Complications due to ultrasound transthoracic cutting biopsy of peripheral pulmonary lesions and lesions in the chest wall and mediastinum.

INTRODUCTION: Evaluation of patients with peripheral lung lesions and lesions of the chest wall and mediastinum is challenging. The nature of the lesion identified by imaging studies can be determined by histological evaluation of biopsies. An important place in this direction is the ever-increasing popularity among thoracic surgeons of the transthoracic biopsy with a cutting needle under ultrasound control (US-TTCNB).

Clinical characteristics, radiological features and outcomes in pulmonary involvement of cryoglobulinemia.

BACKGROUND: Cryoglobulinemia with pulmonary involvement is rare, and its characteristics, radiological findings, and outcomes are still poorly understood. METHODS: Ten patients with pulmonary involvement of 491 cryoglobulinemia patients at Peking Union Medical College Hospital were enrolled in this retrospective study. We analyzed the characteristics, radiological features and management of pulmonary involvement patients, and compared with those of non-pulmonary involvement with cryoglobulinemia. RESULTS: The 10 patients with pulmonary involvement (2 males; median age, 53 years) included three patients with type I cryoglobulinemia and seven patients with mixed cryoglobulinemia. All of 10 patients were IgM isotype cryoglobulinemia. All type I patients were secondary to B-cell non-Hodgkin lymphoma. Four mixed patients were essential, and the remaining patients were secondary to infections (n = 2) and systemic lupus erythematosus (n = 1), respectively. Six patients had additional affected organs, including skin (60%), kidney (50%), peripheral nerves (30%), joints (20%), and heart (20%). The pulmonary symptoms included dyspnea (50%), dry cough (30%), chest tightness (30%), and hemoptysis (10%). Chest computed tomography (CT) showed diffuse ground-glass opacity (80%), nodules (40%), pleural effusions (30%), and reticulation (20%). Two patients experienced life-threatening diffuse alveolar hemorrhage. Five patients received corticosteroid-based regimens, and four received rituximab-based regimens. All patients on rituximab-based regimens achieved clinical remission. The estimated two-year overall survival (OS) was 40%. Patients with pulmonary involvement had significantly worse OS and progression-free survival than non-pulmonary involvement patients of cryoglobulinemia (P < 0.0001). CONCLUSIONS: A diagnosis of pulmonary involvement should be highly suspected for patients with cryoglobulinemia and chest CT-indicated infiltrates without other explanations. Patients with pulmonary involvement had a poor prognosis. Rituximab-based treatment may improve the outcome.

Pulmonary transplant complications: a radiologic review.

Lung transplantation has become the definitive treatment for end stage respiratory disease. Numbers and survival rates have increased over the past decade, with transplant recipients living longer and with greater comorbidities, resulting in greater complexity of care. Common and uncommon complications that occur in the immediate, early, intermediate, and late periods can have significant impact on the course of the transplant. Fortunately, advancements in surgery, medical care, and imaging as well as other diagnostics work to prevent, identify, and manage complications that would otherwise have a negative impact on survivability. This review will focus on contextualizing complications both categorically and chronologically, with highlights of specific imaging and clinical features in order to inform both radiologists and clinicians involved in post-transplant care.

DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features.

Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.

[Analysis of Influencing Factors of Complications for CT-guided 
Percutaneous Lung Biopsy].

