Abstract
In this study, we used machine learning techniques to reconstruct the wavelength dependence of the absorption coefficient of human normal and pathological colorectal mucosa tissues. Using only diffuse reflectance spectra from the ex vivo mucosa tissues as input to algorithms, several approaches were tried before obtaining good matching between the generated absorption coefficients and the ones previously calculated for the mucosa tissues from invasive experimental spectral measurements. Considering the optimized match for the results generated with the multilayer perceptron regression method, we were able to identify differentiated accumulation of lipofuscin in the absorption coefficient spectra of both mucosa tissues as we have done before with the corresponding results calculated directly from invasive measurements. Considering the random forest regressor algorithm, the estimated absorption coefficient spectra almost matched the ones previously calculated. By subtracting the absorption of lipofuscin from these spectra, we obtained similar hemoglobin ratios at 410/550 nm: 18.9-fold/9.3-fold for the healthy mucosa and 46.6-fold/24.2-fold for the pathological mucosa, while from direct calculations, those ratios were 19.7-fold/10.1-fold for the healthy mucosa and 33.1-fold/17.3-fold for the pathological mucosa. The higher values obtained in this study indicate a higher blood content in the pathological samples used to measure the diffuse reflectance spectra. In light of such accuracy and sensibility to the presence of hidden absorbers, with a different accumulation between healthy and pathological tissues, good perspectives become available to develop minimally invasive spectroscopy methods for in vivo early detection and monitoring of colorectal cancer.
Highlights
The optical properties of biological tissues condition how light beams propagate inside those tissues and interact with their biological components
We initiated this experimental study by measuring the Rd spectra from ten healthy and ten pathological mucosa samples
Using the individual Rd and μa spectra that originated from the mean results presented in Fig. 2, we started to develop the machine learning (ML) models with different approaches
Summary
The optical properties of biological tissues condition how light beams propagate inside those tissues and interact with their biological components. There are some traditional tissue windows, located at certain wavelength ranges, where current optical diagnostic and therapeutic methods work: I (625–975 nm), II (1100–1350 nm), III (1600–1870 nm), and IV (2100–2300 nm).. There are some traditional tissue windows, located at certain wavelength ranges, where current optical diagnostic and therapeutic methods work: I (625–975 nm), II (1100–1350 nm), III (1600–1870 nm), and IV (2100–2300 nm).5,6 In addition to these natural tissue windows, where local maxima for the light penetration depth are observed, other optical diagnostic and treatment windows can be induced through the application of optical clearing treatments, as recently demonstrated for the ultraviolet (UV) range with transmittance efficiency peaks at 230, 275, and 300 nm. There are some optical properties that can be estimated/calculated for a biological material, but the most commonly used are the refractive index (RI), the absorption coefficient (μa), the scattering coefficient (μs), and the anisotropy-factor (g). These fundamental properties quantify the speed of light inside the material and the number of photons that are absorbed and/or scattered per unit length inside the medium and characterize the mean directionality of such scattering. The wavelength dependence of the optical properties of a tissue provides useful information for the optimization of current optical methods in clinical practice or for the development of new methods that operate at individual wavelengths within the electromagnetic spectrum. There are some traditional tissue windows, located at certain wavelength ranges, where current optical diagnostic and therapeutic methods work: I (625–975 nm), II (1100–1350 nm), III (1600–1870 nm), and IV (2100–2300 nm). In addition to these natural tissue windows, where local maxima for the light penetration depth are observed, other optical diagnostic and treatment windows can be induced through the application of optical clearing treatments, as recently demonstrated for the ultraviolet (UV) range with transmittance efficiency peaks at 230, 275, and 300 nm.
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