Diagnostic Value of Six Tumor Markers for Malignant Pleural Effusion in 1,230 Patients: A Single-Center Retrospective Study

Tumor markers determination in malignant pleural effusion: pearls and pitfalls

Serum and pleural fluid tumor markers are well-recognized auxiliary diagnostic tools for malignant pleural effusion (MPE). Here, we discuss some pearls and pitfalls regarding the role of tumor markers in MPE management. The following issues are discussed in this article: What is the appropriate clinical scenario for evaluating pleural tumor markers? Which tumor markers should be advocated for diagnosing MPE? Can extremely high levels of tumor markers be employed to establish a diagnosis of MPE? Does the serum-to-pleural fluid ratio of a tumor marker have the same diagnostic efficacy as the measurement of that marker alone in the pleural fluid? Can tumor markers be used to estimate the risk of specific cancers? What should be considered when interpreting the diagnostic accuracy of tumor markers? How should tumor marker studies be performed? We addressed these issues with published works, particularly systematic reviews and meta-analyses.

American Association of Bronchology and Interventional Pulmonology Essential Knowledge in Interventional Pulmonology Series: Selected Topics in Malignant Pleural Disease

The goal of the American Association of Bronchology and Interventional Pulmonology Essential Knowledge in Interventional Pulmonology Series is to provide clinicians with concise, up-to-date reviews of important topics in the field of interventional pulmonology. This 3-year alternating rotation of primary topics will start with a focus on selected topics in malignant pleural disease. In this article, we update the reader on malignant pleural effusion in 3 parts: part 1-diagnosis, focusing on imaging and fluid biomarkers; part 2-management, with review of multimodal approaches, cost considerations, and evolving targeted therapies; and part 3-pleural mesothelioma. These reviews complement the Essential Knowledge in Interventional Pulmonology Lecture Series presented at the 2023 AABIP Annual Conference, available for viewing on the AABIP website (https://aabip.memberclicks.net/essential-knowledge-in-interventional-pulmonology-series).

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.

The Diagnosis of Malignant Pleural Effusion Using Tumor-Marker Combinations: A Cost-Effectiveness Analysis Based on a Stacking Model

PURPOSE

By incorporating the cost of multiple tumor-marker tests, this work aims to comprehensively evaluate the financial burden of patients and the accuracy of machine learning models in diagnosing malignant pleural effusion (MPE) using tumor-marker combinations.

METHODS

Carcinoembryonic antigen (CEA), carbohydrate antigen (CA)19-9, CA125, and CA15-3 were collected from pleural effusion (PE) and peripheral blood (PB) of 319 patients with pleural effusion. A stacked ensemble (stacking) model based on five machine learning models was utilized to evaluate the diagnostic accuracy of tumor markers. We evaluated the discriminatory accuracy of various tumor-marker combinations using the area under the curve (AUC), sensitivity, and specificity. To evaluate the cost-effectiveness of different tumor-marker combinations, a comprehensive score (C-score) with a tuning parameter w was proposed.

RESULTS

In most scenarios, the stacking model outperformed the five individual machine learning models in terms of AUC. Among the eight tumor markers, the CEA in PE (PE.CEA) showed the best AUC of 0.902. Among all tumor-marker combinations, the PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA combination (C9 combination) achieved the highest AUC of 0.946. When w puts more weight on the cost, the highest C-score was achieved with the single PE.CEA marker. As w puts over 0.8 weight on AUC, the C-score favored diagnostic models with more expensive tumor-marker combinations. Specifically, when w was set to 0.99, the C9 combination achieved the best C-score.

CONCLUSION

The stacking diagnostic model using PE.CEA is a relatively accurate and affordable choice in diagnosing MPE for patients without medical insurance or in a low economic level. The stacking model using the combination PE.CA19-9 + PE.CA15-3 + PE.CEA + PB.CEA is the most accurate diagnostic model and the best choice for patients without an economic burden. From a cost-effectiveness perspective, the stacking diagnostic model with PE.CA19-9 + PE.CA15-3 + PE.CEA combination is particularly recommended, as it gains the best trade-off between the low cost and high effectiveness.

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.

The value of lymphocyte to monocyte ratio in the prognosis of head and neck squamous cell carcinoma: a meta-analysis

Objectives

Although lymphocyte-monocyte ratio (LMR) is a potential prognostic biomarker in many tumor indications, a doubt occurs around its association with head and neck squamous cell carcinoma (HNSCC). We aimed to evaluate the predictive value of LMR in patients with HNSCC.

