Publication Cover
Spectroscopy Letters
An International Journal for Rapid Communication
Volume 56, 2023 - Issue 6
124
Views
1
CrossRef citations to date
0
Altmetric
Research Articles

Optimizing strategies of Raman spectra model combining pre-processing and classification for diagnosis of lung cancer

, , , &
Pages 309-322 | Received 19 Dec 2022, Accepted 17 Apr 2023, Published online: 08 May 2023

References

  • World Cancer Research Fund International. Cancer-Trends, Lung-Cancer-Statistics. https://www.wcrf.org/cancer-trends/lung-cancer-statistics/ (accessed Apr 21, 2023).
  • World Health Organization. Cancer Fact Sheets, Lung Cancer. https://www.who.int/news-room/fact-sheets/detail/cancer. (accessed April 21, 2023).
  • Xia, C.; Dong, X.; Li, H.; Cao, M.; Sun, D.; He, S.; Yang, F.; Ya, X.; Zhang, S.; Li, N.; et al. Cancer Statistics in China and United States, 2022: Profiles, Trends, and Determinants. Chin. Chinese Medical Journal 2022, 135 (5), 584–590. DOI: 10.1097/CM9.0000000000002108.
  • Siegel, R. L.; Miller, K. D.; Wagle, N. S.; Jemal, A. Cancer Statistics, 2023. Ca-Cancer. A Cancer Journal for Clinicians 2023, 73(1), 17–48. DOI: 10.3322/caac.21763.
  • Poon, C.; Haderi, A.; Roediger, A.; Yuan, M. Should We Screen for Lung Cancer? A 10-Country Analysis Identifying Key Decision-Making Factors. Health Policy 2022, 126(9), 879–888. DOI: 10.1016/j.healthpol.2022.06.003.
  • National Cancer Institute, Surveillance, Epidemiology, and End Results Program. Cancer Stat Facts: Lung and Bronchus Cancer. https://seer.cancer.gov/statfacts/html/lungb.html (accessed Apr 21, 2023).
  • O'Dowd, G.; Bell, S.; Wright, S. Wheater’s Pathology: A Text, Atlas and Review of Histopathology E-Book; Elsevier Health Sciences: Amsterdam, 2019.
  • Mazzone, P. J.; Silvestri, G. A.; Souter, L. H.; Caverly, T. J.; Kanne, J. P.; Katki, H. A.; Wiener, R. S.; Detterbeck, F. C. Screening for Lung Cancer: CHEST Guideline and Expert Panel Report. Chest 2021, 160(5), e427–e494. DOI: 10.1016/j.chest.2021.06.063.
  • McCreery, R. L. Raman Spectroscopy for Chemical Analysis; John Wiley & Sons: Chichester, 2005.
  • Campion, A.; Kambhampati, P. Surface-Enhanced Raman Scattering. Chemical Society Reviews 1998, 27 (4), 241–250. DOI: 10.1039/a827241z.
  • Kong, K.; Kendall, C.; Stone, N.; Notingher, I. Raman Spectroscopy for Medical Diagnostics—From In-Vitro Biofluid Assays to In-Vivo Cancer Detection. Advanced Drug Delivery Reviews 2015, 89, 121–134. DOI: 10.1016/j.addr.2015.03.009.
  • Engel, J.; Gerretzen, J.; Szymańska, E.; Jansen, J. J.; Downey, G.; Blanchet, L.; Buydens, L. M. Breaking with Trends in Pre-Processing? Trac Trends in Analytical Chemistry. 2013, 50, 96–106. DOI: 10.1016/j.trac.2013.04.015.
  • Liu, K.; Jin, S.; Song, Z.; Jiang, L.; Ma, L.; Zhang, Z. Label-Free Surface-Enhanced Raman Spectroscopy of Serum Based on Multivariate Statistical Analysis for the Diagnosis and Staging of Lung Adenocarcinoma. Vibrational Spectroscopy. 2019, 100, 177–184. DOI: 10.1016/j.vibspec.2018.12.007.
  • Chen, R.; Lin, J.; Feng, S.; Huang, Z.; Chen, G.; Wang, J.; Li, Y.; Zeng, H. Applications of SERS Spectroscopy for Blood Analysis. In Applications of Raman Spectroscopy to Biology; Ghomi, M., Ed.; Adv. Biomed. Spectrosc. IOS Press: Amsterdam, 2012; pp72–105
  • Feng, S.; Chen, R.; Lin, J.; Pan, J.; Chen, G.; Li, Y.; Cheng, M.; Huang, Z.; Chen, J.; Zeng, H. Nasopharyngeal Cancer Detection Based on Blood Plasma Surface-Enhanced Raman Spectroscopy and Multivariate Analysis. Biosensors & Bioelectronics 2010, 25 (11), 2414–2419. DOI: 10.1016/j.bios.2010.03.033.
