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논문 기본 정보

자료유형
학술저널
저자정보
Ho Il Yoon (Seoul National University) Oh-Ran Kwon (BioInfra Inc. Seoul) Kyung Nam Kang (BioInfra Inc. Seoul) Yong Sung Shin (BioInfra Inc. Seoul) Ho Sang Shin (BioInfra Inc. Seoul) Eun Hee Yeon (BioInfra Inc. Seoul) 권건영 (계명대학교) 황일선 (계명대학교) 전윤경 (서울대학교) Yongdai Kim (Department of Statistics College of Natural Science Seoul National University) Chul Woo Kim (BioInfra Inc. Seoul Department of Pathology Seoul National University College of Medicine)
저널정보
대한암예방학회 대한암예방학회지 대한암예방학회지 제21권 제3호
발행연도
2016.1
수록면
187 - 193 (7page)

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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: We collected serum samples from 355 lung cancer patients and 590 control subjects and divided them into training and validation datasets. After measuring serum levels of 6 biomarkers (human epididymis secretory protein 4 [HE4], carcinoembryonic antigen [CEA], regulated on activation, normal T cell expressed and secreted [RANTES], apolipoprotein A2 [ApoA2], transthyretin [TTR], and secretory vascular cell adhesion molecule-1 [sVCAM-1]), we tested various sets of biomarkers for their diagnostic performance in lung cancer. 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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