The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis

Li, Lei; Guo, Cheng; Wan, Jie; Fan, Qing-Shuai; Xu, Xiaoliang; Fu, Yu‐Fei

doi:10.1080/17843286.2020.1797330

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Predictive models developed to evaluate small SPNs in prior reports determined that CEA levels were signi cantly related to the risk of malignancy [8,23]. One meta-analysis further demonstrated that CEA exhibited good diagnostic performance when used to distinguish between benign and malignant PNs [24]. In the present study, however, no relationship was detected between tumor marker levels and the malignancy status of small SPNs.…”

Section: Discussionmentioning

confidence: 43%

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Sun,

Zhou,

et al. 2024

Preprint

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Background At present, computed tomography (CT) radiomics-based models capable of evaluating small (≤ 20 mm) solid pulmonary nodules (SPNs) are lacking. Accordingly, the present study sought to develop a CT radiomics-based model capable of differentiating between benign and malignant small SPNs. Methods Between January 2019 and November 2021, this study enrolled consecutive patients presenting with small SPNs, randomly assigning these individuals to training and testing cohorts at an 8:2 ratio. CT images were processed to extract radiomics features, with a radiomics scoring model being developed based on the features selected in the training group through univariate and multivariate logistic regression analyses. The testing cohort was then used to validate the developed predictive model. Results In total, this study included 210 patients in the training (n = 168) and testing (n = 42) cohorts. Radiomics scores were ultimately calculated based on 9 selected CT radiomics features. Traditional CT and clinical risk factors associated with malignancy in SPNs included lobulation (P < 0.001), spiculation (P < 0.001), and a larger diameter (P < 0.001). The developed CT radiomics scoring model consisted of the following formula: X = -6.773 + 12.0705×radiomics score + 2.5313×lobulation + 3.1761×spiculation + 0.3253×diameter. The CT radiomics-based model, CT radiomics score, and clinicoradiological score were associated with area under the curve (AUC) values of 0.957, 0.945, and 0.853, respectively, in the training cohort, while the testing cohort exhibited corresponding AUC values of 0.943, 0.916, and 0.816. Conclusions The CT radiomics-based model designed in the present study offers valuable diagnostic accuracy when employed to distinguish between benign and malignant SPNs.

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Section: Discussionmentioning

confidence: 43%

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Sun,

Zhou,

et al. 2024

Preprint

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“…Even though serum CEA levels were linked to age and smoking [30], multivariate analysis revealed serum CEA to be a signi cant factor instead of a confounding factor. CEA was also thought to be a key factor in distinguishing between malignant and benign SPNs by Li et al [30].…”

Section: Discussionmentioning

confidence: 87%

“…Furthermore, serum tumor markers have been linked to cancer [29], and CEA has been an essential marker for various cancers [30]. Even though serum CEA levels were linked to age and smoking [30], multivariate analysis revealed serum CEA to be a signi cant factor instead of a confounding factor. CEA was also thought to be a key factor in distinguishing between malignant and benign SPNs by Li et al [30].…”

Section: Discussionmentioning

confidence: 99%

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

Zhao

Shi

et al. 2021

Preprint

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Background: There is a lack of clinical-radiological predictive models for the small (≤ 20 mm) solitary pulmonary nodules (SPNs). We aim to establish a clinical-radiological predictive model for differentiating malignant and benign small SPNs.Materials and Methods: Between January 2013 and December 2018, a retrospective cohort of 250 patients with small SPNs was used to construct the predictive model. A second retrospective cohort of 101 patients treated between January 2019 and December 2020 was used to independently test the model. The model was also compared to two other models that had previously been identified.Results: In the training group, 250 patients with small SPNs including 156 (62.4%) malignant SPNs and 94 (37.6%) benign SPNs patients were included. Multivariate logistic regression analysis indicated that older age, pleural retraction sign, CT bronchus sign, and higher CEA level were the risk factors of malignant small SPNs. The predictive model was established as: X = -10.111 + [0.129 × age (y)] + [1.214 × pleural retraction sign (present = 1; no present = 0)] + [0.985 × CT bronchus sign (present = 1; no present = 0)] + [0.21 × CEA level (ug/L)]. Our model had a significantly higher region under the receiver operating characteristic (ROC) curve (0.870; 50% CI: 0.828-0.913) than the other two models. Conclusions: We established and validated a predictive model for estimating the pre-test probability of malignant small SPNs, that can help physicians to choose and interpret the outcomes of subsequent diagnostic tests.

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“…In one prior meta-analysis, tests for carcinoembryonic antigen (CEA) alone were found to be associated with moderate diagnostic utility (AUC = 77%) when differentiating between malignant and benign SPNs [ 35 ]. However, the incorporation of other tumor marker tests can improve the overall accuracy of developed diagnostic models for SPN evaluation [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

Chen

Bai

Wen

et al. 2022

J Cardiothorac Surg

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Background To date, multiple predictive models have been developed with the goal of reliably differentiating between solitary pulmonary nodules (SPNs) that are malignant and those that are benign. The present meta-analysis was conducted to assess the diagnostic utility of these predictive models in the context of SPN differential diagnosis. Methods The PubMed, Embase, Cochrane Library, CNKI, Wanfang, and VIP databases were searched for relevant studies published through August 31, 2021. Pooled data analyses were conducted using Stata v12.0. Results In total, 20 retrospective studies that included 5171 SPNs (malignant/benign: 3662/1509) were incorporated into this meta-analysis. Respective pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic score values were 88% (95CI%: 0.84–0.91), 78% (95CI%: 0.74–0.80), 3.91 (95CI%: 3.42–4.46), 0.16 (95CI%: 0.12–0.21), and 3.21 (95CI%: 2.87–3.55), with an area under the summary receiver operating characteristic curve value of 86% (95CI%: 0.83–0.89). Significant heterogeneity among studies was detected with respect to sensitivity (I2 = 89.07%), NLR (I2 = 87.29%), and diagnostic score (I2 = 72.28%). In a meta-regression analysis, sensitivity was found to be impacted by the standard reference in a given study (surgery and biopsy vs. surgery only, P = 0.02), while specificity was impacted by whether studies were blinded (yes vs. unclear, P = 0.01). Sensitivity values were higher when surgery and biopsy samples were used as a standard reference, while unclear blinding status was associated with increased specificity. No significant evidence of publication bias was detected for the present meta-analysis (P = 0.539). Conclusions The results of this meta-analysis demonstrate that predictive models can offer significant diagnostic utility when establishing whether SPNs are malignant or benign.

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The use of carcinoembryonic antigen levels to predict lung nodule malignancy: a meta-analysis

Cited by 8 publications

References 20 publications

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

CT radiomics based model for differentiating malignant and benign small (≤20mm) solid pulmonary nodules

Clinical-radiological predictive model in differential diagnosis of small (≤ 20 mm) solitary pulmonary nodules

Predictive model for the probability of malignancy in solitary pulmonary nodules: a meta-analysis

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