BACKGROUND: Computed tomography guided percutaneous lung biopsy (CT-PLB) is a widely used method for the diagnosis of lung lesions. However, it is invasive, and the most common complications are pneumothorax and pulmonary hemorrhage, which can be life-threatening in severe cases. Therefore, the aim of this study is to analyze the independent risk factors affecting the occurrence of different complications of CT-PLB, so as to reduce the incidence of complications. METHODS: The 605 patients with complete clinical data who underwent CT-PLB in our hospital from May 2018 to December 2019 were retrospectively analyzed. According to the location of the lesions, they were divided into subpleural group and non-subpleural group. The patients were divided into pneumothorax group, pulmonary hemorrhage group, pneumothorax with pulmonary hemorrhage group and non-pneumothorax/pulmonary hemorrhage group according to the complications. The risk factors affecting the incidence of different complications and the independent risk factors of each complication were analyzed. RESULTS: The incidence of pneumothorax was 34.1%, the incidence of pulmonary hemorrhage was 28.1%, and the incidence of pneumothorax complicated with pulmonary hemorrhage was 10.8% (63 cases). The independent risk factor affecting the incidence of subpleural pneumothorax was lesion size (P=0.002). The independent risk factors affecting the occurrence of pneumothorax in the non-subpleural group were plain scan CT value (P=0.035), length of needle through lung tissue (P=0.003), and thickness of needle through chest wall (P=0.020). Independent risk factors affecting the occurrence of pulmonary hemorrhage in the non-subpleural group were length of needle through lung tissue (P<0.001), △CT value of needle travel area (P=0.001), lesion size (P=0.034) and body position (P=0.014). The independent risk factors affecting the co-occurrence of pneumothorax and pulmonary hemorrhage were the length of needle through lung tissue (P<0.001) and the △CT value of needle travel area (P<0.001). CONCLUSIONS: CT-PLB is a safe and effective diagnostic method, which of high diagnostic value for lung lesions. Selecting the appropriate puncture program can reduce complications such as pneumothorax and pulmonary hemorrhage, and improve diagnosis and treatment efficiency.

[Diffuse cystic lung disease]. / Cystische Lungenerkrankungen.

INTRODUCTION: Diffuse cystic lung disease (DCLD) represents a heterogeneous group of conditions, typically characterized by the presence of multiple thin-walled, predominantly round parenchymal lucencies. The increased accessibility of computed tomography (CT) underscores the growing relevance of a relatively rare group of diseases as more clinicians are confronted with the presence of multiple lung cysts on the chest CT scan. Although the etiology of these conditions is very diverse, the focus of the differential diagnosis revolves around four primary causative factors - Lymphangioleiomyomatosis (LAM), Pulmonary Langerhanscell histiocytosis (PLCH), Birt-Hogg-Dubé (BHD) and lymphoid interstitial pneumonia (LIP). Achieving an accurate diagnosis poses a challenge and typically necessitates lung biopsies; however, it is crucial for ensuring proper management.

Uncommon Causes of Interlobular Septal Thickening on CT Images and Their Distinguishing Features.

Interlobular septa thickening (ILST) is a common and easily recognized feature on computed tomography (CT) images in many lung disorders. ILST thickening can be smooth (most common), nodular, or irregular. Smooth ILST can be seen in pulmonary edema, pulmonary alveolar proteinosis, and lymphangitic spread of tumors. Nodular ILST can be seen in the lymphangitic spread of tumors, sarcoidosis, and silicosis. Irregular ILST is a finding suggestive of interstitial fibrosis, which is a common finding in fibrotic lung diseases, including sarcoidosis and usual interstitial pneumonia. Pulmonary edema and lymphangitic spread of tumors are the commonly encountered causes of ILST. It is important to narrow down the differential diagnosis as much as possible by assessing the appearance and distribution of ILST, as well as other pulmonary and extrapulmonary findings. This review will focus on the CT characterization of the secondary pulmonary lobule and ILST. Various uncommon causes of ILST will be discussed, including infections, interstitial pneumonia, depositional/infiltrative conditions, inhalational disorders, malignancies, congenital/inherited conditions, and iatrogenic causes. Awareness of the imaging appearance and various causes of ILST allows for a systematic approach, which is important for a timely diagnosis. This study highlights the importance of a structured approach to CT scan analysis that considers ILST characteristics, associated findings, and differential diagnostic considerations to facilitate accurate diagnoses.

A lung disease diagnosis algorithm based on 2D spectral features of ultrasound RF signals.

