The value of macrophage inhibitory cytokine‐1 level in differentiating benign from malignant solitary pulmonary nodules

Xu, Chunhua; Xue, Jin Shan; Zhang, Xiu Wei; Liu, Yu; Lin, Yong

doi:10.1111/crj.12693

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…10 At the same time, serum CEA is widely used for progression monitoring of lung cancer. 11 , 12 , 13 Moreover, several research studies 14 , 15 have reported that serum CEA had a high specificity in detecting lung cancer. While looking for new preferable diagnostic biomarkers, traditional biomarkers in the clinic should be reasonably applied to support the diagnosis of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

CT and CEA‐based machine learning model for predicting malignant pulmonary nodules

et al. 2022

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Computed tomography (CT), an efficient radiological technology, is used to detect lung cancer in the clinic. Carcinoembryonic antigen (CEA), a common tumor biomarker, is applied in the detection of various tumors. To highlight the advantages of two‐dimensional techniques and assist clinicians in optimizing lung cancer diagnostic schemes, we established a favorable model combining CT and CEA. In the study, univariate analysis was performed to screen independent predictors in a training cohort of 271 patients with malignant pulmonary nodules (MPNs) and 92 with benign pulmonary nodules (BPNs). Six machine learning–based models involving five CT predictors (mediastinal lymph node enlargement, lobulation, vascular notch sign, spiculation, and nodule number) and lnCEA were constructed and validated in an independent cohort of 129 participants (92 MPNs and 37 BPNs) by SPSS Modeler. A nomogram and the Delong test were generated by R software. Finally, the model established by logistic regression had highest diagnostic efficiency (area under the curve [AUC] = 0.912). Moreover, the diagnostic ability of the logistic model in the validation cohort (AUC = 0.882, 80.4% sensitivity, 75.7% specificity) was higher than that of the Peking University model (AUC = 0.712, 68.5% sensitivity, 70.3% specificity) and the Mayo model (AUC = 0.745, 62.0% sensitivity, 75.7% specificity). Interestingly, for the participants with intermediate (10‐30 mm) and CEA‐negative nodule, the model reached an AUC of 0.835 (72.3% sensitivity, 83.3% specificity). The AUC for the early lung cancer was as high as 0.822 with 67.3% sensitivity and 78.9% specificity. As a conclusion, this promising model presents a new diagnostic strategy for the clinic to distinguish MPNs from BPNs.

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Section: Introductionmentioning

confidence: 99%

CT and CEA‐based machine learning model for predicting malignant pulmonary nodules

et al. 2022

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18F-PBR06 PET/CT imaging of inflammation and differentiation of lung cancer in mice

et al. 2019

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

Guo

Wan

et al. 2020

Acta Clinica Belgica

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The value of macrophage inhibitory cytokine‐1 level in differentiating benign from malignant solitary pulmonary nodules

Abstract: In conclusion, measurement of serum MIC-1 levels could be considered as a diagnostic biomarker for malignant SPN patients.

Cited by 3 publications

References 19 publications

CT and CEA‐based machine learning model for predicting malignant pulmonary nodules

CT and CEA‐based machine learning model for predicting malignant pulmonary nodules

18F-PBR06 PET/CT imaging of inflammation and differentiation of lung cancer in mice

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

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