Evaluation of creatine kinase (CK)-MB to total CK ratio as a diagnostic biomarker for primary tumors and metastasis screening

Li, Yan; Chen, Yongxin; Shao, Beibei; Liu, Junjun; Hu, Ruiguo; Zhao, Feng; Cui, Xiaofeng; Zhao, Xia; Wang, Yujiao

doi:10.1016/j.plabm.2023.e00336

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…CK-MB was identified as one of the most important features in the LightGBM model prediction. As a creatine kinase isoenzyme, CK-MB exists mainly in the myocardium and skeletal muscles ( 22 ). Previous studies have found that the ratio of CK-MB to total CK is significantly higher in advanced malignant tumor patients compared to early-stage ones ( 23 ), suggesting an association between CK-MB and cancer progression stages.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies have found that the ratio of CK-MB to total CK is significantly higher in advanced malignant tumor patients compared to early-stage ones ( 23 ), suggesting an association between CK-MB and cancer progression stages. Moreover, serum CK-MB activity is significantly elevated in metastatic tumor patients compared to those with primary tumors ( 22 ). Regarding the source of elevated serum CK-MB in malignant tumor patients, studies have detected a higher proportion of CK-MB in tumor tissues of lung cancer patients, implying that the increased plasma CK-MB may originate from tumor tissues rather than myocardium and skeletal muscles ( 24 ).…”

Section: Discussionmentioning

confidence: 99%

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Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study

Zhang,

Tan,

Huang

et al. 2024

Front. Oncol.

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ObjectiveThis study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.MethodsData from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.ResultsThe LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.ConclusionThis study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study

Zhang,

Tan,

Huang

et al. 2024

Front. Oncol.

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show abstract

“…CK-MB was identified as one of the most important features in the LightGBM model prediction. As a creatine kinase isoenzyme, CK-MB exists mainly in the myocardium and skeletal muscles (22). Previous studies have found that the ratio of CK-MB to total CK is significantly higher in advanced malignant tumor patients compared to early-stage ones (23), suggesting an association between CK-MB and cancer progression stages.…”

Section: B Amentioning

confidence: 99%

“…Previous studies have found that the ratio of CK-MB to total CK is significantly higher in advanced malignant tumor patients compared to early-stage ones (23), suggesting an association between CK-MB and cancer progression stages. Moreover, serum CK-MB activity is significantly elevated in metastatic tumor patients compared to those with primary tumors (22). Regarding the source of elevated serum CK-MB in malignant tumor patients, studies have detected a higher proportion of CK-MB in tumor tissues of lung cancer patients, implying that the increased plasma CK-MB may originate from tumor tissues rather than myocardium and skeletal muscles (24).…”

Section: B Amentioning

confidence: 99%

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AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter

Tan,

Zhang,

Huang

et al. 2024

Sci Rep

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Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

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Evaluation of creatine kinase (CK)-MB to total CK ratio as a diagnostic biomarker for primary tumors and metastasis screening

Cited by 4 publications

References 35 publications

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study

Development and validation of AI models using LR and LightGBM for predicting distant metastasis in breast cancer: a dual-center study

Table_1.docx

AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter

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