Abstract | BACKGROUND: Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer. METHODS: RESULTS: In a training dataset, the area under the curve (AUC) values were 0.821 for HE4, 0.753 for CEA, 0.858 for RANTES, 0.867 for ApoA2, 0.830 for TTR, and 0.552 for sVCAM-1. A model using all 6 biomarkers and age yielded an AUC value of 0.986 and sensitivity of 93.2% (cutoff at specificity 94%). Applying this model to the validation dataset showed similar results. The AUC value of the model was 0.988, with sensitivity of 93.33% and specificity of 92.00% at the same cutoff point used in the validation dataset. Analyses by stages and histologic subtypes all yielded similar results. CONCLUSIONS: Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.
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Authors | Ho Il Yoon, Oh-Ran Kwon, Kyung Nam Kang, Yong Sung Shin, Ho Sang Shin, Eun Hee Yeon, Keon Young Kwon, Ilseon Hwang, Yoon Kyung Jeon, Yongdai Kim, Chul Woo Kim |
Journal | Journal of cancer prevention
(J Cancer Prev)
Vol. 21
Issue 3
Pg. 187-193
(Sep 2016)
ISSN: 2288-3649 [Print] Korea (South) |
PMID | 27722145
(Publication Type: Journal Article)
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