Current best practice in the evaluation and management of malignant pleural effusions

Walker, Steven; Bibby, Anna; Maskell, Nick A

doi:10.1177/1753465816671697

Cited by 10 publications

(9 citation statements)

References 71 publications

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“…Although the gold standard of MPE diagnosis is cytopathology, occasionally, several sampling times were required for identification of malignant cancer cells with microscopy. Various markers have been suggested as noninvasive tests to help discriminate between BPE and MPE and to provide prognostic information . The most heavily studied diagnostic biomarker in pleural effusion is carcinoembryonic antigen (CEA).…”

Section: Discussionmentioning

confidence: 99%

“…Various markers have been suggested as noninvasive tests to help discriminate between BPE and MPE and to provide prognostic information. 16,17 The most heavily studied diagnostic biomarker in pleural effusion is carcinoembryonic antigen (CEA). A meta-analysis including 49 studies suggested that the pooled sensitivity and specificity of pleural effusion CEA for diagnosing MPE were 0.549 and 0.962.…”

Section: Discussionmentioning

confidence: 99%

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Thymidine kinase 1 concentration in pleural effusion is a diagnostic marker and survival predictor for malignant pleural effusion

Tian

et al. 2019

Clinical Laboratory Analysis

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Objective Thymidine kinase 1 (TK1) is a key enzyme in the pyrimidine salvage pathway. Increased TK1 concentration correlates with cell division. TK1 is an emerging biomarker in cancer diagnosis; however, its effectiveness in diagnosis and management for malignant pleural effusion (MPE) is unclear. We evaluated the diagnostic efficiency and prognostic value of pleural effusion TK1 (pTK1) concentration for MPE. Methods From 2013 to 2017, 210 pleural effusion samples were collected from 160 patients diagnosed with MPE and 50 patients diagnosed with benign pleural effusion (BPE). TK1 concentrations in pleural effusion were measured by chemiluminescence dot blot assays. The median follow‐up was 12 months. We constructed a receiver‐operating characteristic (ROC) curve to find the optimal cutoff value for MPE diagnosis. The hazard ratios were estimated using a multivariable Cox proportional hazard model. A nomogram was drawn to illustrate the prognostic characteristics of MPE. Results The TK1 concentration in pleural effusion was significantly higher in MPE than BPE (P < 0.001), and patients with MPE could be distinguished by an optimal cutoff value of 3.10 pmol/L with a sensitivity of 0.894 and a specificity of 0.800. The multivariate analysis suggested that pTK1 concentration was an independent predictor of survival in patients with MPE. Conclusions The diagnostic and prognostic prediction of MPE may be improved by measuring pTK1 concentration and utilizing a multivariate nomogram.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Thymidine kinase 1 concentration in pleural effusion is a diagnostic marker and survival predictor for malignant pleural effusion

Tian

et al. 2019

Clinical Laboratory Analysis

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“…Magnetic resonance (MR) offers better-quality images of soft tissue than CT scans. Therefore, MR has higher sensitivity to detect chest-wall and diaphragmatic involvement, although the lung images have a lower quality [ 23 ]. Although there are reports of sensitivity and specificity rates of 90% to differentiate malignant from benign PE, the optimal protocol has not yet been established.…”

Section: Diagnostic Testsmentioning

confidence: 99%

Malignant Pleural Effusion: Diagnosis and Management

Ferreiro

Suárez-Antelo

Álvarez-Dobaño

et al. 2020

Canadian Respiratory Journal

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Symptomatic malignant pleural effusion is a common clinical problem. This condition is associated with very high mortality, with life expectancy ranging from 3 to 12 months. Studies are contributing evidence on an increasing number of therapeutic options (therapeutic thoracentesis, thoracoscopic pleurodesis or thoracic drainage, indwelling pleural catheter, surgery, or a combination of these therapies). Despite the availability of therapies, the management of malignant pleural effusion is challenging and is mainly focused on the relief of symptoms. The therapy to be administered needs to be designed on a case-by-case basis considering patient’s preferences, life expectancy, tumour type, presence of a trapped lung, resources available, and experience of the treating team. At present, the management of malignant pleural effusion has evolved towards less invasive approaches based on ambulatory care. This approach spares the patient the discomfort caused by more invasive interventions and reduces the economic burden of the disease. A review was performed of the diagnosis and the different approaches to the management of malignant pleural effusion, with special emphasis on their indications, usefulness, cost-effectiveness, and complications. Further research is needed to shed light on the current matters of controversy and help establish a standardized, more effective management of this clinical problem.

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“…Exudative pleural effusion can be further classified as malignant or benign based on the detection of malignant cells in the pleural fluid. Pleural fluid of malignant pleural effusions (MPE) contains cancer cells, while benign pleural effusions (BPE) does not 6 . An accurate diagnosis of MPE is crucial, since MPE can be an indication of pleural metastases caused by lung cancer, breast cancer, ovarian cancer and lymphomas, with lung cancer being the leading cause 7 – 9 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to near-infrared spectra for clinical pleural effusion classification

Chen

Lou

et al. 2021

Sci Rep

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Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method based on FTIR near-infrared spectroscopy (NIRS) combined with machine learning strategy for clinical pleural effusion classification. NIRS spectra were recorded for 47 MPE samples and 35 BPE samples. The sample data were randomly divided into train set (n = 62) and test set (n = 20). Partial least squares, random forest, support vector machine (SVM), and gradient boosting machine models were trained, and subsequent predictive performance were predicted on the test set. Besides the whole spectra used in modeling, selected features using SVM recursive feature elimination algorithm were also investigated in modeling. Among those models, NIRS combined with SVM showed the best predictive performance (accuracy: 1.0, kappa: 1.0, and AUCROC: 1.0). SVM with the top 50 feature wavenumbers also displayed a high predictive performance (accuracy: 0.95, kappa: 0.89, AUCROC: 0.99). Our study revealed that the combination of NIRS and machine learning is an innovative, rapid, and convenient method for clinical pleural effusion classification, and worth further evaluation.

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Current best practice in the evaluation and management of malignant pleural effusions

Cited by 10 publications

References 71 publications

Thymidine kinase 1 concentration in pleural effusion is a diagnostic marker and survival predictor for malignant pleural effusion

Thymidine kinase 1 concentration in pleural effusion is a diagnostic marker and survival predictor for malignant pleural effusion

Malignant Pleural Effusion: Diagnosis and Management

Machine learning applied to near-infrared spectra for clinical pleural effusion classification

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