Machine learning-assisted label-free colorectal cancer diagnosis using plasmonic needle-endoscopy system.

Autor: Jo K; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea; School of Chemical Engineering, Pusan National University, Busan, 46241, South Korea., Linh VTN; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Yang JY; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Heo B; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Kim JY; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Mun NE; Biomedical Science Graduate Program, Chonnam National University, Hwasun, 58128, South Korea; Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun, 58128, South Korea; Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School, Hwasun, 58128, South Korea., Im JH; Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun, 58128, South Korea; Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School, Hwasun, 58128, South Korea., Kim KS; School of Chemical Engineering, Pusan National University, Busan, 46241, South Korea., Park SG; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Lee MY; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea., Yoo SW; Biomedical Science Graduate Program, Chonnam National University, Hwasun, 58128, South Korea; Department of Nuclear Medicine, Chonnam National University Medical School and Hwasun Hospital, Hwasun, 58128, South Korea; Institute for Molecular Imaging and Theranostics, Chonnam National University Medical School, Hwasun, 58128, South Korea. Electronic address: yoosw.md@jnu.ac.kr., Jung HS; Advanced Bio and Healthcare Materials Research Division, Korea Institute of Materials Science (KIMS), Changwon, 51508, South Korea; Advanced Materials Engineering Division, University of Science and Technology (UST), Daejeon, 34113, South Korea; School of Convergence Science and Technology, Medical Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, South Korea. Electronic address: jhs0626@kims.re.kr.
Jazyk: angličtina
Zdroj: Biosensors & bioelectronics [Biosens Bioelectron] 2024 Nov 15; Vol. 264, pp. 116633. Date of Electronic Publication: 2024 Aug 03.
DOI: 10.1016/j.bios.2024.116633
Abstrakt: Early and accurate detection of colorectal cancer (CRC) is critical for improving patient outcomes. Existing diagnostic techniques are often invasive and carry risks of complications. Herein, we introduce a plasmonic gold nanopolyhedron (AuNH)-coated needle-based surface-enhanced Raman scattering (SERS) sensor, integrated with endoscopy, for direct mucus sampling and label-free detection of CRC. The thin and flexible stainless-steel needle is coated with polymerized dopamine, which serves as an adhesive layer and simultaneously initiates the nucleation of gold nanoparticle (AuNP) seeds on the needle surface. The AuNP seeds are further grown through a surface-directed reduction using Au ions-hydroxylamine hydrochloride solution, resulting in the formation of dense AuNHs. The formation mechanism of AuNHs and the layered structure of the plasmonic needle-based SERS (PNS) sensor are thoroughly analyzed. Furthermore, a strong field enhancement of the PNS sensor is observed, amplified around the edges of the polyhedral shapes and at nanogap sites between AuNHs. The feasibility of the PNS sensor combined with endoscopy system is further investigated using mouse models for direct colonic mucus sampling and verifying noninvasive label-free classification of CRC from normal controls. A logistic regression-based machine learning method is employed and successfully differentiates CRC and normal mice, achieving 100% sensitivity, 93.33% specificity, and 96.67% accuracy. Moreover, Raman profiling of metabolites and their correlations with Raman signals of mucus samples are analyzed using the Pearson correlation coefficient, offering insights for identifying potential cancer biomarkers. The developed PNS-assisted endoscopy technology is expected to advance the early screening and diagnosis approach of CRC in the future.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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