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.
REFERENCES
1.
S. L.
Jacques
, “Optical properties of biological tissues: A review
,” Phys. Med. Biol.
58
, R37
–R61
(2013
). 2.
V. V.
Tuchin
, Tissue Optics Light Scattering Methods and Instruments for Medical Diagnosis
, 3rd ed. (SPIE Press
, Bellingham
, WA
, 2015
), pp. 245
–358
.3.
L. M.
Oliveira
and V. V.
Tuchin
, The Optical Clearing Method—A New Tool for Clinical Practice and Biomedical Engineering
(Springer
, Cham
, 2019
), pp. 1
–106
.4.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Measurement of optical properties of normal and pathological human liver tissue from deep-UV to NIR
,” Tissue Opt. Photonics
11363
, 113630G
(2020
). 5.
D. C.
Sordillo
, L. A.
Sordillo
, P. P.
Sordillo
, L.
Shi
, and R. R.
Alfano
, “Short wavelength infrared optical windows for evaluation of benign and malignant tissues
,” J. Biomed. Opt.
22
(4
), 45002
(2017
). 6.
L.
Shi
, L. A.
Sordillo
, A.
Rodríguez-Contreras
, and R.
Alfano
, “Transmission in near-infrared optical windows for deep brain imaging
,” J. Biophot.
9
, 38
–43
(2016
). 7.
A. N.
Bashkatov
, E. A.
Genina
, M. D.
Kozintseva
, V. I.
Kochubei
, S. Y.
Gorodkov
, and V. V.
Tuchin
, “Optical properties of peritoneal biological tissues in the spectral range of 350-2500
,” Opt. Spectrosc.
120
, 6
–14
(2016
). 8.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Moving tissue spectral window to the deep-ultraviolet via optical clearing
,” J. Biophot.
12
(12
), e201900181
(2019
). 9.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, A.
Selifonov
, L.
Oliveira
, and V. V.
Tuchin
, “Enhanced ultraviolet spectroscopy by optical clearing for biomedical applications
,” IEEE J. Sel. Top. Quant. Elect.
27
(4
), 7200108
(2021
). 10.
L. M.
Oliveira
, K. I.
Zaytsev
, and V. V.
Tuchin
, “Improved biomedical imaging over a wide spectral range from UV to THz towards multimodality
,” In SPIE PROC of the Third International Conference of Biophotonics Riga 2020
, edited by J.
Spigulis
(SPIE
, Bellingham
, WA
, 2020
), Vol. 11585, p. 11585
.11.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, V. S.
Rubtsov
, E. A.
Kolesnikova
, and V. V.
Tuchin
, “Optical properties of human colon tissues in the 350–2500 spectral range
,” Quant. Elect.
44
, 779
–784
(2014
). 12.
T.
Vo-Dinh
, Biomedical Photonics Handbook
, 2nd ed. (CRC Press
, Boca Raton
, FL
, 2014
), Vol. 1, pp. 23
–168
.13.
L. H.
Wang
, S. L.
Jacques
, and L. Q.
Zheng
, “MCML-Monte Carlo modeling of photon transport in multi-layered tissues
,” Comp. Methods Progr. Biomed.
47
, 131
–146
(1995
). 14.
S. A.
Prahl
, M. J. C.
van Gemert
, and A. J.
Welch
, “Determining the optical properties of turbid media by using the adding-doubling method
,” Appl. Opt.
32
, 559
–568
(1993
). 15.
S. A.
Prahl
, see https://omlc.org/software/index.html for “Optics Software;” accessed 27 January 2021.16.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, and V. V.
Tuchin
, “Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000 nm
,” J. Phys. D: Appl. Phys.
38
, 2543
(2005
). 17.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, and V. V.
Tuchin
, “Optical properties of the subcutaneous adipose tissue in the spectral range 400-2500 nm
,” Opt. Spectrosc.
99
, 836
–842
(2005
). 18.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, and V. V.
Tuchin
, “Optical properties of human cranial bone in the spectral range from 800 to 2000 nm
,” in SPIE Proceedings of the Saratov Fall Meeting 2005: Optical Technologies in Biophysics and Medicine VII, Saratov, Russia, 27–30 September 2005, edited by V. V.
Tuchin
(SPIE
, Bellingham
, WA
, 2006
), Vol. 6163, p. 6163
.19.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, A. A.
Gavrilova
, S. V.
Kapralov
, V. A.
Grishaev
, and V. V.
Tuchin
, “Optical properties of human stomach mucosa in the spectral range from 400 to 2000nm: Prognosis for gastroenterology
,” Med. Laser Appl.
22
, 95
–104
(2007
). 20.
A. N.
Bashkatov
, E. A.
Genina
, V. I.
Kochubey
, and V. V.
Tuchin
, “Optical properties of human sclera in spectral range 370–2500 nm
,” Opt. Spectrosc.
109
, 197
–204
(2010
). 21.
S.
Carvalho
, N.
Gueiral
, E.
Nogueira
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Comparative optical properties of colon mucosa and colon precancerous polyps between 400 and 1000 nm
,” in SPIE Proceedings of BIOS-Photonics West 2017: Dynamics and Fluctuations in Biomedical Photonics, San Francisco, CA, USA, 28 January–2 February 2017, edited by V. V.
Tuchin
, K. V.
Larin
, M. J.
Leahy
, and R. K.
