Advances in pleural effusion diagnostics

Local Anesthetic Thoracoscopy: A Focus on Indications, Techniques and Complications

The main purpose of this narrative review is to educate general practitioners about a crucial pleural procedure, namely local anesthetic thoracoscopy (LAT), and to provide established respiratory physicians with an expert opinion-based summary of the literature. This narrative review focuses on the indications, technical aspects and complications of LAT, highlighting its safety and high degree of diagnostic sensitivity for patients who present with an unexplained pleural effusion and have a high pre-test probability of cancer.

ACR Appropriateness Criteria® Workup of Pleural Effusion or Pleural Disease

Pleural effusions are categorized as transudative or exudative, with transudative effusions usually reflecting the sequala of a systemic etiology and exudative effusions usually resulting from a process localized to the pleura. Common causes of transudative pleural effusions include congestive heart failure, cirrhosis, and renal failure, whereas exudative effusions are typically due to infection, malignancy, or autoimmune disorders. This document summarizes appropriateness guidelines for imaging in four common clinical scenarios in patients with known or suspected pleural effusion or pleural disease. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Use of tumor markers in distinguishing lung adenocarcinoma-associated malignant pleural effusion from tuberculous pleural effusion

BACKGROUND

The distinction between lung adenocarcinoma-associated malignant pleural effusion (MPE) and tuberculous pleural effusion (TPE) continues to pose a challenge. This study sought to assess the supplementary value of tumor markers in enabling a differential diagnosis.

METHODS

Data concerning tumor markers, which included carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), cancer antigen 153 (CA153), cancer antigen 724 (CA724), neuron-specific enolase (NSE), cytokeratin19 fragment (Cyfra21-1), and squamous cell carcinoma antigen (SCCA), in both serum and pleural effusion samples, were retrospectively compiled from lung adenocarcinoma-associated MPE and TPE patients. A comparative analysis of tumor marker concentrations between the two groups was performed to assess diagnostic utility, followed by a multiple logistic regression to control for confounding variables.

RESULTS

While gender, serum CA125 and SCCA, and pleural effusion SCCA manifested comparability between the groups, distinctions were noted in patient age and the concentration of other tumor markers in serum and pleural effusion, which were notably elevated in the MPE group. Multiple logistic regression demonstrated a positive association between the risk of lung adenocarcinoma-associated MPE and levels of CEA and CA153 in serum and pleural effusion, as well as Cyfra21-1 in serum (P < 0.05). The odds ratio for CEA surpassed that of CA153 and Cyfra21-1.

CONCLUSIONS

CEA and CA153 in serum and pleural effusion, and Cyfra21-1 in serum emerge as biomarkers possessing supplementary diagnostic value in distinguishing lung adenocarcinoma-associated MPE from TPE. The diagnostic efficacy of CEA is superior to CA153 and Cyfra21-1. Conversely, the utility of CA125, CA724, NSE, and SCCA appears constrained.

Sonography of the pleura

The CME review presented here is intended to explain the significance of pleural sonography to the interested reader and to provide information on its application. At the beginning of sonography in the 80 s of the 20th centuries, with the possible resolution of the devices at that time, the pleura could only be perceived as a white line. Due to the high impedance differences, the pleura can be delineated particularly well. With the increasing high-resolution devices of more than 10 MHz, even a normal pleura with a thickness of 0.2 mm can be assessed. This article explains the special features of the examination technique with knowledge of the pre-test probability and describes the indications for pleural sonography. Pleural sonography has a high value in emergency and intensive care medicine, preclinical, outpatient and inpatient, in the general practitioner as well as in the specialist practice of pneumologists. The special features in childhood (pediatrics) as well as in geriatrics are presented. The recognition of a pneumothorax even in difficult situations as well as the assessment of pleural effusion are explained. With the high-resolution technology, both the pleura itself and small subpleural consolidations can be assessed and used diagnostically. Both the direct and indirect sonographic signs and accompanying symptoms are described, and the concrete clinical significance of sonography is presented. The significance and criteria of conventional brightness-encoded B-scan, colour Doppler sonography (CDS) with or without spectral analysis of the Doppler signal (SDS) and contrast medium ultrasound (CEUS) are outlined. Elastography and ultrasound-guided interventions are also mentioned. A related further paper deals with the diseases of the lung parenchyma and another paper with the diseases of the thoracic wall, diaphragm and mediastinum.

