Ruptured mediastinal pseudocyst with pancreaticopleural fistula formation in a 15-year-old boy presenting with white-out hemithorax: A case report

Pancreaticopleural fistula (PPF) is a rare complication of chronic pancreatitis described more commonly in adults with alcoholic and necrotizing pancreatitis. We report a rare case of ruptured mediastinal pseudocyst with the formation of PPF in a 15-year-old boy who presented with progressive dyspnea and large left-sided pleural effusion that recurred despite repeated drainage. On the basis of imaging findings and pleural fluid analysis, the diagnosis of PPF with ruptured mediastinal pseudocyst was made. The diagnosis of PPF should be considered in patients with non-resolving large left-sided pleural effusions. The diagnosis can be confirmed either by significantly raised amylase levels in pleural fluid or direct visualization of the fistula on Computed tomography/magnetic resonance cholangiopancreatography.

Automatic Deep Learning-Based Consolidation/Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia

<div>Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.</div>

Automatic Deep Learning-Based Consolidation/Collapse Classification in Lung Ultrasound Images for COVID-19 Induced Pneumonia

<div>Our automated deep learning-based approach identifies consolidation/collapse in LUS images to aid in the diagnosis of late stages of COVID-19 induced pneumonia, where consolidation/collapse is one of the possible associated pathologies. A common challenge in training such models is that annotating each frame of an ultrasound video requires high labelling effort. This effort in practice becomes prohibitive for large ultrasound datasets. To understand the impact of various degrees of labelling precision, we compare labelling strategies to train fully supervised models (frame-based method, higher labelling effort) and inaccurately supervised models (video-based methods, lower labelling effort), both of which yield binary predictions for LUS videos on a frame-by-frame level. We moreover introduce a novel sampled quaternary method which randomly samples only 10% of the LUS video frames and subsequently assigns (ordinal) categorical labels to all frames in the video based on the fraction of positively annotated samples. This method outperformed the inaccurately supervised video-based method of our previous work on pleural effusions. More surprisingly, this method outperformed the supervised frame-based approach with respect to metrics such as precision-recall area under curve (PR-AUC) and F1 score that are suitable for the class imbalance scenario of our dataset despite being a form of inaccurate learning. This may be due to the combination of a significantly smaller data set size compared to our previous work and the higher complexity of consolidation/collapse compared to pleural effusion, two factors which contribute to label noise and overfitting; specifically, we argue that our video-based method is more robust with respect to label noise and mitigates overfitting in a manner similar to label smoothing. Using clinical expert feedback, separate criteria were developed to exclude data from the training and test sets respectively for our ten-fold cross validation results, which resulted in a PR-AUC score of 73% and an accuracy of 89%. While the efficacy of our classifier using the sampled quaternary method must be verified on a larger consolidation/collapse dataset, when considering the complexity of the pathology, our proposed classifier using the sampled quaternary video-based method is clinically comparable with trained experts and improves over the video-based method of our previous work on pleural effusions.</div>

Some pleural effusions labeled as idiopathic could be produced by the inhalation of silica

Objectives Exposure to silica nanoparticles has been associated with pleural effusions (PEs) in animal models and case series. We hypothesized that some PEs labelled as “idiopathic” could, in fact, be secondary to inhalation of silica. Methods A retrospective case control study was designed utilizing a prospectively maintained pleural database. Cases, represented by idiopathic PEs, were matched by age and gender to control patients who had been diagnosed with malignant, cardiac, or infectious PEs. A survey consisting of questions about occupational life and possibility of silica inhalation was conducted. In a subgroup of patients, pleural fluid concentrations of silica were quantified by plasma atomic emission spectrometry analysis. Also, the pleural biopsy of a silica-exposed case was subjected to an energy dispersive X-ray spectroscopy (EDX) to identify the mineral, the size of which was determined by electron microscopy. Results A total of 118 patients (59 cases and 59 controls) completed the survey. There were 25 (42%, 95% CI 31–55%) and 13 (22%, 95% CI 13–34%) silica-exposed workers in case and control groups, respectively. The exposure attributable fraction was 0.62 (95% CI 0.14–0.83). Four of eight exposed cases showed detectable levels of silica in the pleural fluid (mean 2.37 mg/L), as compared to none of 16 tested controls. Silica nanoparticles of 6–7 nm were identified in the pleural biopsy of an exposed case patient. Conclusions It is plausible that some idiopathic PEs could actually be caused by occupational silica inhalation.