Methods

We searched PubMed, Web of Science, EMBASE, and the Cochrane database from inception to May 8, 2023 for systematic review and meta-analysis on LMR and outcomes related to HNSCC development. STATA software was used to estimate the correlation between LMR and prognosis. The risk ratio (hazard ratio, HR) and 95% confidence interval l (CI) for overall survival (OS), disease-free survival (DFS), cancer-specific survival (CSS), and progression-free survival (PFS) were calculated, and the association between LMR and OS was further validated by subgroup analysis. The source of heterogeneity with the results of subgroup analysis was analyzed by meta-regression analysis. This meta-analysis was registered at PROSPERO (CRD42023418766).

Results

After a comprehensive exploration, the results of 16 selected articles containing 5,234 subjects were evaluated. A raised LMR was connected to improved OS (HR = 1.36% CI [1.14-1.62] P = 0.018), DFS (HR = 0.942, 95% CI [0.631-1.382], P = 0.02), and PFS (HR = 0.932, 95% CI [0.527-1.589], P < 0.022). Subgroup analysis indicated that patients with a low LMR level had a poor prognosis with a critical value of ≥4. The LMR was found to be prognostic for cases with an LMR of <4. The meta-regression analysis showed that the cut-off values and treatment methods were the primary sources of high heterogeneity in patients with HNSCC.

Conclusions

Our study suggested that an elevated LMR is a potential prognostic biomarker in patients with HNSCC and could be used to predict patient outcomes.

Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data

BACKGROUND

The differential diagnosis of malignant pleural effusion (MPE) and benign pleural effusion (BPE) presents a clinical challenge. In recent years, the use of artificial intelligence (AI) machine learning models for disease diagnosis has increased.

OBJECTIVE

This study aimed to develop and validate a diagnostic model for early differentiation between MPE and BPE based on routine laboratory data.

DESIGN

This was a retrospective observational cohort study.

METHODS

A total of 2352 newly diagnosed patients with pleural effusion (PE), between January 2008 and March 2021, were eventually enrolled. Among them, 1435, 466, and 451 participants were randomly assigned to the training, validation, and testing cohorts in a ratio of 3:1:1. Clinical parameters, including age, sex, and laboratory parameters of PE patients, were abstracted for analysis. Based on 81 candidate laboratory variables, five machine learning models, namely extreme gradient boosting (XGBoost) model, logistic regression (LR) model, random forest (RF) model, support vector machine (SVM) model, and multilayer perceptron (MLP) model were developed. Their respective diagnostic performances for MPE were evaluated by receiver operating characteristic (ROC) curves.

RESULTS

Among the five models, the XGBoost model exhibited the best diagnostic performance for MPE (area under the curve (AUC): 0.903, 0.918, and 0.886 in the training, validation, and testing cohorts, respectively). Additionally, the XGBoost model outperformed carcinoembryonic antigen (CEA) levels in pleural fluid (PF), serum, and the PF/serum ratio (AUC: 0.726, 0.699, and 0.692 in the training cohort; 0.763, 0.695, and 0.731 in the validation cohort; and 0.722, 0.729, and 0.693 in the testing cohort, respectively). Furthermore, compared with CEA, the XGBoost model demonstrated greater diagnostic power and sensitivity in diagnosing lung cancer-induced MPE.

CONCLUSION

The development of a machine learning model utilizing routine laboratory biomarkers significantly enhances the diagnostic capability for distinguishing between MPE and BPE. The XGBoost model emerges as a valuable tool for the diagnosis of MPE.

Malignant pleural effusion diagnosis and therapy

Malignant pleural effusion (MPE) is a serious complication of advanced tumor, with relatively high morbidity and mortality rates, and can severely affect the quality of life and survival of patients. The mechanisms of MPE development are not well defined, but much research has been conducted to gain a deeper understanding of this process. In recent decades, although great progress has been made in the management of MPE, the diagnosis and treatment of MPE are still major challenges for clinicians. In this article, we provide a review of the research advances in the mechanisms of MPE development, diagnosis and treatment approaches. We aim to offer clinicians an overview of the latest evidence on the management of MPE, which should be individualized to provide comprehensive interventions for patients in accordance with their wishes, health status, prognosis and other factors.

Pleural fluid biochemical analysis: the past, present and future

Identifying the cause of pleural effusion is challenging for pulmonologists. Imaging, biopsy, microbiology and biochemical analyses are routinely used for diagnosing pleural effusion. Among these diagnostic tools, biochemical analyses are promising because they have the advantages of low cost, minimal invasiveness, observer independence and short turn-around time. Here, we reviewed the past, present and future of pleural fluid biochemical analysis. We reviewed the history of Light’s criteria and its modifications and the current status of biomarkers for heart failure, malignant pleural effusion, tuberculosis pleural effusion and parapneumonic pleural effusion. In addition, we anticipate the future of pleural fluid biochemical analysis, including the utility of machine learning, molecular diagnosis and high-throughput technologies. Clinical Chemistry and Laboratory Medicine (CCLM) should address the topic of pleural fluid biochemical analysis in the future to promote specific knowledge in the laboratory professional community.