Diagnostic and comparative performance for the prediction of tuberculous pleural effusion using machine learning algorithms

OBJECTIVE

Early diagnosis and differential diagnosis of tuberculous pleural effusion (TPE) remains challenging and is critical to the patients' prognosis. The present study aimed to develop nine machine learning (ML) algorithms for early diagnosis of TPE and compare their performance.

METHODS

A total of 1435 untreated patients with pleural effusions (PEs) were retrospectively included and divided into the training set (80%) and the test set (20%). The demographic and laboratory variables were collected, preprocessed, and analyzed to select features, which were fed into nine ML algorithms to develop an optimal diagnostic model for TPE. The proposed model was validated by an independently external data. The decision curve analysis (DCA) and the SHapley Additive exPlanations (SHAP) were also applied.

RESULTS

Support vector machine (SVM) was the best model in discriminating TPE from non-TPE, with a balanced accuracy of 87.7%, precision of 85.3%, area under the curve (AUC) of 0.914, sensitivity of 94.7%, specificity of 80.7%, and F1-score of 86.0% among the nine ML algorithms. The excellent diagnostic performance was also validated by the external data (a balanced accuracy of 87.7%, precision of 85.2%, and AUC of 0.898). Neural network (NN) and K-nearest neighbor (KNN) had better net benefits in clinical usefulness. Besides, PE adenosine deaminase (ADA), PE carcinoembryonic antigen (CEA), and serum CYFRA21-1 were identified as the top three important features for diagnosing TPE.

CONCLUSIONS

This study developed and validated a SVM model for the early diagnosis of TPE, which might help clinicians provide better diagnosis and treatment for TPE patients.

Unveiling Insights: A Comprehensive Review of the Role of Medical Thoracoscopy in Pleural Effusion Assessment

Pleural effusion, characterized by abnormal fluid accumulation in the pleural cavity, poses diagnostic and therapeutic challenges across various medical conditions. This comprehensive review explores the role of medical thoracoscopy in assessing pleural effusions, providing insights into its historical context, procedural intricacies, diagnostic performance, safety considerations, and clinical applications. Medical thoracoscopy, a minimally invasive endoscopic procedure, offers advantages such as high diagnostic yield, therapeutic interventions, real-time assessment, and a minimally invasive nature. The review critically analyzes the procedure's advantages and disadvantages, including technical expertise, risk of complications, resource intensity, and patient selection criteria. Comparative analyses with alternative diagnostic modalities highlight the unique benefits of medical thoracoscopy in specific clinical scenarios. The diagnostic yield of medical thoracoscopy is examined, considering sensitivity and specificity in various contexts. Patient selection criteria, complications, and safety measures are discussed, emphasizing the importance of careful consideration in integrating thoracoscopy into clinical practice. The review further explores its clinical applications, including differentiating exudative and transudative effusions, identifying specific etiologies, and its role in treatment planning. In conclusion, medical thoracoscopy emerges as a valuable tool in the comprehensive management of pleural effusions, offering a nuanced approach to diagnosis and treatment. The evolving landscape of diagnostic modalities underscores the continued significance of medical thoracoscopy and potential advancements in the field.

