OCANSpectra: an oral cancer detection system from salivary ATR-FTIR spectroscopy
Resumo
Detectar câncer oral com análise imuno-histoquímica é invasivo, caro e só funciona em estágios avançados. Por isso, a comunidade médica busca um método diagnóstico não invasivo, preciso, sustentável e de baixo custo. A espectroscopia infravermelha de reflexão total atenuada (ATR-FTIR) pode ajudar a detectar várias doenças, incluindo câncer oral, usando amostras salivares. Nós testamos cinco métodos de correção de linha de base e cinco técnicas de classificação para melhorar a qualidade do espectro e o modelo preditivo. A combinação do método de mínimos quadrados assimétricos com máquina de vetores de suporte (kernel gaussiano) obteve os melhores resultados com os dados reais.
Palavras-chave:
ATR-FTIR, FTIR, câncer oral, detecção, classificação, SVM, LDA, correção de baselines
Referências
Baek, S.-J., Park, A., Ahn, Y.-J., and Choo, J. (2015). Baseline correction using asymmetrically reweighted penalized least squares smoothing. Analyst, 140(1):250–257.
Baker, M. J., Trevisan, J., Bassan, P., Bhargava, R., Butler, H. J., Dorling, K. M., Fielden, P. R., Fogarty, S. W., Fullwood, N. J., Heys, K. A., et al. (2014). Using fourier transform ir spectroscopy to analyze biological materials. Nature protocols, 9(8):1771–1791.
Balakrishnama, S. and Ganapathiraju, A. (1998). Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 18(1998):1–8.
Berrar, D. (2019). Cross-validation.
Boelens, H. F., Eilers, P. H., and Hankemeier, T. (2005). Sign constraints improve the detection of differences between complex spectral data sets: Lcir as an example. Analytical chemistry, 77(24):7998–8007.
Bozkurt, O., Severcan, M., and Severcan, F. (2010). Diabetes induces compositional, structural and functional alterations on rat skeletal soleus muscle revealed by ftir spectroscopy: a comparative study with edl muscle. Analyst, 135(12):3110–3119.
Bunaciu, A. A., Hoang, V. D., and Aboul-Enein, H. Y. (2015). Applications of ft-ir spectrophotometry in cancer diagnostics. Critical reviews in analytical chemistry, 45(2):156–165.
Butler, H. J., Brennan, P. M., Cameron, J. M., Finlayson, D., Hegarty, M. G., Jenkinson, M. D., Palmer, D. S., Smith, B. R., and Baker, M. J. (2019). Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer. Nature communications, 10(1):1–9.
Caixeta, D. C., Carneiro, M. G., Rodrigues, R., Alves, D. C. T., Goulart, L. R., Cunha, T. M., Espindola, F. S., Vitorino, R., and Sabino-Silva, R. (2023). Salivary ATR-FTIR spectroscopy coupled with support vector machine classification for screening of type 2 diabetes mellitus. Diagnostics, 13(8):1396.
Carneiro, M. G. (2016). Redes complexas para classificação de dados via conformidade de padrão, caracterização de importância e otimização estrutural. PhD thesis, Universidade de São Paulo.
Falamas, A., Faur, C., Ciupe, S., Chirila, M., Rotaru, H., Hedesiu, M., and Pinzaru, S. C. (2021). Rapid and noninvasive diagnosis of oral and oropharyngeal cancer based on micro-raman and ft-ir spectra of saliva. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 252:119477.
Giamougiannis, P., Morais, C. L., Rodriguez, B., Wood, N. J., Martin-Hirsch, P. L., and Martin, F. L. (2021). Detection of ovarian cancer (±neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort. Analytical and bioanalytical chemistry, 413(20):5095–5107.
Janik, P. and Lobos, T. (2006). Automated classification of power-quality disturbances using svm and rbf networks. IEEE Transactions on Power Delivery, 21(3):1663–1669.
Jubair, F., Al-karadsheh, O., Malamos, D., Al Mahdi, S., Saad, Y., and Hassona, Y. (2022). A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Diseases, 28(4):1123–1130.
