Rapid, label-free detection of colorectal cancer with an aberration-free line scanning confocal Raman imager
Year: 2025
Type: article
Abstract: • Aberration-free line scanning confocal Raman imager and achromatic design achieving superior spatial and spectral resolution. • Utilizing deep learning algorithms improves detection speed and accuracy. • Achieved a classification accuracy of 92.8% in identifying 14 pairs of normal/cancer samples using deep learning algorithms. • Freezing and thawing damage cell structures, but Raman imaging classifies samples correctly based on chemical changes. A self-developed aberration-free line scanning confocal Raman imager (AFLSCRI) with a spectral resolution of 0.12 nm and a spatial resolution of 2 μm is utilized to diagnose colorectal cancer. The tissues were categorized into four subgroups (typical tissue, lipid-rich tissue, fat-rich tissue, and collagen-rich tissue) and were successfully distinguished with our Raman imaging results. Compared to traditional point-scanning Raman spectroscopy, this imager offers a much faster speed with high spectral resolution while maintaining a similar spatial resolution. The Raman spectroscopy results of the same sample of colorectal cancer remain stable and unaffected even measured after six months. The molecular composition of the tissues was analyzed, and potential biomarkers such as carotenoids and protein structures were identified for four different types of colorectal tissues. When combined with machine learning algorithms, an accuracy of 92.8% was achieved in identifying 14 pairs of normal/cancer samples. These results highlight the great potential of the AFLSCRI in label-free, rapid, and non-invasive medical diagnosis.
Authors: Changwei Jiao, Miaoliang Chen, Jiaqi Liao, Jialun Li, Ruili Zhang, Sailing He
Language: English
Subfield: Biophysics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $3,750
OpenAlex ID: W4409385376