LDH/ADA ratio in pleural fluid for the diagnosis of infectious pleurisy

Pleural effusion (PE) is a common medical concern, often requiring thoracentesis for a definitive diagnosis. An elevated pleural fluid adenosine deaminase (ADA) may indicate tuberculosis, but this is not always the case. This study aimed to evaluate the accuracy of biomarkers determined in pleural fluid and propose a new diagnostic strategy for PE in patients with high levels of ADA in pleural fluid. This retrospective analysis studied patients with PE who received thoracentesis for the first time with an ADA level of > 33 U/L in the pleural fluid analysis at two tertiary hospitals from March 2019 to March 2023. Demographic and clinical data, as well as pleural fluid biomarkers and their ratios, were studied and compared between different PE groups, and a decision tree was developed. During the study period, 259 patients were enrolled, with four different types of PE: parapneumonic (PPE) 155, tuberculosis (TPE) 41, malignant (MPE) 50, and miscellaneous 13. Biomarkers and their ratios performed well in the differential diagnosis of PE, with the LDH/ADA ratio distinguishing between PPE and non-PPE with sensitivity and specificity of 98.06% and 98.08%, respectively. The combination of LDH/ADA ratio, ADA, and mononuclear cell percentage was identified as important factors for creating a decision tree with an overall accuracy of 89.96%. The pleural fluid LDH/ADA ratio was a useful diagnostic for distinguishing PPE from non-PPE, and a decision tree with an accuracy of 89.96% was created to differentiate the four forms of PE in clinical situations.

Diagnostic accuracy and microbial profiles of tuberculous pleurisy: a comparative study of metagenomic next generation sequencing and GeneXpert Mycobacterium tuberculosis

Introduction

There is a clinical challenge in diagnosing tuberculous pleurisy accurately and promptly, highlighting the urgent need for a rapid and sensitive diagnostic method. This study aimed to evaluate the diagnostic accuracy of metagenomic next-generation sequencing (mNGS) and GeneXpert Mycobacterium tuberculosis (MTB) for identifying tuberculous pleurisy and analyzing the microbial profiles of both tuberculous and non-tuberculous pleural effusions.

Methods

The study enrolled 31 patients with suspected tuberculous pleurisy, of which 15 were confirmed to have tuberculous pleurisy and subsequently allocated to the tuberculous pleurisy group (TP group), while the remaining 16 individuals were assigned to the non-tuberculous pleurisy group (NTP group). mNGS and GeneXpert MTB were performed on pleural effusion samples, and the diagnostic accuracy of both tests was compared. We employed established formulas to compute crucial indicators, including sensitivity, specificity, missed diagnosis rate, misdiagnosed rate, positive predictive value (PPV), and negative predictive value (NPV).

Results

The results showed that both tests had high specificity (100%) and positive predictive value (100%) for detecting tuberculous pleurisy, along with comparable sensitivity (46.67% for mNGS and 40.0% for GeneXpert MTB). Further analysis of the combined efficacy of mNGS and GeneXpert MTB showed that the combined test had a sensitivity of 66.67% and a specificity of 100%. mNGS analysis revealed that MTB was detected in 7 out of 15 patients with tuberculous pleural effusions, while non-tuberculous pleural effusions were associated with a diverse range of microbial genera and species. The most frequently detected genera at the microbial genus level in the NTP group were Microbacterium spp. (6/16), Prevotella spp. (5/16), and Campylobacter spp. (5/16).

Discussion

These findings suggest that mNGS and GeneXpert MTB are useful diagnostic tools for identifying patients with tuberculous pleurisy, and mNGS can provide valuable insights into the microbial profiles of both tuberculous and non-tuberculous pleural effusions.

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.

Construction of a multi-classified decision tree model for identifying malignant pleural effusion and tuberculous pleural effusion

OBJECTIVE

Pleural effusion (PE) is a common clinical complication associated with various disorders. We aimed to utilize laboratory variables and their corresponding ratios in serum and PE for the differential diagnosis of multiple types of PE based on a decision tree (DT) algorithm.

METHODS

A total of 1435 untreated patients with PE admitted to The First Affiliated Hospital of Ningbo University were enrolled. The demographic and laboratory variables were collected and compared. The receiver operating characteristic curve was used to select important variables for diagnosing malignant pleural effusion (MPE) or tuberculous pleural effusion (TPE) and included in the DT model. The data were divided into the training set and the test set at a ratio of 7:3. The training data was used to develop the DT model, and the test data was for evaluating the model. Independent data was collected as external validation.