  • Lasch, P. Spectral Pre-Processing for Biomedical Vibrational Spectroscopy and Microspectroscopic Imaging. Chemometrics and Intelligent Laborary Systems. 2012, 117, 100–114. DOI: 10.1016/j.chemolab.2012.03.011.
  • Gautam, R.; Vanga, S.; Ariese, F.; Umapathy, S. Review of Multidimensional Data Processing Approaches for Raman and Infrared Spectroscopy. EPJ Techniques and Instrumentation 2015, 2 (1), 1–38. DOI: 10.1140/epjti/s40485-015-0018-6.
  • Byrne, H. J.; Knief, P.; Keating, M. E.; Bonnier, F. Spectral Pre and Post Processing for Infrared and Raman Spectroscopy of Biological Tissues and Cells. Chemical Society Reviews 2016, 45 (7), 1865–1878. DOI: 10.1039/C5CS00440C.
  • Afseth, N. K.; Segtnan, V. H.; Wold, J. P. Raman Spectra of Biological Samples: A Study of Preprocessing Methods. Applied Spectroscopy 2006, 60 (12), 1358–1367. DOI: 10.1366/000370206779321454.
  • Heraud, P.; Wood, B. R.; Beardall, J.; McNaughton, D. Effects of Pre-Processing of Raman Spectra on In Vivo Classification of Nutrient Status of Microalgal Cells. Journal of Chemometrics 2006, 20 (5), 193–197. DOI: 10.1002/cem.990.
  • Butler, H. J.; Ashton, L.; Bird, B.; Cinque, G.; Curtis, K.; Dorney, J.; Esmonde-White, K.; Fullwood, N. J.; Gardner, B.; Martin-Hirsch, P. L.; et al. Using Raman Spectroscopy to Characterize Biological Materials. Nature Protocols 2016, 11 (4), 664–687. DOI: 10.1038/nprot.2016.036.
  • Wang, C.; Long, Y.; Li, W.; Dai, W.; Xie, S.; Liu, Y.; Zhang, Y.; Liu, M.; Tian, Y.; Li, Q.; Duan, Y. Exploratory Study on Classification of Lung Cancer Subtypes through A Combined K-Nearest Neighbor Classifier in Breathomics. Scientific Reports 2020, 10 (1), 5880. DOI: 10.1038/s41598-020-62803-4.
  • Widlak, P.; Pietrowska, M.; Polanska, J.; Marczyk, M.; Ros-Mazurczyk, M.; Dziadziuszko, R.; Jassem, J.; Rzyman, W. Serum Mass Profile Signature as A Biomarker of Early Lung Cancer. Lung Cancer 2016, 99, 46–52. DOI: 10.1016/j.lungcan.2016.06.011.
  • Thakur, S. K.; Singh, D. P.; Choudhary, J. Lung Cancer Identification: A Review on Detection and Classification. Cancer Metastasis Reviews 2020, 39(3), 989–998. DOI: 10.1007/s10555-020-09901-x.
  • Martyna, A.; Menżyk, A.; Damin, A.; Michalska, A.; Martra, G.; Alladio, E.; Zadora, G. Improving Discrimination of Raman Spectra by Optimising Preprocessing Strategies on the Basis of the Ability to Refine the Relationship Between Variance Components. Chemometrics and Intelligent Laborary Systems. 2020, 202, 104029. DOI: 10.1016/j.chemolab.2020.104029.
  • Bocklitz, T.; Walter, A.; Hartmann, K.; Rösch, P.; Popp, J. How to Pre-Process Raman Spectra for Reliable and Stable Models? Analytica Chimica Acta 2011, 704 (1-2), 47–56. DOI: 10.1016/j.aca.2011.06.043.
  • Ryabchykov, O.; Guo, S.; Bocklitz, T. Analyzing Raman Spectroscopic Data. Physical Sciences Reviews 2019. DOI: 10.1515/psr-2017-0043.
  • Krstajic, D.; Buturovic, L. J.; Leahy, D. E.; Thomas, S. Cross-Validation Pitfalls When Selecting and Assessing Regression and Classification Models. Journal of Cheminformatics 2014, 6 (1), 1–15. DOI: 10.1186/1758-2946-6-10.
  • Bastús, N. G.; Comenge, J.; Puntes, V. Kinetically Controlled Seeded Growth Synthesis of Citrate-Stabilized Gold Nanoparticles of Up to 200 nm: Size Focusing Versus Ostwald Ripening. Langmuir : The ACS Journal of Surfaces and Colloids 2011, 27(17), 11098–11105. DOI: 10.1021/la201938u.
  • Wei, D.; Chen, S.; Liu, Q. Review of Fuorescence Suppression Techniques in Raman Spectroscopy. Applied Spectroscopy Reviews. 2015, 50 (5), 387–406. DOI: 10.1080/05704928.2014.999936.
