results145 comparative measurements of pulmonary edema with TPTD and CT were included in the study. Estimating pulmonary edema using either automated lung segmentation on CT or TPTD showed a low bias overall (− 104 ml) but wide levels of agreement (upper: 936 ml, lower: − 1144 ml). In 13% of the analyzed CT scans, the agreement between the segmentation of the AI algorithm and a dedicated investigator was poor. Manual segmentation and automated segmentation adjusted for contrast agent did not improve the agreement levels.ConclusionsAutomated lung segmentation on CT can be considered an unbiased but imprecise measurement of pulmonary edema in mechanically ventilated patients with ARDS.

High-Precision Detection of Lung Adenocarcinoma Using Augmented VGG16 and Transfer Learning

Objectives: The research aims to enhance the effectiveness of the deep learning model for detecting lung adenocarcinoma using VGG16 CNN with transfer learning on medical images. The proposed model addresses the overfitting issues of existing models, thereby improving lung cancer detection accuracy. Methods: The lung CT scan images are analyzed for detection and classification using VGG16, leveraging transfer learning techniques. The dataset includes labeled lung images from LUNA 16, Kaggle, and other datasets, which are augmented to introduce variability and reduce overfitting. The images are resized format to 224 × 224 pixels. During the validation and training phases, the model's performance is evaluated based on accuracy, and precision. Findings: The adjusted VGG16 model attains a training accuracy of 99.42%, a validation accuracy of 99.13%, and a validation precision of 95.45%, indicating strong generalization capabilities, outperforming other existing models. Novelty: This research presents a new method for detecting lung cancer by combining advanced preprocessing and data augmentation techniques with the VGG16 architecture using transfer learning. This combination greatly enhances the model's ability to detect cancer accurately, setting a new standard in the field. Keywords: Lung cancer, VGG16, Transfer learning, Data augmentation, medical imaging, CT (Computer Tomography)

Implementing verifiable oncological imaging by quality assurance and optimization (i‑Violin) : Protocol for aEuropean multicenter study.

Advanced imaging techniques play apivotal role in oncology. Alarge variety of computed tomography (CT) scanners, scan protocols, and acquisition techniques have led to awide range in image quality and radiation exposure. This study aims at implementing verifiable oncological imaging by quality assurance and optimization (i-Violin) through harmonizing image quality and radiation dose across Europe. The 2‑year multicenter implementation study outlined here will focus on CT imaging of lung, stomach, and colorectal cancer and include imaging for four radiological indications: diagnosis, radiation therapy planning, staging, and follow-up. Therefore, 480 anonymized CT data sets of patients will be collected by the associated university hospitals and uploaded to arepository. Radiologists will determine key abdominopelvic structures for image quality assessment by consensus and subsequently adapt apreviously developed lung CT tool for the objective evaluation of image quality. The quality metrics will be evaluated for their correlation with perceived image quality and the standardized optimization strategy will be disseminated across Europe. The results of the outlined study will be used to obtain European reference data, to build teaching programs for the developed tools, and to create aculture of optimization in oncological CT imaging. The study protocol and rationale for i‑Violin, aEuropean approach for standardization and harmonization of image quality and optimization of CT procedures in oncological imaging, is presented. Future results will be disseminated across all EU member states, and i‑Violin is thus expected to have asustained impact on CT imaging for cancer patients across Europe.

Bronchiectasis in a patient with Autoimmune Polyendocrinopathy-Candidiasis-Ectodermal Dystrophy: a case report