Lung diseases are commonly diagnosed based on clinical pathological indications criteria and radiological imaging tools (e.g., X-rays and CT). During a pandemic like COVID-19, the use of ultrasound imaging devices has broadened for emergency examinations by taking their unique advantages such as portability, real-time detection, easy operation and no radiation. This provides a rapid, safe, and cost-effective imaging modality for screening lung diseases. However, the current pulmonary ultrasound diagnosis mainly relies on the subjective assessments of sonographers, which has high requirements for the operator's professional ability and clinical experience. In this study, we proposed an objective and quantifiable algorithm for the diagnosis of lung diseases that utilizes two-dimensional (2D) spectral features of ultrasound radiofrequency (RF) signals. The ultrasound data samples consisted of a set of RF signal frames, which were collected by professional sonographers. In each case, a region of interest of uniform size was delineated along the pleural line. The standard deviation curve of the 2D spatial spectrum was calculated and smoothed. A linear fit was applied to the high-frequency segment of the processed data curve, and the slope of the fitted line was defined as the frequency spectrum standard deviation slope (FSSDS). Based on the current data, the method exhibited a superior diagnostic sensitivity of 98% and an accuracy of 91% for the identification of lung diseases. The area under the curve obtained by the current method exceeded the results obtained that interpreted by professional sonographers, which indicated that the current method could provide strong support for the clinical ultrasound diagnosis of lung diseases.

Increasing the scan-efficiency of pulmonary imaging at 0.55 T using iterative concomitant field and motion-corrected reconstruction.

PURPOSE: To develop an iterative concomitant field and motion corrected (iCoMoCo) reconstruction for isotropic high-resolution UTE pulmonary imaging at 0.55 T. METHODS: A free-breathing golden-angle stack-of-spirals UTE sequence was used to acquire data for 8 min with prototype and commercial 0.55 T MRI scanners. The data was binned into 12 respiratory phases based on superior-inferior navigator readouts. The previously published iterative motion corrected (iMoCo) reconstruction was extended to include concomitant field correction directly in the cost function. The reconstruction was implemented within the Gadgetron framework for inline reconstruction. Data were retrospectively reconstructed to simulate scan times of 2, 4, 6, and 8 min. Image quality was assessed using apparent SNR and image sharpness. The technique was evaluated in healthy volunteers and patients with known lung pathology including coronavirus disease 2019 infection, chronic granulomatous disease, lymphangioleiomyomatosis, and lung nodules. RESULTS: The technique provided diagnostic-quality images, and image quality was maintained with a slight loss in SNR for simulated scan times down to 4 min. Parenchymal apparent SNR was 4.33 ± 0.57, 5.96 ± 0.65, 7.36 ± 0.64, and 7.87 ± 0.65 using iCoMoCo with scan times of 2, 4, 6, and 8 min, respectively. Image sharpness at the diaphragm was comparable between iCoMoCo and reference images. Concomitant field corrections visibly improved the sharpness of anatomical structures away from the isocenter. Inline image reconstruction and artifact correction were achieved in <5 min. CONCLUSION: The proposed iCoMoCo pulmonary imaging technique can generate diagnostic quality images with 1.75 mm isotropic resolution in less than 5 min using a 6-min acquisition, on a 0.55 T scanner.

Incidence and risk factors for pulmonary hemorrhage after percutaneous CT-guided pulmonary nodule biopsy: an observational study.

To evaluate the current incidence of pulmonary hemorrhage and the potential factors contributing to its increased risk after percutaneous CT-guided pulmonary nodule biopsy and to summarize the technical recommendations for its treatment. In this observational study, patient data were collected from ten medical centers from April 2021 to April 2022. The incidence of pulmonary hemorrhage was as follows: grade 0, 36.1% (214/593); grade 1, 36.8% (218/593); grade 2, 18.9% (112/593); grade 3, 3.5% (21/593); and grade 4, 4.7% (28/593). High-grade hemorrhage (HGH) occurred in 27.2% (161/593) of the patients. The use of preoperative breathing exercises (PBE, p =0.000), semiautomatic cutting needles (SCN, p = 0.004), immediate contrast enhancement (ICE, p =0.021), and the coaxial technique (CoT, p = 0.000) were found to be protective factors for HGH. A greater length of puncture (p =0.021), the presence of hilar nodules (p = 0.001), the presence of intermediate nodules (p = 0.026), a main pulmonary artery diameter (mPAD) larger than 29 mm (p = 0.015), and a small nodule size (p = 0.014) were risk factors for high-grade hemorrhage. The area under the curve (AUC) was 0.783. These findings contribute to a deeper understanding of the risks associated with percutaneous CT-guided pulmonary nodule biopsy and provide valuable insights for developing strategies to minimize pulmonary hemorrhage.

