Diagnostics in Pleural Disease

Clinical overview of the physiology and pathophysiology of pleural fluid movement: a narrative review

In physiological conditions, the pleural space couples the lung with the chest wall and contains a small amount of fluid in continuous turnover. The volume of pleural fluid is the result from the balance between the entry of fluid through the pleural capillaries and drainage by the lymphatics in the most dependent areas of the parietal pleura. Fluid filtration is governed by Starling forces, determined by the hydrostatic and oncotic pressures of the capillaries and the pleural space. The reabsorption rate is 28 times greater than the rate of pleural fluid production. The mesothelial layer of the inner lining of the pleural space is metabolically active and also plays a role in the production and reabsorption of pleural fluid. Pleural effusion occurs when the balance between the amount of fluid that enters the pleural space and the amount that is reabsorbed is disrupted. Alterations in hydrostatic or oncotic pressure produce a transudate, but they do not cause any structural damage to the pleura. In contrast, disturbances in fluid flow (increased filtration or decreased reabsorption) produce an exudate via several mechanisms that cause damage to pleural layers. Thus, cellular processes and the inflammatory and immune reactions they induce determine the composition of pleural fluid. Understanding the underlying pathophysiological processes of pleural effusion, especially cellular processes, can be useful in establishing its aetiology.

Image-Assısted Pleural Needle Biopsy or Medical Thoracoscopy: Which Method for Which Patient? A Randomızed Controlled Trial

BACKGROUND

Image-guided or assisted needle biopsies and the increasing use of medical thoracoscopy (MT) have increased the diagnostic accuracy of pleural diseases significantly. However, no consensus exists regarding which patients with pleural effusion should undergo MT and which patients should undergo image-guided or assisted needle biopsy as the first procedure to ensure greater diagnostic accuracy.

RESEARCH QUESTION

Which biopsy method is more appropriate for which patient to provide the highest diagnostic accuracy in the diagnosis of pleural effusion?

STUDY DESIGN AND METHODS

This prospective, randomized, parallel study included 228 patients with undiagnosed exudative pleural effusion. Patients were divided into two groups based on CT scan findings. Group 1 included patients with pleural effusion only. Group 2 included patients with pleural thickening or lesion in addition to pleural effusion. Patients in each group were assigned randomly to an image-assisted Abrams needle pleural biopsy (IA-ANPB) or MT arm. The diagnostic sensitivity, reliability, and safety were determined for both groups.

RESULTS

The false-negative rate was 30.3% for the IA-ANPB arm and 3.1% for the MT arm in group 1. The same rates were 11.9% for IA-ANPB and 4.7% for MT in group 2. In group 1, the sensitivity for the IA-ANPB arm was 69.7%, and the negative likelihood ratio was 0.30. The same rates for the MT arm were 96.9% and 0.03 (P = .009). In group 2, these values were 88.1% and 0.12 for the IA-ANPB arm and 95.4% and 0.05 for the MT arm (P = .207). The rate of complications between the two biopsy methods was not different (8.5% and 15.8%, respectively; P = .107).

INTERPRETATION

MT showed a high diagnostic success in all patients with pleural fluid. However, IA-ANPB showed similar diagnostic success as MT in patients with pleural effusion and associated pleural thickening or lesions. Therefore, in the latter case, IA-ANPB could be preferable to MT.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT05428891; URL: www.

CLINICALTRIALS

gov.

Natural antibodies targeting LPS in pleural effusions of various etiologies

Respiratory infection, cancer, and heart failure can cause abnormal accumulation of fluid in the pleural cavity. The immune responses within the cavity are orchestrated by leucocytes that reside in the serosal-associated lymphoid tissue. Natural antibodies (NAbs) are abundant in the serum (S) having a major role in systemic and mucosal immunity; however, their occurrence in pleural fluid (PF) remains an open question. Our aim herein was to detect and measure the levels of NAbs (IgM, IgG, IgA) targeting lipopolysaccharides (LPS) in both the pleural fluid and the serum of 78 patients with pleural effusions (PEs) of various etiologies. The values of anti-LPS NAb activity were extracted through a normalization step regarding the total IgM, IgG, and IgA levels, all determined by in-house ELISA. In addition, the ratios of PF/S values were analyzed further with other critical biochemical parameters from pleural fluids. Anti-LPS NAbs of all Ig classes were detected in most of the samples, while a significant increase of anti-LPS activity was observed in infectious and noninfectious compared with malignant PEs. Multivariate linear regression confirmed a negative correlation of IgM and IgA anti-LPS PF/S ratio with malignancy. Moreover, anti-LPS NAbs PF/S measurements led to increased positive and negative predictive power in ROC curves generated for the discrimination between benign and malignant PEs. Our results highlight the role of anti-LPS NAbs in the pleural cavity and demonstrate the potential translational impact that should be further explored.NEW & NOTEWORTHY Here we describe the detection and quantification of natural antibodies (NAbs) in the human pleural cavity. We show for the first time that IgM, IgG, and IgA anti-LPS natural antibodies are detected and measured in pleural effusions of infectious, noninfectious, and malignant etiologies and provide clinical correlates to demonstrate the translational impact of our findings.