BackgroundThe rare monogenic syndrome Autoimmune Polyendocrinopathy-Candidiasis-Ectodermal Dystrophy (APECED) leads to multisystemic autoimmunity with possible lung involvement. Autoimmune pneumonitis is a rare manifestation, with bronchiectasis being the most frequent radiologic pattern, and may lead to fatal outcome. The Sardinian population in Italy has a high incidence of APECED, although no case of lung manifestation has been reported yet in this cohort. This is the case of a Sardinian APECED patient referred to a bronchiectasis clinic. Our aim is to raise awareness and screen these patients earlier for pulmonary involvement and to initiate multidisciplinary treatment for better outcome.Case presentationA 49-year-old female native of Sardinia from consanguineous parents was diagnosed with APECED in childhood and was referred to our bronchiectasis clinic in March 2023. In addition to typical APECED features, she reported recurrent respiratory infections since childhood, chronic purulent sputum and a hospitalization for pneumonia. She came to our attention with a recent isolation of P. aeruginosa on sputum culture and diffuse cylindrical and varicoid bronchiectasis on her first CT scan. She underwent aetiologic screening for bronchiectasis with no evidence of another cause of disease. Lung treatment was optimized according to bronchiectasis guidelines, and during follow-up the patient developed methicillin-resistant Staphylococcus aureus (MRSA) infection and M. intracellulare pulmonary disease. The patient was offered P. aeruginosa eradication treatment with intravenous antibiotics and initiation of antimycobacterial therapy.ConclusionThis is the first documented lung involvement case of APECED in a Sardinian patient, and the first patient reported to enter a bronchiectasis program. The patient was prescribed lung imaging late in time when bronchiectasis complications were already present. Our case report highlights the need for early pulmonary screening and multidisciplinary management in patients with APECED.

A patient with unexplained hypoxemia after a fall diagnosed with platypnea orthodeoxia syndrome: approaches to resolving discrepancies between level of hypoxemia and clinical presentation.

Platypnea orthodeoxia syndrome (POS) is a rare cause of hypoxemia. Diagnosis of POS is challenging, requiring a high index of clinical suspicion, special investigations, and collaboration with multiple specialists. We describe an 86-yr-old male who presented to the emergency department with hip pain after a witnessed fall. He was noted to be hypoxemic at presentation with a peripheral oxygen saturation (SpO2) of 84% on room air, with an inadequate increase in oxygenation after administration of a fractional concentration of inspired oxygen (FIO2) of 1.00. A chest radiograph, computed tomography pulmonary angiogram, and Doppler ultrasound of the liver were unremarkable. In the supine position with an FIO2 of 0.65, his SpO2 and arterial partial pressure of oxygen (PaO2) (96% and 74mm Hg, respectively) increased significantly relative to the seated position (84% and 50mm Hg, respectively). Contrast transthoracic echocardiography (TTE) showed a large patent foramen ovale (PFO) with right-to-left shunt. Transthoracic echocardiography showed rotation of the patient's heart, enabling direct alignment of the inferior vena cava with the PFO, creating a large anatomical right-to-left shunt in the seated position. Right heart catheterization confirmed a large PFO with normal right-sided heart pressures. He was treated with a septal occlusion and his SpO2 in the seated position improved immediately. The patient was discharged home without requiring supplemental oxygen. Platypnea orthodeoxia syndrome is a rare presentation of hypoxemia. Positional changes in oxygenation are the cardinal feature of POS. Discordance between lung imaging and the severity of hypoxemia should prompt investigation for an intracardiac shunt, which can occur in POS even in the absence of increased right-sided heart pressures. Either contrast TTE or transesophageal echocardiography is necessary to make this diagnosis.

Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

BackgroundPulmonary embolism (PE) is life-threatening and requires timely and accurate diagnosis, yet current imaging methods, like computed tomography pulmonary angiography, present limitations, particularly for patients with contraindications to iodinated contrast agents. We aimed to develop a quantitative texture analysis pipeline using machine learning (ML) based on non-contrast thoracic computed tomography (CT) scans to discover intensity and textural features correlated with regional lung perfusion (Q) physiology and pathology and synthesize voxel-wise Q surrogates to assist in PE diagnosis.MethodsWe retrospectively collected 99mTc-labeled macroaggregated albumin Q-SPECT/CT scans from patients suspected of PE, including an internal dataset of 76 patients (64 for training, 12 for testing) and an external testing dataset of 49 patients. Quantitative CT features were extracted from segmented lung subregions and underwent a two-stage feature selection pipeline. The prior-knowledge-driven preselection stage screened for robust and non-redundant perfusion-correlated features, while the data-driven selection stage further filtered features by fitting ML models for classification. The final classification model, trained with the highest-performing PE-associated feature combination, was evaluated in the testing cohorts based on the Area Under the Curve (AUC) for subregion-level predictability. The voxel-wise Q surrogate was then synthesized using the final selected feature maps (FMs) and model score maps (MSMs) to investigate spatial distributions. The Spearman correlation coefficient (SCC) and Dice similarity coefficient (DSC) were used to assess the spatial consistency between FMs or MSMs and Q-SPECT scans.ResultsThe optimal model performance achieved an AUC of 0.863 during internal testing and 0.828 on the external testing cohort. The model identified a combination containing 14 intensity and textural features that were non-redundant, robust, and capable of distinguishing between high- and low-functional lung regions. Spatial consistency assessment in the internal testing cohort showed moderate-to-high agreement between MSMs and reference Q-SPECT scans, with median SCC of 0.66, median DSCs of 0.86 and 0.64 for high- and low-functional regions, respectively.ConclusionsThis study validated the feasibility of using quantitative texture analysis and a data-driven ML pipeline to generate voxel-wise lung perfusion surrogates, providing a radiation-free, widely accessible alternative to functional lung imaging in managing pulmonary vascular diseases.Clinical trial numberNot applicable.

Pathophysiological Markers of Acute Respiratory Distress Syndrome Severity Are Correlated With Ventilation-Perfusion Mismatch Measured by Electrical Impedance Tomography.

Pulmonary ventilation/perfusion (V/Q) mismatch measured by electrical impedance tomography (EIT) is associated with the outcome of patients with the acute respiratory distress syndrome (ARDS), but the underlying pathophysiological mechanisms have not been fully elucidated. The present study aimed to verify the correlation between relevant pathophysiological markers of ARDS severity and V/Q mismatch. Prospective observational study. General ICU of a university-affiliated hospital. Deeply sedated intubated adult patients with ARDS under controlled mechanical ventilation. Measures of V/Q mismatch by EIT, respiratory mechanics, gas exchange, lung imaging, and plasma biomarkers. Unmatched V/Q units were assessed by EIT as the fraction of ventilated nonperfused plus perfused nonventilated lung units. At the same time, plasma biomarkers with proven prognostic and mechanistic significance for ARDS (carbonic anhydrase 9 [CA9], hypoxia-inducible factor 1 [HIF1], receptor for advanced glycation endproducts [RAGE], angiopoietin 2 [ANG2], gas exchange, respiratory mechanics, and quantitative chest CT scans were measured. Twenty-five intubated ARDS patients were included with median unmatched V/Q units of 37.1% (29.2-49.2%). Unmatched V/Q units were correlated with plasma levels of CA9 (rho = 0.47; p = 0.01), HIF1 (rho = 0.40; p = 0.05), RAGE (rho = 0.46; p = 0.02), and ANG2 (rho = 0.42; p = 0.03). Additionally, unmatched V/Q units correlated with plateau pressure (r = 0.38; p = 0.05) and with the number of quadrants involved on chest radiograph (r = 0.73; p < 0.01). Regional unmatched V/Q units were correlated with the corresponding fraction of poorly aerated lung tissue (r = 0.62; p = 0.01) and of lung tissue weight (rho: 0.51; p = 0.04) measured by CT scan. In ARDS patients, unmatched V/Q units are correlated with pathophysiological markers of lung epithelial and endothelial dysfunction, increased lung stress, and lung edema. Unmatched V/Q units could represent a comprehensive marker of ARDS severity, reflecting the complex organ pathophysiology and reinforcing their prognostic significance.

Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing

Malignancy Detection in Lung and Colon Histopathology Images by Transfer Learning with Class Selective Image Processing

Beneficial effect of sequential treatment with high-dose steroids and short-course oral glucocorticoids in patients with severe influenza virus-associated pneumonia

Accumulating evidence supports that glucocorticoid treatment for viral pneumonia (VPA) can shorten the disease course and improve survival. However, currently, the use of glucocorticoids in treating VPA remains controversial. Moreover, a unified standard for the dosage and duration of glucocorticoid therapy has not been presented in published articles. A retrospective analysis was conducted in patients who were hospitalized for severe influenza virus-associated pneumonia, and they received sequential treatment with high-dose glucocorticoids and short-course oral glucocorticoids. Patients were followed up for 3 months. A total of 11 patients were included in the study (average age 56 years). There was no gender difference, but age and underlying diseases could be risk factors for severe influenza virus-associated pneumonia. The types of viruses causing pneumonia included influenza A/B. The main clinical symptoms of patients were fever, cough, sputum production, and dyspnea. Chest computed tomography showed multiple ground-glass shadows in the lobes, and the presence of bacterial and fungal infections was accompanied by consolidation shadows. After glucocorticoid therapy, the symptoms improved. None of the patients underwent tracheal intubation, and all survived. After a 3-month follow-up, lung CT absorption in all patients had reached more than 80%, and lung imaging absorption in 20% patients was complete. No serious complications occurred in any of the patients. Sequential treatment with high-dose steroids and short-course oral glucocorticoids may be helpful for reducing the tracheal intubation rate and mortality rate in patients with severe influenza virus-associated pneumonia. Additionally, short-course oral glucocorticoids may reduce pulmonary fibrosis in patients with severe influenza virus-associated pneumonia without any serious complications.

Lung MRI: The future is now.

Tomographic imaging via MRI can capture structural abnormalities at high resolution and reveal functional abnormalities if acquired during respiration. In infants and children with chronic lung diseases, MRI can serve as a safe and effective tool to characterize cardiorespiratory disease where PFTs are not feasible. Functional lung imaging via hyperpolarized Xe MRI has been shown to have very high sensitivity to regional airway obstruction, changes in alveolar-airspace size/structure, and gas-exchange due to abnormalities in the interstitium or blood-flow; the technique is routinely used in ages 5 and up. This summary outlines each of the functional measures currently available using Xe MRI.

Correlation between elderly patients with COPD and the impact on immunity in tuberculosis patients: A retrospective study.

The prevalence of chronic obstructive pulmonary disease (COPD) and tuberculosis (TB) is increasing globally, yet their comprehensive impact on the immune system remains underexplored. This study aimed to provide a thorough assessment of the immune status of patients with COPD and tuberculosis (TB-COPD), including their pulmonary conditions, immune cell responses, and changes in lymphocyte subpopulations. A total of 151 patients with TB-COPD patients were included, and clinical data were compared between the TB-COPD group and a group of TB patients without COPD (TB-NCOPD). Lung imaging findings and peripheral blood immune cell levels were compared between the 2 groups. Flow cytometry was used to analyze the absolute counts of lymphocyte subpopulations. The incidence of pulmonary lobe lesions and cavitation in the TB-COPD group aged 70 years or older was significantly higher than that in the control group. At the immune cell level, patients with TB-COPD showed a significant reduction in total lymphocytes, CD4+ T lymphocytes and CD4+/CD8+ ratio. Regardless of COPD status, the CD4+ T cell count in the CMV-infected group was significantly lower than that in the uninfected group (P < .05). Additionally, the CD4+/CD8+ ratio in the COPD + TB CMV + group was significantly lower than that in the uninfected group. Analysis of lymphocyte subpopulations revealed a decrease in the counts of CD4+ T lymphocytes in patients with TB-COPD, potentially associated with the chronic inflammatory state induced by COPD. The one-month treatment outcomes showed that the improvement rate in the control group was 70.58%, which was significantly higher than the 38.92% in the COPD + TB group (P < .001). We observed a significant increase in the number of pulmonary cavity patients in the TB-COPD group, suggesting that COPD may be a potential risk factor for the formation of pulmonary cavities in patients with TB. At the immune cell level, TB-COPD patients showed a notable decrease in lymphocytes and CD4+ T lymphocytes, implying that COPD combined with pulmonary TB may significantly affect the immune system, leading to a reduction in the counts of key immune cells.