Comparative Analysis of CT Findings and Clinical Outcomes in Adult Patients With Disseminated and Localized Pulmonary Nocardiosis.

BACKGROUND: Pulmonary nocardiosis is a rare opportunistic infection with occasional systemic dissemination. This study aimed to investigate the computed tomography (CT) findings and prognosis of pulmonary nocardiosis associated with dissemination. METHODS: We conducted a retrospective analysis of patients diagnosed with pulmonary nocardiosis between March 2001 and September 2023. We reviewed the chest CT findings and categorized them based on the dominant CT findings as consolidation, nodules and/or masses, consolidation with multiple nodules, and nodular bronchiectasis. We compared chest CT findings between localized and disseminated pulmonary nocardiosis and identified significant prognostic factors associated with 12-month mortality using multivariate Cox regression analysis. RESULTS: Pulmonary nocardiosis was diagnosed in 75 patients, of whom 14 (18.7%) had dissemination, including involvement of the brain in 9 (64.3%) cases, soft tissue in 3 (21.4%) cases and positive blood cultures in 3 (21.4%) cases. Disseminated pulmonary nocardiosis showed a higher frequency of cavitation (64.3% vs. 32.8%, P = 0.029) and pleural effusion (64.3% vs. 29.5%, P = 0.014) compared to localized infection. The 12-month mortality rate was 25.3%. The presence of dissemination was not a significant prognostic factor (hazard ratio [HR], 0.80; confidence interval [CI], 0.23-2.75; P = 0.724). Malignancy (HR, 9.73; CI, 2.32-40.72; P = 0.002), use of steroid medication (HR, 3.72; CI, 1.33-10.38; P = 0.012), and a CT pattern of consolidation with multiple nodules (HR, 4.99; CI, 1.41-17.70; P = 0.013) were associated with higher mortality rates. CONCLUSION: Pulmonary nocardiosis with dissemination showed more frequent cavitation and pleural effusion compared to cases without dissemination, but dissemination alone did not affect the mortality rate of pulmonary nocardiosis.

An elderly woman with Birt-Hogg-Dubé syndrome having multiple pulmonary cysts mimicking lymphangioleiomyomatosis.

The characteristics of the pulmonary cysts on the high-resolution computed tomography (HRCT) chest images are an important diagnostic clue to distinguish among cystic lung diseases. The diagnostic accuracy of HRCT was reported to be as high as 90% by experienced pulmonologists and radiologists. Herein, we report the case of an elderly woman with Birt-Hogg-Dubé syndrome (BHDS) whose HRCT images displayed lymphangioleiomyomatosis-like features of the pulmonary cysts, rendering it difficult for us to diagnose BHDS. This case illustrates the significance of a thorough anamnesis, physical examination, and skin biopsy of facial papules to establish an accurate diganosis.

[Congenital pulmonary malformations : Diagnosis and treatment]. / Kongenitale Lungenfehlbildungen : Diagnostik und Therapie.

PERFORMANCE: Congenital pulmonary malformations (CPM) are rare and can be associated with high morbidity. Clinical presentation, diagnostic procedures, imaging, and therapy of CPM are discussed. ACHIEVEMENTS: Today, most CPM can be diagnosed prenatally by ultrasound. Postnatally, respiratory symptoms up to respiratory failure and recurrent lower respiratory tract infection are typical findings. Due to low diagnostic accuracy of chest x­ray in CPM, all children with prenatal diagnosis of CPM or postnatally suspected CPM should undergo cross-sectional imaging. PRACTICAL RECOMMENDATIONS: Based on imaging alone, the various subtypes of CPM cannot be definitively differentiated, which is why histological confirmation remains the gold standard. Surgical resection is the standard of care with minimally invasive procedures increasingly being employed. In certain situations, a watch-and-wait approach is possible.

Chest CT Image based Lung Disease Classification - A Review.