Wang
(SPIE
, Bellingham
, WA
, 2017
), Vol. 10063, p. 10063
.22.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Optical properties of colorectal muscle in visible/NIR range
,” in SPIE Proceedings of Photonics Europe Biophotonics—Photonic Solutions for Better Health Care VI, Strasbourg, France, 22–26 April 2018, edited by J.
Popp
, V. V.
Tuchin
, and F. S.
Pavone
(SPIE
, Belingham
, WA
, 2018
), Vol. 10685, p. 10685
.23.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Measuring optical properties of human liver between 400 and 1000 nm
,” Quant. Electr.
49
, 13
–19
(2019
). 24.
S.
Carvalho
, I.
Carneiro
, R.
Henrique
, V. V.
Tuchin
, and L.
Oliveira
, “Lipofuscin-type pigment as a marker of colorectal cancer
,” Electronics
9
, 1805
(2020
). 25.
E. A.
Genina
, A. N.
Bashkatov
, and V. V.
Tuchin
, “Optical clearing of human dura mater by glucose solutions
,” J. Biomed. Photonics Eng.
3
(1
), 010309
(2017
). 26.
J. W.
Pixkering
, C. J. M.
Moes
, H. J. C. M.
Sterenborg
, S. A.
Prahl
, and M. J. C.
van Gemert
, “Two integrating spheres with an intervening scattering sample
,” J. Opt. Soc. Am. A
9
, 621
–631
(1992
). 27.
I.
Carneiro
, S.
Carvalho
, V.
Silva
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Kinetics of optical properties of human colorectal tissues during optical clearing: A comparative study between normal and pathological tissues
,” J. Biomed. Opt.
23
, 121620
(2018
). 28.
I.
Carneiro
, S.
Carvalho
, R.
Henrique
, L.
Oliveira
, and V. V.
Tuchin
, “Kinetics of optical properties of colorectal muscle during optical clearing
,” IEEE J. Sel. Top. Quant. Elect.
25
(1
), 7200608
(2019
). 29.
M. I.
Jordan
and T. M.
Mitchell
, “Machine learning: Trends, perspectives, and prospects
,” Science
349
(6245
), 255
–260
(2015
). 30.
P.
Pradhan
, S.
Guo
, O.
Ryabchykov
, J.
Popp
, and T. W.
Bocklitz
, “Deep learning a boom for biophotonics?
,” J. Biophot.
13
(6
), e201960186
(2020
). 31.
W. S.
McCullock
and W.
Pitts
, “A logical calculus of the ideas immanent in nervous activity
,” Bull. Mathmat. Biophys.
5
, 115
–133
(1943
). 32.
A. L.
Samuel
, “Some studies in machine learning using the game of checkers
,” IBM J. Res. Develop.
3
(3
), 210
–229
(1959
). 33.
L.
Guyon
, A.
da Silva
, A.
Planat-Chrétien
, P.
Riso
, and J.-M.
Dinten
, “X2 analysis for estimating the accuracy of optical properties derived from time resolved diffuse-reflectance
,” Opt. Express
17
(22
), 20521
(2009
). 34.
H.-P.
Hsieh
, F.-H.
Ko
, and K.-B.
Sung
, “Hybrid method to estimate two-layered superficial tissue optical properties from simulated data of diffuse reflectance spectroscopy
,” Appl. Opt.
57
(12
), 3038
(2018
). 35.
T. J.
Farrel
, B. C.
Wilson
, and M. S.
Patterson
, “The use of a neural network to determine tissue optical properties from spatially resolved diffuse reflectance measurements
,” Phys. Med. Biol.
37
(12
), 2281
–2286
(1992
). 36.
S.
Panigrahi
and S.
Gioux
, “Machine learning approach for rapid and accurate estimation of optical properties using spatial frequency domain imaging
,” J. Biomed. Opt.
24
(7
), 071606
(2018
). 37.
F.
Pedregosa
, G.
Varoquaux
, A.
Gramfort
, V.
Michel
, B.
Thirion
, O.
Grisel
, M.
Blondel
, A.
Müller
, J.
Nothman
, G.
Louppe
, P.
Prettenhofer
, R.
Weiss
, V.
Dubourg
, J.
Vanderplas
, A.
Passos
, D.
Cournapeau
, M.
Brucher
, M.
Perrot
, and É
Duchesnay
, “Scikit-learn: Machine learning in Python
,” J. Mach. Learn. Res.
12
, 2825
–2830
(2011
).38.
I. D.
Nagtegaal
, M. J.
Arends
, and M.
Salto-Tellez
, “Colorectal adenocarcinoma
,” in WHO Classification of Tumours—Digestive System Tumours
, 5th ed. (The WHO Classification of Tumours Editorial Board
, 2019
), pp. 177
–187
.39.
T.
Zhang
, “Leave-one-out bounds for kernel methods
,” Neural Comput.
15
(6
), 1397
–1437
(2003
). 40.
J. D.
Johansson
and K.
Wårdell
, “Intracerebral quantitative chromophore estimation from reflectance spectra captured during deep brain stimulation implantation
,” J. Biophot.
6
(5
), 435
–445
(2013
). 41.
X.
Ying
, “An overview of overfitting and its solutions
,” J. Phys.
1168
(2
), 022022
(2019
). © 2021 Author(s). Published under an exclusive license by AIP Publishing.
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