한빛사논문
- 조회446
Hyunku Shin1,10, Byeong Hyeon Choi2,3,10, On Shim1, Jihee Kim1, Yong Park4, Suk Ki Cho5, Hyun Koo Kim2,6 & Yeonho Choi1,7,8,9
1EXoPERT Corporation, Seoul 02580, Republic of Korea.
2Department of Thoracic and Cardiovascular Surgery, College of Medicine, Korea University Guro Hospital, Seoul 08308, Republic of Korea.
3Korea Artificial Organ Center, Korea University, Seoul 02841, Republic of Korea.
4Division of HematologyOncology, Department of Internal Medicine, Korea University College of Medicine, Seoul 02841, Republic of Korea.
5Division of Thoracic Surgery, Department of Thoracic and Cardiovascular Surgery, Seoul National University Bundang Hospital, Seongnam 13620, Republic of Korea.
6Department of Biomedical Sciences, College of Medicine, Korea University, 02841 Seoul, Republic of Korea.
7School of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea.
8Department of Biomedical Engineering, Korea University, Seoul 02841, Republic of Korea.
9Interdisciplinary Program in Precision Public Health, Korea University, 02841 Seoul, Republic of Korea.
10These authors contributed equally: Hyunku Shin, Byeong Hyeon Choi.
Corresponding authors : Correspondence to Hyun Koo Kim or Yeonho Choi.
Abstract
Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.
논문정보
- Research article
- 2023년 03월 (BRIC 등록일 2023-03-27)
- 국내 연구진
- 바이오·의료융합 > 바이오센싱 및 나노바이오물질
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