Shedding Light on Colorectal Cancer: An In Vivo Raman Spectroscopy Approach Combined with Deep Learning Analysis

被引:2
|
作者
Kouri, Maria Anthi [1 ,2 ]
Karnachoriti, Maria [3 ]
Spyratou, Ellas [1 ]
Orfanoudakis, Spyros [3 ]
Kalatzis, Dimitris [1 ]
Kontos, Athanassios G. [3 ]
Seimenis, Ioannis [4 ]
Efstathopoulos, Efstathios P. [1 ]
Tsaroucha, Alexandra [5 ]
Lambropoulou, Maria [6 ]
机构
[1] Natl & Kapodistrian Univ Athens, Med Sch, Dept Radiol 2, Athens 11527, Greece
[2] Univ Massachusetts, Kennedy Coll Sci, Dept Phys & Appl Phys, Med Phys Program, 265 Riverside St, Lowell, MA 01854 USA
[3] Natl Tech Univ Athens, Sch Appl Math & Phys Sci, Phys Dept, Iroon Politech 9, Athens 15780, Greece
[4] Natl & Kapodistrian Univ Athens, Med Sch, 75 Mikras Assias Str, Athens 11527, Greece
[5] Democritus Univ Thrace, Sch Med, Lab Bioeth, Alexandroupolis 68100, Greece
[6] Democritus Univ Thrace, Sch Med, Lab Histol Embryol, Alexandroupolis 68100, Greece
关键词
SCID mice model; colorectal cancer; Raman spectroscopy; portable Raman probe; transfer learning analysis; tissue classification;
D O I
10.3390/ijms242316582
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Raman spectroscopy has emerged as a powerful tool in medical, biochemical, and biological research with high specificity, sensitivity, and spatial and temporal resolution. Recent advanced Raman systems, such as portable Raman systems and fiber-optic probes, provide the potential for accurate in vivo discrimination between healthy and cancerous tissues. In our study, a portable Raman probe spectrometer was tested in immunosuppressed mice for the in vivo localization of colorectal cancer malignancies from normal tissue margins. The acquired Raman spectra were preprocessed, and principal component analysis (PCA) was performed to facilitate discrimination between malignant and normal tissues and to highlight their biochemical differences using loading plots. A transfer learning model based on a one-dimensional convolutional neural network (1D-CNN) was employed for the Raman spectra data to assess the classification accuracy of Raman spectra in live animals. The 1D-CNN model yielded an 89.9% accuracy and 91.4% precision in tissue classification. Our results contribute to the field of Raman spectroscopy in cancer diagnosis, highlighting its promising role within clinical applications.
引用
收藏
页数:14
相关论文
共 50 条
  • [1] A Deep Learning Approach for Detecting Colorectal Cancer via Raman Spectra
    Cao, Zheng
    Pan, Xiang
    Yu, Hongyun
    Hua, Shiyuan
    Wang, Da
    Chen, Danny Z.
    Zhou, Min
    Wu, Jian
    BME FRONTIERS, 2022, 2022
  • [2] Raman spectroscopy and FTIR spectroscopy fusion technology combined with deep learning: A novel cancer prediction method
    Leng, Hongyong
    Chen, Cheng
    Chen, Chen
    Chen, Fangfang
    Du, Zijun
    Chen, Jiajia
    Yang, Bo
    Zuo, Enguang
    Xiao, Meng
    Lv, Xiaoyi
    Liu, Pei
    SPECTROCHIMICA ACTA PART A-MOLECULAR AND BIOMOLECULAR SPECTROSCOPY, 2023, 285
  • [3] Quantitative analysis of excipient dominated drug formulations by Raman spectroscopy combined with deep learning
    Fu, Xiang
    Zhong, Li-min
    Cao, Yong-bing
    Chen, Hui
    Lu, Feng
    ANALYTICAL METHODS, 2021, 13 (01) : 64 - 68
  • [4] Deep Learning Applied to Raman Spectroscopy for the Detection of Microsatellite Instability/MMR Deficient Colorectal Cancer
    Blake, Nathan
    Gaifulina, Riana
    Griffin, Lewis D.
    Bell, Ian M.
    Rodriguez-Justo, Manuel
    Thomas, Geraint M. H.
    CANCERS, 2023, 15 (06)
  • [5] Early screening of cervical cancer based on tissue Raman spectroscopy combined with deep learning algorithms
    Kang, Zhenping
    Liu, Jie
    Ma, Cailing
    Chen, Chen
    Lv, Xiaoyi
    Chen, Cheng
    PHOTODIAGNOSIS AND PHOTODYNAMIC THERAPY, 2023, 42
  • [6] Rapid diagnosis of lung cancer and glioma based on serum Raman spectroscopy combined with deep learning
    Chen, Chen
    Wu, Wei
    Chen, Cheng
    Chen, Fangfang
    Dong, Xiaogang
    Ma, Mingrui
    Yan, Ziwei
    Lv, Xiaoyi
    Ma, Yuhua
    Zhu, Min
    JOURNAL OF RAMAN SPECTROSCOPY, 2021, 52 (11) : 1798 - 1809
  • [7] Rapid Identification of Candida auris by Raman Spectroscopy Combined With Deep Learning
    Koya, S. Kiran
    Brusatori, Michelle A.
    Yurgelevic, Sally
    Huang, Changhe
    Demeulemeester, Jake
    Percefull, Danielle
    Salimnia, Hossein
    Auner, Gregory W.
    JOURNAL OF RAMAN SPECTROSCOPY, 2025, 56 (03) : 218 - 227
  • [8] Analysis of handmade paper by Raman spectroscopy combined with machine learning
    Yan, Chunsheng
    Cheng, Zhongyi
    Luo, Si
    Huang, Chen
    Han, Songtao
    Han, Xiuli
    Du, Yuandong
    Ying, Chaonan
    JOURNAL OF RAMAN SPECTROSCOPY, 2022, 53 (02) : 260 - 271
  • [9] Raman spectroscopy of natron:: shedding light on ancient Egyptian mummification
    Edwards, Howell G. M.
    Currie, Katherine J.
    Ali, Hassan R. H.
    Jorge Villar, Susana E.
    David, A. Rosalie
    Denton, John
    ANALYTICAL AND BIOANALYTICAL CHEMISTRY, 2007, 388 (03) : 683 - 689
  • [10] Raman spectroscopy of natron: shedding light on ancient Egyptian mummification
    Howell G. M. Edwards
    Katherine J. Currie
    Hassan R. H. Ali
    Susana E. Jorge Villar
    A. Rosalie David
    John Denton
    Analytical and Bioanalytical Chemistry, 2007, 388 : 683 - 689