Cut-off levels of CYFRA21-1 to differentiate between metastatic and non-metastatic NSCLC

Satoh, Hiroaki; Ishikawa, Hiroichi; Ohtsuka, Morio; Sekizawa, Kiyohisa

doi:10.1016/j.lungcan.2004.10.001

Cited by 3 publications

(2 citation statements)

References 5 publications

(11 reference statements)

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“…In lung cancer, elevated levels of these markers are associated with bone metastasis. For example, elevated NSE levels correlate with the number of lung cancer bone metastases ( 35 ), while high Cyfra 21-1 levels are linked to distant metastasis ( 36 ). Therefore, these findings suggest that lung cancer tumor markers are closely related to bone metastasis of lung cancer.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

He,

Liang,

et al. 2024

Front. Oncol.

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ObjectiveThis study aims to develop a predictive model for identifying lung cancer patients at elevated risk for bone metastases, utilizing the Unified Immunoinflammatory Index and various tumor markers. This model is expected to facilitate timely and effective therapeutic interventions, especially in the context of the growing significance of immunotherapy for lung cancer treatment.MethodsA retrospective analysis was conducted on 324 lung cancer patients treated between January 2019 and January 2021. After meeting the inclusion criteria, 241 patients were selected, with 56 exhibiting bone metastases. The cohort was divided into a training group (169 patients) and a validation group (72 patients) at a 7:3 ratio. Lasso regression was employed to identify critical variables, followed by logistic regression to construct a Nomogram model for predicting bone metastases. The model’s validity was ascertained through internal and external evaluations using the Concordance Index (C-index) and Receiver Operating Characteristic (ROC) curve.ResultsThe study identified several factors influencing bone metastasis in lung cancer, such as the Systemic Immune-Inflammatory Index (SII), Carcinoembryonic Antigen (CEA), Neuron Specific Enolase (NSE), Cyfra21-1, and Neutrophil-to-Lymphocyte Ratio (NLR). These factors were incorporated into the Nomogram model, demonstrating high validation accuracy with C-index scores of 0.936 for internal and 0.924 for external validation.ConclusionThe research successfully developed an intuitive and accurate Nomogram prediction model utilizing clinical indicators to predict the risk of bone metastases in lung cancer patients. This tool can be instrumental in aiding clinicians in developing personalized treatment plans, thereby optimizing patient outcomes in lung cancer care.

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Section: Discussionmentioning

confidence: 99%

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

He,

Liang,

et al. 2024

Front. Oncol.

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“…The high expression of Krt14 is a parameter of the metastatic potential of squamous cell carcinoma of the lung and sentinel lymph node metastasis of oral and oropharyngeal squamous cell carcinoma [62,63]. Krt19, especially its serum fragment CYFRA21-1, is a useful diagnostic marker for metastasis in different tumors such as NSCLC, head and neck carcinoma and hepatocellular carcinoma [64][65][66][67]. In general, the observations of keratin expression in this study are in agreement with previous reports and comprehensive cytokeratin typing in lung cancer may improve early diagnosis and be useful in anti-metastasis drug development.…”

Section: The Function Of Cytokeratins In Lung Cancer Metastasismentioning

confidence: 99%

Comparative proteome analysis of three mouse lung adenocarcinoma CMT cell lines with different metastatic potential by two‐dimensional gel electrophoresis and mass spectrometry

Zhang

Wrzesinski

Fey³

et al. 2008

Proteomics

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Metastasis is a lethal attribute of a cancer and presents a continuing therapeutic challenge. Metastasis is a highly complex process and more knowledge about the mechanisms behind metastasis is highly desirable. Isogenic CMT cell lines were selected from a spontaneous mouse lung adenocarcinoma and characterized in vivo to have different metastatic potential. In this study, the comprehensive protein expression profiles of three of these CMT cell lines at passage 5, 15 and 35 were analyzed by 2-DE separation followed by MS identification. As a result, 82 and 40 unique proteins were found to be significantly up- or down-regulated between cell lines with different metastatic potential at passages 5 and 15, respectively. These proteins were identified by MS and most of them have previously been reported to be related to cancer development and/or metastasis. Bioinformatics analysis indicated that several of the proteins were involved in proteasome, cell-cycle and cell-communication pathways. Among them, some keratins, 14-3-3 proteins and 26S proteasome proteins were identified and their aberrant expression may be directly or indirectly involved in cancer development and metastasis. In conclusion, our comprehensive 2-DE-based proteomics studies revealed some candidate proteins, protein families and signaling pathways, which might be important in cancer development and metastasis.

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Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study

Liu,

Ren,

Pei

et al. 2024

International Journal of Surgery

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Background: Clinical differentiation between pulmonary metastases and noncalcified pulmonary hamartomas (NCPH) often presents challenges, leading to potential misdiagnosis. However, the efficacy of a comprehensive model that integrates clinical features, radiomics, and deep learning (CRDL) for differential diagnosis of these two diseases remains uncertain. Objective: This study evaluated the diagnostic efficacy of a Clinical Features, Radiomics, and Deep Learning (CRDL) model in differentiating pulmonary metastases from noncalcified pulmonary hamartomas (NCPH). Methods: We retrospectively analyzed the clinical and imaging data of 256 patients from Hospital A and 85 patients from Hospital B, who were pathologically confirmed pulmonary hamartomas or pulmonary metastases after thoracic surgery. Employing Python 3.7 software suites, we extracted radiomic features and deep learning attributes from patient datasets. The cohort was divided into training set, internal validation set, and external validation set. The diagnostic performance of the constructed models was evaluated using receiver operating characteristic (ROC) curve analysis to determine their effectiveness in differentiating between pulmonary metastases and NCPH. Results: Clinical features such as white blood cell count (WBC), platelet count (PLT), history of cancer, carcinoembryonic antigen (CEA) level, tumor marker status, lesion margin characteristics (smooth or blurred) and maximum diameter were found to have diagnostic value in differentiating between the two diseases. In the domains of radiomics and deep learning. Of the 1,130 radiomics features and 512 deep learning features, 24 and 7, respectively, were selected for model development. The area under the ROC curve (AUC) values for the four groups were 0.980, 0.979, 0.999, and 0.985 in the training set, 0.947, 0.816, 0.934, and 0.952 in the internal validation set, and 0.890, 0.904, 0.923, and 0.938 in the external validation set. This demonstrated that the CRDL model showed the greatest efficacy. Conclusions: The comprehensive model incorporating clinical features, radiomics, and deep learning shows promise for aiding in the differentiation between pulmonary metastases and hamartomas.

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Cut-off levels of CYFRA21-1 to differentiate between metastatic and non-metastatic NSCLC

Cited by 3 publications

References 5 publications

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

Comparative proteome analysis of three mouse lung adenocarcinoma CMT cell lines with different metastatic potential by two‐dimensional gel electrophoresis and mass spectrometry

Development of a CT-Based comprehensive model combining clinical, radiomics with deep learning for differentiating pulmonary metastases from noncalcified pulmonary hamartomas: a retrospective cohort study

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