Clinical value of jointly detection serum lactate dehydrogenase/pleural fluid adenosine deaminase and pleural fluid carcinoembryonic antigen in the identification of malignant pleural effusion

Zhang, Fan; Hu, Lijuan; Wang, Junjun; Chen, Jian; Chen, Jie; Wang, Yumin

doi:10.1002/jcla.22106

Cited by 16 publications

(21 citation statements)

References 12 publications

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“…Pan et al [ 36 ] found that levels of effusion CEA were significantly higher in patients with MPE than that in BPE patients and thus effusion ECA was incorporated into the predictive model. Furthermore, a clinical study conducted by Zhang et al [ 37 ] revealed that effusion CEA could be served as a promising indicator for the discrimination of MPE and with favorable diagnostic performance as reflected by an AUC of 0.924. Our study showed that effusion CEA was the most informative parameter in the deep learning and machine learning models, indicating the significant role of effusion CEA in the differential diagnosis of MPE and BPE.…”

Section: Discussionmentioning

confidence: 99%

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Tian

Huang

et al. 2021

Translational Oncology

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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.

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Section: Discussionmentioning

confidence: 99%

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Tian

Huang

et al. 2021

Translational Oncology

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“…We performed a meta-analysis of 7 studies (including the present one) involving 860 patients with MPE and 1,218 patients with BPE [18,[22][23][24][25][26]. These studies were published from 2016 to present, across five countries, three in China, two in Singapore, one in Egypt, and one in Poland.…”

Section: Meta-analysismentioning

confidence: 99%

Diagnostic accuracy of the cancer ratio for the prediction of malignant pleural effusion: evidence from a validation study and meta-analysis

Zhang

Liu

et al. 2021

Annals of Medicine

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Objective This study aimed to assess the diagnostic accuracy of serum LDH to pleural ADA ratio (cancer ratio, CR)for malignant pleural effusion (MPE) through an original study and meta-analysis. Methods We retrospectively collected data from 145 patients with MPE and 117 cases of benign pleural effusions (BPE). The diagnostic performance of CR and a typical biomarker of MPE, carcinoembryonic antigen (CEA), were analysed using the receiver operating characteristic (ROC) curves and the area under the curve (AUC) as a measure of accuracy. The overall diagnostic accuracy of CR was summarised by a standard diagnostic meta-analysis. Results Significantly higher CR and pleural CEA values were observed in the MPE patients than in the BPE patients. At a cut-off value of 14.97, CR showed high sensitivity (0.91), low specificity (0.67), and high AUC (0.85). The combination of CEA and CR increased the AUC to 0.98. The meta-analysis included seven studies involving 2,078 patients. The pooled values for sensitivity, specificity, positive/negative likelihood ratio, and diagnostic odds ratio of CR were 0.96, 0.88, 7.70, 0.05, and 169, respectively. The AUC of the summary ROC of CR was 0.98. Conclusion CR has a high diagnostic accuracy for predicting MPE, especially when used in combination with pleural CEA.

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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%

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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Clinical value of jointly detection serum lactate dehydrogenase/pleural fluid adenosine deaminase and pleural fluid carcinoembryonic antigen in the identification of malignant pleural effusion

Abstract: Joint detection of sLDH/pADA and pCEA can be used as a good indicator for the identification of benign and MPE with higher sensitivity and specificity than pCEA or sLDH/pADA.

Cited by 16 publications

References 12 publications

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Driverless artificial intelligence framework for the identification of malignant pleural effusion

Diagnostic accuracy of the cancer ratio for the prediction of malignant pleural effusion: evidence from a validation study and meta-analysis

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

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