ArticleBMC pulmonary medicine2025
Integration of circulating biomarkers and clinical factors: construction and validation of a prediction model for lung cancer metastasis.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- [Research Progress on Molecular Mechanisms and Precision Diagnosis and Treatment of Spinal Organotropic Metastasis in Lung Adenocarcinoma].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Development and validation of a machine-learning-based triage model incorporating systemic inflammatory, nutritional, coagulation, and tumor marker parameters for identifying baseline distant metastasis in lung cancer.Frontiers in oncology · 2026Article
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4 authors.
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Abstract
objectiveTo develop and validate a prediction model for metastasis risk in lung cancer patients based on circulating biomarkers and clinical factors, thereby facilitating early risk assessment.
methodsA total of 511 lung cancer patients who received treatment in the hospital from January 2020 to December 2024 were selected. Their clinical data and laboratory test indicators were collected and divided into a training set (n = 358) and a validation set (n = 153) at a ratio of 7:3. In the training set, risk factors were screened by univariate and multivariate Logistic regression to construct a nomogram model. The receiver operating characteristic curve (ROC) and calibration curve were drawn to evaluate the model's efficacy, and the model was validated in the validation set. Decision curve analysis (DCA) was used to evaluate the clinical value.
resultsIn the training set, 143 cases (39.94%) had lung cancer metastasis, and in the validation set, 61 cases (39.87%) had lung cancer metastasis. Multivariate Logistic regression showed that lymph node status, Total Prostate-Specific Antigen (TPSA), Carcinoembryonic Antigen (CEA), tumor size, Carbohydrate Antigen 19 - 9(CA199) and Alpha-Fetoprotein were significantly associated with the risk of lung cancer metastasis (all P < 0.05). The nomogram demonstrated consistent performance across the training and validation sets, with C-indices of 0.714 and 0.710, and AUCs of 0.714 (95% CI: 0.649-0.778), with a sensitivity of 0.571 and a specificity of 0.745 and 0.710 (95% CI: 0.609-0.812), with a sensitivity of 0.622 and a specificity of 0.629, respectively. The P values of the Hosmer - Lemeshow test were 0.183 and 0.075, indicating a good model fit, respectively.
conclusionThe nomogram model constructed based on circulating biomarkers and clinical factors can effectively predict the metastasis risk of lung cancer patients and has certain clinical application value. However, multi - center and large - sample studies are still needed for further validation.
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