Echogenic swirling pattern, carcinoembryonic antigen, and lactate dehydrogenase in the diagnosis of malignant pleural effusion

Chian, Chih-Feng; Wu, Fang; Tsai, Chen‐Liang; Peng, Chung-Kan; Shen, Chien‐Chang; Perng, Wann-Cherng; Hsu, Shih-Chang

doi:10.1038/s41598-022-08188-y

Cited by 4 publications

(5 citation statements)

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“…The model’s most discriminating variable was dyspnoea, which increased the likelihood of the effusion being malignant by 14-fold ( Table 3 ). MPE is linked to a median survival of 3–6 months and can significantly impair quality of life due to severe dyspnoea ( 18 ). Our model included fever and X-ray/CT findings compatible with malignancy, similar to the predictive MPE model proposed by Luis Valdes et al.…”

Section: Discussionmentioning

confidence: 99%

Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations

Ji,

Shi,

Yan

et al. 2024

Front. Oncol.

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BackgroundMalignant pleural effusion (MPE) is prevalent among cancer patients, indicating pleural metastasis and predicting poor prognosis. However, accurately identifying MPE in clinical settings is challenging. The aim of this study was to establish an innovative nomogram-derived model based on clinical indicators and serum metal ion levels to identify MPE.MethodsFrom July 2020 to May 2022, 428 patients diagnosed with pleural effusion (PE) were consecutively recruited. Comprehensive demographic details, clinical symptoms, imaging data, pathological information, and laboratory results, including serum metal ion levels, were systematically collected. The nomogram was created by incorporating the most significant predictors identified through LASSO and multivariate logistic regression analysis. The predictors were assigned weighted points based on their respective regression coefficients, allowing for the calculation of a total score that corresponds to the probability of MPE. Internal validation using bootstrapping techniques assessed the nomogram’s performance, including calibration, discrimination, and clinical applicability.ResultsSeven key variables were identified using LASSO regression and multiple regression analysis, including dyspnea, fever, X-ray/CT compatible with malignancy, pleural carcinoembryonic antigen(pCEA), serum neuron-specific enolase(sNSE), serum carcinoembryonic antigen(sCEA), and pleural lactate dehydrogenase(pLDH). Internal validation underscored the superior performance of our model (AUC=0.940). Decision curve analysis (DCA) analysis demonstrated substantial net benefit across a probability threshold range > 1%. Additionally, serum calcium and copper levels were significantly higher, while serum zinc levels were significantly lower in MPE patients compared to benign pleural effusion (BPE) patients.ConclusionThis study effectively developed a user-friendly and reliable MPE identification model incorporating seven markers, aiding in the classification of PE subtypes in clinical settings. Furthermore, our study highlights the clinical value of serum metal ions in distinguishing malignant pleural effusion from BPE. This significant advancement provides essential tools for physicians to accurately diagnose and treat patients with MPE.

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Section: Discussionmentioning

confidence: 99%

Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations

Ji,

Shi,

Yan

et al. 2024

Front. Oncol.

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“…The clinical diagnostic e ciency of sCEA, pCEA, pLDH, and sNSE in the diagnosis of MPE was reportedly satisfactory. (17,18,19,20,21) A large body of work on the function of tumor markers showed that these contributed to the identi cation of MPE. ( 22)However, sSCC levels were ltered out by LASSO regression analysis, which is considered superior to select predictors by univariate analysis.…”

Section: Discussionmentioning

confidence: 99%

Development and validation a Nomogram for identify malignant pleural effusion

Yan

Wang

et al. 2023

Preprint

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Background Malignant pleural effusion (MPE) is a common complication in cancer patients, indicating the presence of pleural metastasis. However, the ability to identify MPE clinically is still limited. The aim of this study was to develop a novel nomogram-based model for predicting MPE. Methods Between July 2020 and May 2022, a total of 428 patients with pleural effusion (PE) were consecutively enrolled. Demographic data, laboratory test results, and pathological parameters were collected. The LASSO regression method was used to select potential variables, and a multivariate logistic regression method was employed to construct a nomogram. Internal validation was performed using a bootstrapping method, and the nomogram's performance was evaluated based on calibration, discrimination, and clinical utility. Results Out of the 428 patients with PE, 217 (50.7%) were diagnosed with MPE. A diagnostic model was established using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression, which identified four variables: serum carcinoembryonic antigen (sCEA), serum neuron-specific enolase (sNSE), pleural carcinoembryonic antigen (pCEA), and pleural lactate dehydrogenase (pLDH). The internal validation of the model showed an area under the curve (AUC) of 0.894 (95% CI: 0.864–0.934). The model was well-calibrated, and decision curve analysis (DCA) indicated that using the proposed nomogram to predict MPE would obtain a net benefit if the threshold probability of MPE was between 5% and 95%. Conclusion This study aimed to construct a nomogram that includes four demographic and clinical characteristics of patients with PE. The nomogram can be highly beneficial in distinguishing between MPE and benign pleural effusion (BPE).

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“…[13] Previously, most reported studies were performed using contact B-scans. [14][15][16] Yang et al analyzed sonographic images of 320 patients with pleural effusion. Their results showed that all patients with transudates presented anechoic appearance on ultrasonography (96/96), whereas anechoic effusion could be a TPE or EPE (33.9%).…”

Section: Discussionmentioning

confidence: 99%

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

Wang¹,

Fang³

et al. 2022

Medicine

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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.

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Echogenic swirling pattern, carcinoembryonic antigen, and lactate dehydrogenase in the diagnosis of malignant pleural effusion

Cited by 4 publications

References 35 publications

Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations

Development of a diagnostic algorithm to ascertain malignant pleural effusion utilizing clinical indicators and serum metal concentrations

Development and validation a Nomogram for identify malignant pleural effusion

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

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