Advances in the diagnosis and follow-up of pleural lesions: a scoping review

INTRODUCTION

Pleural lesions may have heterogeneous presentation and causes. In recent years, there have been significant advances in pleural lesions diagnostics. The aim of this review is to provide an overview of the state-of-the-art, and recent updates for diagnostic modalities and monitoring regimes for pleural lesions.

AREAS COVERED

A literature search was conducted through PubMed and Web of Science for relevant articles published from 1 January 2000- 1 March 2023. This article critically appraises the radiological modalities and biopsy techniques that are employed in pleural lesions diagnostics, including chest radiography, thoracic ultrasound, computed tomography, F-fluorodeoxyglycose positron emission tomography, magnetic resonance imaging, percutaneous, and thoracoscopic pleural biopsies with reference to their strengths, limitations, and clinical use. The review asserts also the available literature regarding monitoring algorithms.

EXPERT OPINION

Despite the recent advances in the field, there are several key areas for improvement, including the development and validation of minimal invasive methods and tools for risk stratification, the integration of multi-omics technologies, the implementation of standardized, evidence-based diagnostic and monitoring guidelines and increased focus on research and patient-centric approaches. The broad establishment of dedicated pleural clinics may significantly assist toward this direction.

Infective Pleural Effusions-A Comprehensive Narrative Review Article

Infective pleural effusions are mainly represented by parapneumonic effusions and empyema. These conditions are a spectrum of pleural diseases that are commonly encountered and carry significant mortality and morbidity rates reaching upwards of 50%. The causative etiology is usually an underlying bacterial pneumonia with the subsequent seeding of the infectious culprit and inflammatory agents to the pleural space leading to an inflammatory response and fibrin deposition. Radiographical evaluation through a CT scan or ultrasound yields high specificity and sensitivity, with features such as septations or pleural thickening indicating worse outcomes. Although microbiological yields from pleural studies are around 56% only, fluid analysis assists in both diagnosis and prognosis by evaluating pH, glucose, and other biomarkers such as lactate dehydrogenase. Management centers around antibiotic therapy for 2-6 weeks and the drainage of the infected pleural space when the effusion is complicated through tube thoracostomies or surgical intervention. Intrapleural enzymatic therapy, used to increase drainage, significantly decreases treatment failure rates, length of hospital stay, and surgical referrals but carries a risk of pleural hemorrhage. This comprehensive review article aims to define and delineate the progression of parapneumonic effusions and empyema as well as discuss pathophysiology, diagnostic, and treatment modalities with aims of broadening the generalist's understanding of such complex disease by reviewing the most recent and relevant high-quality evidence.

Deep learning model for pleural effusion detection via active learning and pseudo-labeling: a multisite study

BACKGROUND

The study aimed to develop and validate a deep learning-based Computer Aided Triage (CADt) algorithm for detecting pleural effusion in chest radiographs using an active learning (AL) framework. This is aimed at addressing the critical need for a clinical grade algorithm that can timely diagnose pleural effusion, which affects approximately 1.5 million people annually in the United States.

METHODS

In this multisite study, 10,599 chest radiographs from 2006 to 2018 were retrospectively collected from an institution in Taiwan to train the deep learning algorithm. The AL framework utilized significantly reduced the need for expert annotations. For external validation, the algorithm was tested on a multisite dataset of 600 chest radiographs from 22 clinical sites in the United States and Taiwan, which were annotated by three U.S. board-certified radiologists.

RESULTS

The CADt algorithm demonstrated high effectiveness in identifying pleural effusion, achieving a sensitivity of 0.95 (95% CI: [0.92, 0.97]) and a specificity of 0.97 (95% CI: [0.95, 0.99]). The area under the receiver operating characteristic curve (AUC) was 0.97 (95% DeLong's CI: [0.95, 0.99]). Subgroup analyses showed that the algorithm maintained robust performance across various demographics and clinical settings.