RESULTS

Three PE indicators (carcinoembryonic antigen, adenosine deaminase [ADA], and total protein), two serum indicators (neuron-specific enolase and cytokeratin 19 fragments), and two ratios [high-sensitivity C-reactive protein (hsCRP)/ PE lymphocyte and hsCRP/PE ADA] were used to construct the DT model. The area under the curve (AUC), sensitivity, and specificity for diagnosing MPE were 0.963, 84.0%, 91.6% in the training set, 0.976, 84.1%, 88.6% in the test set, and 0.955,83.3%, 86.7% in the external validation set. The AUC, sensitivity, and specificity of diagnosing TPE were 0.898, 86.8%, 92.3% in the training set, 0.888, 88.8%, 92.7% in the test set, and 0.778, 84.8%, 94.3% in the external validation set.

CONCLUSION

The DT model showed good diagnostic efficacy and could be applied for the differential diagnosis of MPE and TPE in clinical settings.

Lymphoscintigraphy findings in patients with chylothorax: influence of biochemical parameters

BACKGROUND

Chylothorax is a condition that can be challenging to diagnose due to its nonspecific clinical presentation. Several biochemical parameters of chylous pleural effusion have been identified as important indicators for the diagnosis of chylothorax. Lymphoscintigraphy is utilized to assess chylothorax and determine the location of chyle leakage. The present study aimed to evaluate the correlation between the biochemical parameters of chylous pleural effusion and 99mTc-dextran (99mTc-DX) lymphoscintigraphy in diagnosing chylothorax.

MATERIAL AND METHODS

A total of 120 patients were enrolled in the study, 83 of the patients with unilateral chylothorax, and 37 with bilateral chylothorax. All patients underwent both 99mTc-DX lymphoscintigraphy and pleural effusion laboratory analysis. The 99mTc-DX lymphoscintigraphy images were categorized as positive or negative groups based on the presence or absence of abnormal radioactive tracer accumulation in the thorax, respectively. The biochemical parameters of the two groups were subsequently compared.

RESULTS

Among these patients, 101 (84.17%) had exudative effusions, while 19 (15.83%) had transudative effusions, as determined by the levels of pleural effusion protein, lactate dehydrogenase and cholesterol. Abnormal tracer accumulation in thorax was observed in 82 patients (68.33%). Our findings indicated that lymphoscintigraphy results were not associated with exudative and transudative chylothorax (P = 0.597). The lymphoscintigraphy positive group displayed significantly higher levels of pleural effusion triglyceride and pleural effusion triglyceride/serum triglyceride ratio in all biochemical parameters, compared to the negative group (P = 0.000 and P = 0.005). We identified cutoff values of 2.870 mmol/L for pleural effusion triglycerides and 4.625 for pleural effusion triglyceride/serum triglyceride ratio, respectively, which can facilitate differentiating the positive and negative cases on lymphoscintigraphy.

CONCLUSION

Lymphoscintigraphy technique is a dependable diagnostic tool for the qualitative assessment of chylous pleural effusion. Higher pleural effusion triglyceride level and pleural effusion triglyceride/serum triglyceride ratio indicate a positive result in patients with chylothorax on lymphoscintigraphy, with the cutoff values of 2.870 mmol/L and 4.625 aiding in the diagnosis.

Primary Effusion Lymphoma: A Rare and Challenging Diagnosis for Recurrent Pleural Effusion

Primary Effusion Lymphoma is an extremely rare and aggressive subtype of B-cell lymphoma, accounting for only <1% of all cases of this neoplasm. It has a unique clinical presentation because it has a predilection for appearing in body cavities, such as the pleural space, pericardium and peritoneum. It mainly affects immunocompromised individuals and may also affect individuals in the Mediterranean region and in areas endemic for human herpesvirus 8 (HHV-8). Herein, we report the case of an 83-year-old immunocompetent male complaining of coughing, fever and progressive dyspnea for 3 days. His past medical history revealed a recurrent pleural effusion for the last three years, as well as losing weight and malaise. A subsequent investigation revealed a PEL diagnosis of the pleura.