Computed tomography (CT) scans are widely used to diagnose lung conditions due to their ability to provide a detailed overview of the body's respiratory system. Despite its popularity, visual examination of CT scan images can lead to misinterpretations that impede a timely diagnosis. Utilizing technology to evaluate images for disease detection is also a challenge. As a result, there is a significant demand for more advanced systems that can accurately classify lung diseases from CT scan images. In this work, we provide an extensive analysis of different approaches and their performances that can help young researchers to build more advanced systems. First, we briefly introduce diagnosis and treatment procedures for various lung diseases. Then, a brief description of existing methods used for the classification of lung diseases is presented. Later, an overview of the general procedures for lung disease classification using machine learning (ML) is provided. Furthermore, an overview of recent progress in ML-based classification of lung diseases is provided. Finally, existing challenges in ML techniques are presented. It is concluded that deep learning techniques have revolutionized the early identification of lung disorders. We expect that this work will equip medical professionals with the awareness they require in order to recognize and classify certain medical disorders.

A lung cyst harbouring a congenital pulmonary varix.

A non-smoker man in his second decade presented to a medical centre with intermittent haemoptysis over 2 years. The haemoptysis was infrequent initially to be ignored, but later, the episodes increased in amount and frequency. Routine blood tests including coagulation profile showed normal results. The chest radiography and echocardiography were normal. The contrast-enhanced CT scan of the chest showed a cystic lesion with intracystic abnormality and surrounding ground-glass opacity in the left upper lobe. The CT pulmonary angiography and invasive pulmonary angiography showed the abnormality to be a dilated pulmonary vessel draining into the left atrium, thereby confirming the diagnosis of congenital pulmonary varix contained within a lung cyst. The patient underwent a successful lobectomy following which he experienced no further haemoptysis.

An unusual case of metastatic trophoblastic neoplasm presenting with diffuse cystic lung disease and pulmonary artery pseudoaneurysms in a teenager.

Diffuse cystic lung diseases (DCLDs) are a diverse group of lung disorders characterized by the presence of multiple air filled cysts within the lung tissue. These cysts are thin walled and surrounded by normal lung tissue. In adults, DCLD can be associated with various conditions such as lymphangioleiomyomatosis (LAM), Langerhans cell histiocytosis, cancers, and more. In children, DCLD is often linked to lung developmental abnormalities, with bronchopulmonary dysplasia being a common cause. Patients with pulmonary cysts are typically asymptomatic, but some may experience mild symptoms or pneumothorax. While DCLD in children is rarely due to malignancy, metastatic lung disease can be a cause. It is important for clinicians to be aware of the possibility of metastatic lung disease when encountering DCLD.

A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.

BACKGROUND: Lung diseases, both infectious and non-infectious, are the most prevalent cause of mortality overall in the world. Medical research has identified pneumonia, lung cancer, and Corona Virus Disease 2019 (COVID-19) as prominent lung diseases prioritized over others. Imaging modalities, including X-rays, computer tomography (CT) scans, magnetic resonance imaging (MRIs), positron emission tomography (PET) scans, and others, are primarily employed in medical assessments because they provide computed data that can be utilized as input datasets for computer-assisted diagnostic systems. Imaging datasets are used to develop and evaluate machine learning (ML) methods to analyze and predict prominent lung diseases. OBJECTIVE: This review analyzes ML paradigms, imaging modalities' utilization, and recent developments for prominent lung diseases. Furthermore, the research also explores various datasets available publically that are being used for prominent lung diseases. METHODS: The well-known databases of academic studies that have been subjected to peer review, namely ScienceDirect, arXiv, IEEE Xplore, MDPI, and many more, were used for the search of relevant articles. Applied keywords and combinations used to search procedures with primary considerations for review, such as pneumonia, lung cancer, COVID-19, various imaging modalities, ML, convolutional neural networks (CNNs), transfer learning, and ensemble learning. RESULTS: This research finding indicates that X-ray datasets are preferred for detecting pneumonia, while CT scan datasets are predominantly favored for detecting lung cancer. Furthermore, in COVID-19 detection, X-ray datasets are prioritized over CT scan datasets. The analysis reveals that X-rays and CT scans have surpassed all other imaging techniques. It has been observed that using CNNs yields a high degree of accuracy and practicability in identifying prominent lung diseases. Transfer learning and ensemble learning are complementary techniques to CNNs to facilitate analysis. Furthermore, accuracy is the most favored metric for assessment.