CONCLUSION

This study presents a novel approach in developing clinical grade CADt solutions for the diagnosis of pleural effusion. The AL-based CADt algorithm not only achieved high accuracy in detecting pleural effusion but also significantly reduced the workload required for clinical experts in annotating medical data. This method enhances the feasibility of employing advanced technological solutions for prompt and accurate diagnosis in medical settings.

Lung Ultrasound Signs: The Beginning. Part 3-An Accademia di Ecografia Toracica Comprehensive Review on Ultrasonographic Signs and Real Needs

Over the last 20 years, scientific literature and interest on chest/lung ultrasound (LUS) have exponentially increased. Interpreting mixed-anatomical and artifactual-pictures determined the need of a proposal of a new nomenclature of artifacts and signs to simplify learning, spread, and implementation of this technique. The aim of this review is to collect and analyze different signs and artifacts reported in the history of chest ultrasound regarding normal lung, pleural pathologies, and lung consolidations. By reviewing the possible physical and anatomical interpretation of these artifacts and signs reported in the literature, this work aims to present the AdET (Accademia di Ecografia Toracica) proposal of nomenclature and to bring order between published studies.

Development and validation of a radiomics nomogram for diagnosis of malignant pleural effusion

OBJECTIVE

We aimed to develop a radiomics nomogram based on computed tomography (CT) scan features and high-throughput radiomics features for diagnosis of malignant pleural effusion (MPE).

METHODS

In this study, 507 eligible patients with PE (207 malignant and 300 benign) were collected retrospectively. Patients were divided into training (n = 355) and validation cohorts (n = 152). Radiomics features were extracted from initial unenhanced CT images. CT scan features of PE were also collected. We used the variance threshold algorithm and least absolute shrinkage and selection operator (LASSO) to select optimal features to build a radiomics model for predicting the nature of PE. Univariate and multivariable logistic regression analyzes were used to identify significant independent factors associated with MPE, which were then included in the radiomics nomogram.

RESULTS

A total of four CT features were retained as significant independent factors, including massive PE, obstructive atelectasis or pneumonia, pleural thickening > 10 mm, and pulmonary nodules and/or masses. The radiomics nomogram constructed from 13 radiomics parameters and four CT features showed good predictive efficacy in training cohort [area under the curve (AUC) = 0.926, 95% CI 0.894, 0.951] and validation cohort (AUC = 0.916, 95% CI 0.860, 0.955). The calibration curve and decision curve analysis showed that the nomogram helped differentiate MPE from benign pleural effusion (BPE) in clinical practice.

CONCLUSION

This study presents a nomogram model incorporating CT scan features and radiomics features to help physicians differentiate MPE from BPE.

COVID-19-Related Pleural Diseases

Coronavirus disease 2019 (COVID-19)-related pleural diseases are now well recognized. Since the beginning of the pandemic, increasing cases of pleural diseases including pneumothorax, pneumomediastinum, and pleural effusion with severe COVID-19 infection have attracted the attention of physicians and are not incidental or due to barotrauma. The complicated course of COVID-19 illness highlights the complex pathophysiological underpinnings of pleural complications. The management of patients with pneumothorax and pneumomediastinum is challenging as the majority require assisted ventilation; physicians therefore appear to have a low threshold to intervene. Conversely, pleural effusion cases, although sharing some similar patient characteristics with pneumothorax and pneumomediastinum, are in general managed more conservatively. The evidence suggests that patients with COVID-19-related pleural diseases, either due to air leak or effusion, have more severe disease with a worse prognosis. This implies that prompt recognition of these complications and targeted management are key to improve outcomes.

Assessment of a Large-Scale Unbiased Malignant Pleural Effusion Proteomics Study of a Real-Life Cohort

Simple Summary

Pleural effusion (PE) occurs as a consequence of various pathologies. Malignant effusion due to lung cancer is one of the most frequent causes. A method for accurate differentiation of malignant from benign PE is an unmet clinical need. Proteomics profiling of PE has shown promising results. However, mass spectrometry (MS) analysis typically involves the tedious elimination of abundant proteins before analysis, and clinical annotation of proteomics profiled cohorts is limited. This study compares the proteomes of malignant PE and nonmalignant PE, identifies lung cancer malignant markers in agreement with other studies, and identifies markers strongly associated with patient survival.