Value of ultrasonography in determining the nature of pleural effusion: Analysis of 582 cases

To explore the value of ultrasonography in the auxiliary diagnosis of pleural effusion, we retrospectively analyzed the ultrasonographic findings of 275 exudates and 307 transudates and summarized the ultrasonographic image features of pleural effusion according to patients' primary diseases. The findings of thoracic ultrasonography performed before the initial thoracentesis in 582 patients with subsequently confirmed exudative/transudative pleural effusion were analyzed with regard to the sonographic features of pleural effusion. In 275 cases with exudates, thoracic ultrasonography showed a complex septate appearance in 19 cases (6.9%), complex nonseptate appearance in 100 cases (36.4%), complex homogenous sign in 46 cases (16.7%), and pleural thickness > 3 mm in 105 cases. In contrast, in 307 patients with transudates, most patients (97.1%) had bilateral pleural effusion. Ultrasonographic images displayed anechoic appearance and absence of pleural thickening in a vast majority of cases (306, 99.7%; 301, 98%). These positive findings in the exudate were statistically higher than those in their counterparts (P < .05). In the empyema subgroup, the proportion of complex septate appearance, complex nonseptate appearance, complex homogenous sign, and pleural thickening was the highest, at 19/41, 12/41, 10/41, and 30/41, respectively. Ultrasonography is valuable in defining the nature of pleural effusion. Some sonographic features of pleural effusion, such as echogenicity, septation, and pleural thickening, may indicate a high risk of exudative pleural effusion.

The efficiency of a clinical pathway to guide combined applications of interventional pulmonology in undiagnosed pleural effusions

The diagnostic procedure of pleural effusion (PEs) is challenging due to low detection rates and numerous aetiologies. Hence, any attempt to enhance diagnosis is worthwhile. We present a clinical pathway to guide combined application of interventional pulmonology (IP) for tracing causes of undiagnosed PEs. Subjects with undiagnosed PEs were identified in the Hospital Information System of Dalian Municipal Central Hospital from January 1, 2012, to December 31, 2018. Eligible subjects were divided into a group of combined tests and a group of medical thoracoscopy (MT). Optimal and subsequent diagnostic tests were performed depending on the guidance of the clinical pathway by matching profitable chest lesions with the respective adaptation. As the guidance of clinical pathway, common bronchoscopy would be preferentially selected if pulmonary lesions involved or within the central bronchus, EBUS-TBNA was favoured when pulmonary lesions were adjacent to the central bronchus or with the enlarged mediastinal/hilar lymph nodes, guided bronchoscopy would be preferred if pulmonary nodules/masses were larger than 20 mm with discernible bronchus signs, CT-assisted transthoracic core biopsy was preferred if pulmonary nodules were less than 20 mm, image guided cutting needle biopsy was the recommendation if the pleural thickness was larger than 10 mm and pulmonary lesions were miliary. MT was preferred only when undiagnosed PEs was the initial symptom and pulmonary lesions were miliary or absent. A total of 83.57% cases of undiagnosed PEs were eligible for the clinical pathway, and 659 and 216 subjects were included in the combined tests and MT groups, respectively, depending on the optimal recommendation of the clinical pathway. The total diagnostic yields in the combined tests and MT groups were 95.99% and 91.20%, respectively, and the difference in total diagnostic yield was statistically significant (χ² = 7.510, p = 0.006). Overall, clinical pathway guidance of the combined application of IP is useful for tracing the causes of undiagnosed PEs. The diagnostic yield of undiagnosed PEs is significantly increased compared with that of MT alone.

Deep transfer learning to quantify pleural effusion severity in chest X-rays

Purpose

The detection of pleural effusion in chest radiography is crucial for doctors to make timely treatment decisions for patients with chronic obstructive pulmonary disease. We used the MIMIC-CXR database to develop a deep learning model to quantify pleural effusion severity in chest radiographs.

