Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers

Hyunku Shin, Byeong Hyeon Choi, On Shim, Jihee Kim, Yong Park, Suk Ki Cho, Hyun Koo Kim () and Yeonho Choi ()
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Hyunku Shin: EXoPERT Corporation
Byeong Hyeon Choi: Korea University Guro Hospital
On Shim: EXoPERT Corporation
Jihee Kim: EXoPERT Corporation
Yong Park: Korea University College of Medicine
Suk Ki Cho: Seoul National University Bundang Hospital
Hyun Koo Kim: Korea University Guro Hospital
Yeonho Choi: EXoPERT Corporation

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-37403-1

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