Abstract

Background: Pleural effusion (PE) is common in advanced-stage lung cancer patients and is related to poor prognosis. Identification of cancer cells is the standard method for the diagnosis of a malignant PE (MPE). However, it only has moderate sensitivity. Thus, more sensitive diagnostic tools are urgently needed. Methods: The present study aimed to discover potential protein targets to distinguish malignant pleural effusion (MPE) from other non-malignant pathologies. We have collected PE from 97 patients to explore PE proteomes by applying state-of-the-art liquid chromatography-mass spectrometry (LC-MS) to identify potential biomarkers that correlate with immunohistochemistry assessment of tumor biopsy or with survival data. Functional analyses were performed to elucidate functional differences in PE proteins in malignant and benign samples. Results were integrated into a clinical risk prediction model to identify likely malignant cases. Sensitivity, specificity, and negative predictive value were calculated. Results: In total, 1689 individual proteins were identified by MS-based proteomics analysis of the 97 PE samples, of which 35 were diagnosed as malignant. A comparison between MPE and benign PE (BPE) identified 58 differential regulated proteins after correction of the p-values for multiple testing. Furthermore, functional analysis revealed an up-regulation of matrix intermediate filaments and cellular movement-related proteins. Additionally, gene ontology analysis identified the involvement of metabolic pathways such as glycolysis/gluconeogenesis, pyruvate metabolism and cysteine and methionine metabolism. Conclusion: This study demonstrated a partial least squares regression model with an area under the curve of 98 and an accuracy of 0.92 when evaluated on the holdout test data set. Furthermore, highly significant survival markers were identified (e.g., PSME1 with a log-rank of 1.68 × 10⁻⁶).

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Background

The diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.

Objectives

The present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.

Methods

A total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.

Results

Seven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942-0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932-0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956-0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.

Conclusions

The present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

Contrast-enhanced ultrasound guided pleural biopsy improves diagnostic confidence for pleural based lesions: a 3-year prospective study

Objective

To evaluate the accuracy and safety of contrast-enhanced ultrasound (CEUS) guided biopsy in the diagnosis of radiologically determined pleural based lesions.

Method

A prospective study was conducted on patients with radiologically determined pleural based lesions. Patients who met the inclusion criteria received pleural biopsy guided by CEUS to obtain specimens, followed by histomathological and microbiological examinations. After treatment and follow-up, surgical thoracoscopy was performed on cases with undefinite diagnosis.

Result

A total of 460 patients were finally included. CEUS showed internal necrosis in 72.17% cases and obvious peripheral vessels in 55.43% cases, both of which were significantly higher than the conventional ultrasound imaged (p < 0.05). The diagnostic accuracy through CEUS guided biopsy sampling was 98.91% (455/460). The microbiological diagnostic yield achieved 71.88% (225/313) in infectious lesions. In 330 cases combined pleural effusion, CEUS guided biopsy increased the diagnostic yield from 60.30% (199 /330) to 98.36% (325 /330) in all cases (p < 0.05), from 15.56% (14/90) to 94.44% (85/90) in malignant lesions (p < 0.01) and from 77.08% (185/240) to 100% (240/240) in infectious lesions (p < 0.05). No serious adverse events occurred.

Conclusion

CEUS guided biopsy provides a minimally invasive, effective and safe diagnostic biopsy method for pleural lesions.

Clinical Trials Registration: Chinese Clinical Trial Registry ChiCTR2000029749 (ChiCTR, www.chictr.org.cn).

Does Soluble Urokinase-Type Plasminogen Activator Receptor Level Predicts the Occurrence of Inflammatory Complications in Maxillofacial Surgery?

Soluble urokinase-type plasminogen activator receptor (suPAR) is a marker of immune activation and reflects a more distinct aspect of inflammation than C-reactive protein (CRP) does. The study concerns a clinically silent state of the immune system expressed by the level of suPAR, which could affect the occurrence of complications (non-life threatening) after scheduled procedures. The purpose was the evaluation of suPAR predictive value in minor maxillofacial surgery complication incidents. Eighty patients were tested for suPAR, CRP and a series of basic laboratory serum tests on 1 day before surgery. Complications of orthognathic and minor injuries treatments were reported. The suPAR level, expressed as a measure independent of the patient’s age (Index of Body Inflammation, IBI), was analyzed. The protein level was also assessed on postoperative day 3. Basic statistical analysis did not reveal any relevant dependence between suPAR (or IBI) and occurrence of minor complications. The application of factor analysis, artificial neural network and inclusion of chlorides, glycaemia, alanine transaminase (ALT), albumin and hemoglobin levels allowed to indicate the suPAR/IBI ranges associated with an increased risk of minor postoperative complications. Concluding, it seems that, in the current state of the knowledge, the monitoring of pre-operational suPAR level solely does not include sufficient predictive information for the occurrence of minor complications after maxillofacial surgery. The suPAR/IBI level should be combined with other patient characteristics to predict healing complications.