Methods

The Medical Information Mart for Intensive Care Chest X-ray (MIMIC-CXR) dataset was divided into patients ‘with’ or ‘without’ chronic obstructive pulmonary disease (COPD). The label of pleural effusion severity was obtained from the extracted COPD radiology reports and classified into four categories: no effusion, small effusion, moderate effusion, and large effusion. A total of 200 datasets were randomly sampled to manually check each item and determine whether the tags are correct. A professional doctor re-tagged these items as a verification cohort without knowing their previous tags. The learning models include eight common network structures including Resnet, DenseNet, and GoogleNET. Three data processing methods (no sampling, downsampling, and upsampling) and two loss algorithms (focal loss and cross-entropy loss) were used for unbalanced data. The Neural Network Intelligence tool was applied to train the model. Receiver operating characteristic curves, Area under the curve, and confusion matrix were employed to evaluate the model results. Grad-CAM was used for model interpretation.

Results

Among the 8533 patients, 15,620 chest X-rays with clearly marked pleural effusion severity were obtained (no effusion, 5685; small effusion, 4877; moderate effusion, 3657; and large effusion, 1401). The error rate of the manual check label was 6.5%, and the error rate of the doctor’s relabeling was 11.0%. The highest accuracy rate of the optimized model was 73.07. The micro-average AUCs of the testing and validation cohorts was 0.89 and 0.90, respectively, and their macro-average AUCs were 0.86 and 0.89, respectively. The AUC of the distinguishing results of each class and the other three classes were 0.95 and 0.94, 0.76 and 0.83, 0.85 and 0.83, and 0.87 and 0.93.

Conclusion

The deep transfer learning model can grade the severity of pleural effusion.

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.

Cellular composition research of serous pleural effusion fluids. Conceptual issues of preanalytics

The purpose of this work was to show the effectiveness of the cytological method on a small number of observations, excluding all possible errors of the preanalytical stage. The paper presents several simple and easily reproducible algorithms for the cytological study of serous pleural effusions with small cellular content. On the example of 20 observations of the study of the cellular composition of serous exudates, a direct dependence of the research results on the preanalytical stage is shown. A complete study of effusion fluids in compliance with all stages of preanalytics and the use of modern methods of cytological diagnostics makes it possible to nullify the options for false-negative.

Undiagnosed pleural effusion treated with traditional Chinese medicine: A case report

The main clinical manifestations of pleural effusion are exertional dyspnea, predominantly dry cough, and pleuritic chest pain. To treat pleural effusion appropriately, it is important to determine its etiology; which however, remains unclear in nearly 20% of cases.A 73-year-old man with a history of invasive pulmonary tuberculosis (TB), had been experiencing chest congestion and dyspnea with undiagnosed pleural effusion for six years. After a series of clinical examination and laboratory tests, there was still no clear diagnosis. Despite administering diuretics and intermittent draining, the patient's condition aggravated progressively. He sought further treatment at Dongzhimen Hospital Respiratory Outpatient Clinic. The patient was treated with Zanthoxylum and Trichosanthes Decoction (Jiao Mu Gua Lou Tang). After one and a half years, his symptoms greatly improved and ultrasound revealed that the pleural effusion had apparently absorbed.It is suggested that TCM herbal formulas can play a critical role in preventing the progression of complicated, undiagnosed pleural effusion, especially in cases of poor response to conventional therapy and thoracentesis. Additional studies on the functions and mechanisms of the medicinals are warranted.

GC-MS metabolomics identifies novel biomarkers to distinguish tuberculosis pleural effusion from malignant pleural effusion

Background

Tuberculous pleural effusions (TBPEs) and malignant pleural effusions (MPEs) are two of the most common and severe forms of exudative effusions. Clinical differentiation is challenging; however, metabolomics is a collection of powerful tools currently being used to screen for disease‐specific biomarkers.