High-throughput concentration of rare malignant tumor cells from large-volume effusions by multistage inertial microfluidics

On-chip concentration of rare malignant tumor cells (MTCs) in malignant pleural effusions (MPEs) with a large volume is challenging. Previous microfluidic concentrators suffer from a low concentration factor (CF) and...

Th1/Th2 cytokine profiles differentiating tuberculous from malignant pleural effusions: A systematic review and meta-analysis

Review question / Objective: To clarify which one has a different predominance of Th1 and Th2 immune responses in malignant and tuberculous pleural effusions. We did a meta-analysis of the results published previously to assess the levels of Th1/Th2 cytokines in two types of pleural effusion and evaluated its ability to distinguish TPE from MPE. Condition being studied: Malignant and tuberculous pleural effusions are the two most common types of exudative pleural effusions, both of which can be seen with the typical accumulation of lymphocytes. Immune responses mediated by either the Th1 or Th2 subset dominate, depending on different types of pleural effusion. Thus, we performed a meta-analysis of all available studies to quantitatively evaluate the levels of Th1/Th2 cytokine profiles in TPE and MPE, as well as to assess the potential diagnostic value of these cytokines in discriminating TPE from MPE.

Pleuroskopi Sebagai Tindakan Diagnostik pada Paru

Pleuroscopy, also known as medical thoracoscopy, is a minimally invasive procedure that is used to examine and biopsy the pleural cavity as well as to perform therapeutic interventions. This procedure has a near-perfect diagnostic accuracy in malignant pleural effusions and tuberculosis. With a mortality rate of 0.1%, the complication rate is low (2% - 5%) and usually mild (subcutaneous emphysema, bleeding, infection). Objective : Increase knowledge of pleuroscopy as a diagnostic and therapeutic tool in lung disease. Method : This paper is based on a review of the literature on pleuroscopy. Conclusion : Pleuroscopy is a minimally invasive procedure that can be used to examine and biopsy the pleural cavity, as well as for therapeutic intervention. Complications are uncommon and usually minor. Sugestion : Other articles are required to increase knowledge about pleuroscopy in order to obtain more knowledge.

Video Assisted Thoracoscopic Surgery in Exudative Pleural Effusion and its Complication Management: An Experience in a Community Hospital

Background: Exudative pleural effusions are common presentation of pleural disease. Long standing pleural effusion might complicate with loculations and cortex formation. Video assisted thoracoscopic surgery can be a useful tool for the diagnosis and the management of the complications. The aim of the study is to determine the cause and treat the complications related to the exudative pleural effusions. Materials and Methods: It is a retrospective analysis of prospectively collected data of all patients with exudative pleural effusions subjected to surgical management. Data were collected over a period of four years in a community hospital in Kathmandu. Results: Of 38 patients who underwent Video assisted thoracoscopic surgery only 33 were eligible for analysis. Male to female ratio was 2.3:1 with male (23) dominance. Twenty six (78.8%) had lymphocyte predominance and 23 (69.7%) had Adenosine deaminase level of more than 40 International unit in pleural fluid analysis. In histopathological examination most common finding was granulomatous inflammation 13 (39.4%), 9 (27.3%) were malignancy and 9 (27.3%) were nonspecific chronic inflammation. Of malignancies adenocarcinoma 3 (9.09%) was the most common finding, mesothelioma 2(6.06%) and 4 (12.12%) other. Conclusion: Video assisted thoracoscopic surgery has a role to play in diagnosis of exudative pleural effusions, particularly when there is dilemma in diagnosis. Video assisted thoracoscopic surgery definitely has a role in diagnosis and treatment of the complications related to pleural effusions.