Pulmonary Emphysema Disease Classification Model Using Deep Structured Techniques

Emphysema is normally employed in the sense of morphology, and thus, imaging the modalities has a significant part in detecting the disorder. Most importantly, the High-Resolution Computed Tomography (HRCT) is a trustworthy component for illustrating the emphysema pathology. Hence, the pulmonary emphysema diagnosis at the starting stage is very important and can secure the life of a human. When there is a failure in the technique, there is a possibility of the death that displays the importance of effective diagnosis. The endotracheal intubation’s delay is prevented by implementing distinct mechanisms that result in various requirements. The presented work develops a deep learning-aided pulmonary emphysema disease classification model that is implemented to defeat the above difficulties. At first, the images are accumulated from a standard dataset. Then, the collected images are provided to the phase of segmentation. Here, the newly developed Adaptive Trans-Residual Dense UNet (ATRDUNet) is employed to perform the segmentation process. Also, the recommended ATRDUNet model helps to locate the disease in the appropriate location. Moreover, the performance of the segmentation process is enriched to fine-tune the parameter using the Fitness Revised Position of Wild Horse Optimizer (FRP-WHO). While optimizing the parameters effectively, it tends to handle the complex issues to strengthen the model accurately. The segmented results from the ATRDUNet are passed to Vision Transformer-based Residual DenseNet with LSTM layer (ViTrans-RDLSTM) for getting the final classified outcome. To this end, the analysis of the suggested model is correlated to find the effectiveness of the recommended model. However, these results are examined with the help of standard performance measures. Also, the experimentation is carried out using the standard “Automatic emphysema detection using weakly labeled HRCT lung images” dataset to attain better outcomes in the developed model. The findings of the recommended approach show 95% in terms of accuracy. Also, the statistical analysis of the developed model shows 23.9%, 12.3%, 15.2%, and 13.0% better performance than BWO, CO, HBA, and WHO in terms of mean analysis to show enriched performance.

Mapping the amplitude and phase of dissolved 129Xe red blood cell signal oscillations with keyhole spectroscopic lung imaging.

To assess the regional amplitude and phase of dissolved 129Xe red blood cell (RBC) signal oscillations in the lung vasculature with keyhole spectroscopic imaging and to compare with previous methodology, which does not account for oscillation phase. 129Xe gas transfer was measured with a four-echo 3D radial spectroscopic imaging sequence. Keyhole reconstruction-based RBC signal oscillation amplitude mapping was applied retrospectively to data acquired from 28 healthy volunteers, 4 chronic thromboembolic pulmonary hypertension (CTEPH) patients, and 5 patients who were hospitalized due to COVID-19 pneumonia and had residual lung abnormalities. Using a sliding window keyhole reconstruction, maps of RBC oscillation amplitude were corrected for regional phase difference. Repeatability of the phase-adjusted oscillation amplitude was assessed in 8 healthy volunteers across three scans. With sliding window keyhole reconstruction, regional phase differences were observed in the RBC signal oscillations: mean phase = (0.27 ± 0.19) rad in healthy volunteers, (0.24 ± 0.13) rad in CTEPH patients, and (0.33 ± 0.19) rad in patients with post-COVID-19 residual lung abnormality. The oscillation amplitude and phase maps were more heterogeneous (i.e., they showed increased coefficient of variation) for the CTEPH patients. The RBC oscillation amplitude was repeatable, and the mean three-scan coefficient of variation was smaller when the phase adjustment was made (0.07 ± 0.04 compared with 0.16 ± 0.05). Sliding window keyhole reconstruction of radial dissolved 129Xe imaging reveals regional phase differences in the RBC oscillations, which are not captured when performing two phase keyhole reconstruction. This regional phase information may reflect the hemodynamic effect of the cardiac pulse wave in the pulmonary microvasculature.

Capacitively Coupled Electrical Impedance Tomography in Lung Imaging

Capacitively Coupled Electrical Impedance Tomography in Lung Imaging