Methods

17 TBPE and 17 MPE patients were enrolled according to the inclusion criteria. The normalization gas chromatography‐mass spectrometry (GC‐MS) data were imported into the SIMCA‐P + 14.1 software for multivariate analysis. The principal component analysis (PCA) and orthogonal partial least‐squares discriminant analysis (OPLS‐DA) were used to analyze the data, and the top 50 metabolites of variable importance projection (VIP) were obtained. Metabolites were qualitatively analyzed using the National Institute of Standards and Technology (NIST) databases. Pathway analysis was performed by MetaboAnalyst 4.0. The detection of biochemical indexes such as urea and free fatty acids in these pleural effusions was also verified, and significant differences were found between these two groups.

Results

1319 metabolites were screened by non‐targeted metabonomics of GC‐MS. 9 small molecules (urea, L‐5‐oxoproline, L‐valine, DL‐ornithine, glycine, L‐cystine, citric acid, stearic acid, and oleamide) were found to be significantly different (p < 0.05 for all). In OPLS‐DA, 9 variables were considered significant for biological interpretation (VIP≥1). However, after the ROC curve was performed, it was found that the metabolites with better diagnostic value were stearic acid, L‐cystine, citric acid, free fatty acid, and creatinine (AUC > 0.8), with good sensitivity and specificity.

Conclusion

Stearic acid, L‐cystine, and citric acid may be potential biomarkers, which can be used to distinguish between the TBPE and the MPE.

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Our study aimed to explore the applicability of deep learning and machine learning techniques to distinguish MPE from BPE. We initially used a retrospective cohort with 726 PE patients to train and test the predictive performances of the driverless artificial intelligence (AI), and then stacked with a deep learning and five machine learning models, namely gradient boosting machine (GBM), extreme gradient boosting (XGBoost), extremely randomized trees (XRT), distributed random forest (DRF), and generalized linear models (GLM). Furthermore, a prospective cohort with 172 PE patients was applied to detect the external validity of the predictive models. The area under the curve (AUC) in the training, test and validation set were deep learning (0.995, 0.848, 0.917), GBM (0.981, 0.910, 0.951), XGBoost (0.933, 0.916, 0.935), XRT (0.927, 0.909, 0.963), DRF (0.906, 0.809, 0.969), and GLM (0.898, 0.866, 0.892), respectively. Although the Deep Learning model had the highest AUC in the training set (AUC = 0.995), GBM demonstrated stable and high predictive efficiency in three data sets. The final AI model by stacked ensemble yielded optimal diagnostic performance with AUC of 0.991, 0.912 and 0.953 in the training, test and validation sets, respectively. Using the driverless AI framework based on the routinely collected clinical data could significantly improve diagnostic performance in distinguishing MPE from BPE.

Development and validation of a novel scoring system developed from a nomogram to identify malignant pleural effusion

BACKGROUND

This study aimed to establish and validate a novel scoring system based on a nomogram for the differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE).

METHODS

Patients with PE and confirmed aetiology who underwent diagnostic thoracentesis were included in this study. One retrospective set (N = 1261) was used to develop and internally validate the predictive model. The clinical, radiological and laboratory features were collected and subjected to logistic regression analyses. The primary predictive model was displayed as a nomogram and then modified into a novel scoring system, which was externally validated in an independent set (N = 172).

FINDINGS

The novel scoring system was composed of fever (3 points), erythrocyte sedimentation rate (4 points), effusion adenosine deaminase (7 points), serum carcinoembryonic antigen (CEA) (4 points), effusion CEA (10 points) and effusion/serum CEA (8 points). With a cutoff value of 15 points, the area under the curve, specificity and sensitivity for identifying MPE were 0.913, 89.10%, and 82.63%, respectively, in the training set, 0.922, 93.48%, 81.51%, respectively, in the internal validation set and 0.912, 87.61%, 81.36%, respectively, in the external validation set. Moreover, this scoring system was exclusively applied to distinguish lung cancer with PE from tuberculous pleurisy and showed a favourable diagnostic performance in the training and validation sets.

INTERPRETATION

This novel scoring system was developed from a retrospective study and externally validated in an independent set based on six easily accessible clinical variables, and it exhibited good diagnostic performance for identifying MPE.

FUNDING

NFSC grants (no. 81572942, no. 81800094).