주미연 (서울아산병원, 아폴론)

한빛사논문

조회343

서울아산병원, 아폴론

CV File 28 kb

Biosens. Bioelectron., Available online 8 December 2023 | https://doi.org/10.1016/j.bios.2023.115915 Early-stage diagnosis of bladder cancer using surface-enhanced Raman spectroscopy combined with machine learning algorithms in a rat model

Sanghwa Lee ^a,1, Miyeon Jue ^a,b,1, Kwanhee Lee ^c, Bjorn Paulson ^a,d, Jeongmin Oh ^c, Minju Cho ^c, Jun Ki Kim^a,c

^aBiomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, 05505, Republic of Korea

^bApollon, Inc., 68 Achasan-ro, Seongdong-gu, Seoul, 05505, Republic of Korea

^cDepartment of Biomedical Engineering, College of Medicine, University of Ulsan, Seoul, 05505, Republic of Korea

^dMorgridge Institute for Research, Madison, WI, 53715, USA

¹These authors contributed equally to this work.

Corresponding author : Jun Ki Kim

Abstract

Early diagnosis and accurate assessment of tumor development facilitate early bladder cancer resection and initiation of drug therapy. This study enabled an early, accurate, label-free, noninvasive diagnosis of bladder tumors by analyzing nano-biomarkers in a single drop of urine through surface-enhanced Raman spectroscopy (SERS). In a standard N-butyl-N-4-hydroxybutyl nitrosamine–induced rat model of bladder cancer, cancer stage and polyp tumor development were monitored using a small endoscope with a diameter of 1.2 mm in a minimally invasive manner without the need to kill the rats. Samples were divided into cancer-free, early-stage, and polyp-form cancer. Training data were classified according to micro-cystoscopic 5-aminolevulinic acid fluorescence diagnosis, and specimens were postmortem verified through histopathological analysis. A drop of urine from each sample group was placed on an Au-coated zinc oxide nanoporous chip to filter nano-biomaterials and selectively enhance the Raman signals of nanoscale analytes via SERS. Principal component analysis was used to reduce the dimensionality of the collected Raman spectra, and partial least squares discriminant analysis was used to find diagnostic clusters based on the labeled samples. The combination of SERS and machine learning achieved an accuracy ≥99.6% in diagnosing both early- and polyp-stage bladder tumors. With an area under the receiver operating characteristic curve greater than 0.996, the accuracy of the diagnosis in the rat model suggests that SERS-based diagnostic methods are promising when coupled with machine learning. Low-cost, label-free, and noninvasive surface-enhanced Raman spectra are ideal for developing clinically relevant point-of-care diagnostic techniques.

논문정보

형식Research article
게재일2023년 12월 (BRIC 등록일 2023-12-11)
연구진국내(교신)+국외 연구진
분야 바이오·의료융합 > 바이오·의료융합 기타

댓글 작성 로그인

CV 열람하기

연락처 보기