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. 2025 Jan;16(1):e15476.
doi: 10.1111/1759-7714.15476. Epub 2024 Nov 18.

Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy

Xianbei Yang  1 , Anzhi Chen  2 , Kaicheng U  3 , Sophia Meixuan Zhang  4 , Peihao Wang  1 , Zheng Li  5 , Yi Luo  6 , Yong Cui  1
Affiliations

Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy

Xianbei Yang et al. Thorac Cancer. 2025 Jan.

Abstract

Background: Cancer is a severe threat to human health, and surgery is a major method of cancer treatment. This study aimed to develop an optical sensor for fast cancer tissue.

Methods: The tissue autofluorescence spectrum and diffuse reflectance spectrum were obtained by using a laboratory-developed optical sensor system. A total of 151 lung tissue samples were used in this ex vivo study.

Results: Experimental results demonstrate that tissue autofluorescence spectroscopy with a 365-nm excitation has better performance than diffuse reflectance spectroscopy, and 63 of 64 test samples (98.4% accuracy) were correctly classified with tissue autofluorescence spectroscopy and our developed data analysis method.

Conclusions: Our promising ex vivo study results show that the developed optical sensor system has great promise for future clinical translation for intraoperative lung cancer detection and other applications.

Keywords: artificial intelligence; diagnosis; lung cancer; optical sensor.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Scheme of light–tissue interaction including tissue autofluorescence, reflection, absorption, and scattering.
FIGURE 2
FIGURE 2
(a) The design scheme for the laboratory‐developed optical sensor system used in this study for tissue autofluorescence and diffuse reflectance spectrum measurement. Light sources for autofluorescence and diffuse reflectance spectroscopy are connected with an optical probe by fibers separately. The tissue signal light is collected by the optical probe and delivered to the spectrometer for measurement. The system is controlled by a computer, and the whole device is portable. (b) A photograph of the dual‐modality optical probe for tissue differentiation with both tissue autofluorescence and diffuse reflectance spectroscopy.
FIGURE 3
FIGURE 3
Tissue autofluorescence spectrum with 365 nm excitation for lung cancer (red) and normal lung tissue (black). A 365‐nm LED light source is used for fluorescence excitation. Fluorescence index is calculated by normalizing the obtained spectrum. The shaded area shows the standard deviation (SD).
FIGURE 4
FIGURE 4
Tissue diffuse reflectance spectrum for lung cancer (red) and normal lung tissue (black). Reflectance index is calculated by dividing normalized tissue diffuse reflectance spectrum by normalized white‐reference spectrum. The shaded area shows the standard deviation (SD).
FIGURE 5
FIGURE 5
A typical H&E histopathology image of normal lung tissue (a) and lung cancer tissue (b). Normal tissue and cancer tissue have different cellular morphology and number density.
FIGURE 6
FIGURE 6
The shapes of the utilized PCs for fluorescence spectra. The principal component coefficients are plotted in the figure. PCs, principal components.
FIGURE 7
FIGURE 7
The shapes of the utilized PCs for reflectance spectra. The principal component coefficients are plotted in the figure. PCs, principal components.
FIGURE 8
FIGURE 8
Tissue differentiation with three PCA factors from tissue autofluorescence spectrum with 365 nm excitation for lung cancer (red) and normal lung tissue (black). PCA, principal components analysis.
FIGURE 9
FIGURE 9
Tissue differentiation with three PCA factors from tissue reflectance spectrum for lung cancer (red) and normal lung tissue (black). PCA, principal components analysis.
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