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
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页数:14
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