  • Parachalil, D. R.; McIntyre, J.; Byrne, H. Potential of Raman Spectroscopy for the Analysis of Plasma/Serum in the Liquid State: Recent Advances. Analytical and Bioanalytical Chemistry 2020, 412(9), 1993–2007. DOI: 10.1007/s00216-019-02349-1.
  • Guo, S.; Bocklitz, T.; Popp, J. Optimization of Raman-Spectrum Baseline Correction in Biological Application. The Analyst 2016, 141 (8), 2396–2404. DOI: 10.1039/C6AN00041J.
  • Lieber, C. A.; Mahadevan-Jansen, A. Automated Method for Subtraction of Fluorescence from Biological Raman Spectra. Applied Spectroscopy 2003, 57 (11), 1363–1367. DOI: 10.1366/000370203322554518.
  • Zhao, J.; Lui, H.; McLean, D. I.; Zeng, H. Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman Spectroscopy. Applied Spectroscopy 2007, 61 (11), 1225–1232. DOI: 10.1366/000370207782597003.
  • Zhang, Z.-M.; Chen, S.; Liang, Y.-Z. Baseline Correction Using Adaptive Iteratively Reweighted Penalized Least Squares. The Analyst 2010, 135 (5), 1138–1146. DOI: 10.1039/B922045C.
  • Ryan, C.; Clayton, E.; Griffin, W.; Sie, S.; Cousens, D. SNIP, A Statistics-Sensitive Background Treatment for the Quantitative Analysis of PIXE Spectra in Geoscience Applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 1988, 34 (3), 396–402. DOI: 10.1016/0168-583X(88)90063-8.
  • Leger, M. N.; Ryder, A. G. Comparison of Derivative Preprocessing and Automated Polynomial Baseline Correction Method for Classification and Quantification of Narcotics in Solid Mixtures. Applied Spectroscopy 2006, 60 (2), 182–193. DOI: 10.1366/000370206776023304.
  • Savitzky, A.; Golay, M. J. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 1964, 36 (8), 1627–1639. DOI: 10.1021/ac60214a047.
  • Ehrentreich, F.; Sümmchen, L. Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods. Analytical Chemistry 2001, 73 (17), 4364–4373. DOI: 10.1021/ac0013756.
  • Daubechies, I. Ten Lectures on Wavelets; Society for Industrial and Applied Mathematics: Philadelphia, 1993.
  • Kohler, A.; Afseth, N. K.; Martens, H. Chemometrics in Biospectroscopy. Applications of Vibrational Spectroscopy Food Science 2010, 1, 89–106. DOI: 10.1002/0470027320.s8937.
  • Kohler, A.; Kirschner, C.; Oust, A.; Martens, H. Extended Multiplicative Signal Correction as A Tool for Separation and Characterization of Physical and Chemical Information in Fourier Transform Infrared Microscopy Images of Cryo-Sections of Beef Loin. Applied Spectroscopy 2005, 59 (6), 707–716. DOI: 10.1366/0003702054280649.
  • Vapnik, V. The Nature of Statistical Learning Theory; Springer Science & Business Media: New York, 1999.
  • Mello, R. F.; Ponti, M. A. Machine Learning: A Practical Approach on the Statistical Learning Theory; Springer: Berlin, 2018.
  • Wold, S.; Sjöström, M.; Eriksson, L. PLS-Rgression: A Basic Tool of Chemometrics. Chemometrics and Intelligent Laborary Systems. 2001, 58 (2), 109–130. DOI: 10.1016/S0169-7439(01)00155-1.
  • Leardi, R. J. Genetic Algorithms in Chemistry. Journal of Chromatography. A 2007, 1158 (1-2), 226–233. DOI: 10.1016/j.chroma.2007.04.025.
  • Wehrens, R.; Buydens, L. M. Evolutionary Optimisation: A Tutorial. Trac Trends in Analytical Chemistry. 1998, 17 (4), 193–203. DOI: 10.1016/S0165-9936(98)00011-9.
  • Hibbert, D. B. J. C.; Systems, I. L. Genetic Algorithms in Chemistry. Journal of Chromatography A. 1993, 19 (3), 277–293. DOI: 10.1016/j.chroma.2007.04.025.
  • Katoch, S.; Chauhan, S. S.; Kumar, V. A Review on Genetic Algorithm: Past, Present, and Future. Multimedia Tools and Applications 2021, 80(5), 8091–8126. DOI: 10.1007/s11042-020-10139-6.
  • Jazzbin, J. Geatpy: The Genetic and Evolutionary Algorithm Toolbox with High Performance in python, version 2.7.0; Geatpy Development Team, 2020.
  • Metz, C. E. Basic Principles of ROC Analysis. Seminars in Nuclear Medicine 1978, 8(4), 283–298. DOI: 10.1016/S0001-2998(78)80014-2.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.