Kavarthapu, A. and Gurumoorthy, K. (2021). Linking chronic periodontitis and oral cancer: A review. Oral Oncology, 121:105375.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Liaw, A., Wiener, M., et al. (2002). Classification and regression by randomforest. R news, 2(3):18–22.
Lobbezoo, F., Aarab, G., Verhoeff, M. C., and Volgenant, C. M. (2022). The value of oral care in dying and death. The Lancet, 399(10342):2187–2188.
Losq, C. L. (2018). Rampy: a Python library for processing spectroscopic (IR, Raman, XAS...) data.
Malamud, D. (2011). Saliva as a diagnostic fluid. Dental Clinics, 55(1):159–178.
Martinez-Cuazitl, A., Vazquez-Zapien, G. J., Sanchez-Brito, M., Limon-Pacheco, J. H., Guerrero-Ruiz, M., Garibay-Gonzalez, F., Delgado-Macuil, R. J., de Jesus, M. G. G., Corona-Perezgrovas, M. A., Pereyra-Talamantes, A., et al. (2021). ATR-FTIR spectrum analysis of saliva samples from covid-19 positive patients. Scientific Reports, 11(1):1–14.
Mikkonen, J. J., Raittila, J., Rieppo, L., Lappalainen, R., Kullaa, A. M., and Myllymaa, S. (2016). Fourier transform infrared spectroscopy and photoacoustic spectroscopy for saliva analysis. Applied Spectroscopy, 70(9):1502–1510.
Morais, C. L., Paraskevaidi, M., Cui, L., Fullwood, N. J., Isabelle, M., Lima, K. M., Martin-Hirsch, P. L., Sreedhar, H., Trevisan, J., Walsh, M. J., et al. (2019). Standardization of complex biologically derived spectrochemical datasets. Nature protocols, 14(5):1546–1577.
Neville, B. W. and Day, T. A. (2002). Oral cancer and precancerous lesions. CA: a cancer journal for clinicians, 52(4):195–215.
Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12):1565–1567.
Oliveira, S. W., Cardoso-Sousa, L., Georjutti, R. P., Shimizu, J. F., Silva, S., Caixeta, D. C., Guevara-Vega, M., Cunha, T. M., Carneiro, M. G., Goulart, L. R., et al. (2023). Salivary detection of zika virus infection using ATR-FTIR spectroscopy coupled with machine learning algorithms and univariate analysis: A proof-of-concept animal study. Diagnostics, 13(8):1443.
Peng, J., Peng, S., Xie, Q., and Wei, J. (2011). Baseline correction combined partial least squares algorithm and its application in on-line fourier transform infrared quantitative analysis. Analytica chimica acta, 690(2):162–168.
Peng, X., Dai, R., Ma, Y., Lin, B., Hui, X., Chen, X., and Lv, R. (2022). Early diagnosis and bioimaging of lung adenocarcinoma cells/organs based on spectroscopy machine learning. Journal of Innovative Optical Health Sciences, 15(02):2250011.
Ralbovsky, N. M. and Lednev, I. K. (2020). Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chemical Society Reviews, 49(20):7428–7453.
Rivera, C. (2015). Essentials of oral cancer. International journal of clinical and experimental pathology, 8(9):11884.
Sala, A., Anderson, D. J., Brennan, P. M., Butler, H. J., Cameron, J. M., Jenkinson, M. D., Rinaldi, C., Theakstone, A. G., and Baker, M. J. (2020). Biofluid diagnostics by ftir spectroscopy: A platform technology for cancer detection. Cancer letters, 477:122–130.
Shaikh, S., Yadav, D. K., and Rawal, R. (2021). Saliva based non invasive screening of oral submucous fibrosis using ATR-FTIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis, 203:114202.
Silva, L. G., Péres, A. F., Freitas, D. L., Morais, C. L., Martin, F. L., Crispim, J. C., and Lima, K. M. (2020). ATR-FTIR spectroscopy in blood plasma combined with multivariate analysis to detect hiv infection in pregnant women. Scientific reports, 10(1):1–7.
Sitnikova, V. E., Kotkova, M. A., Nosenko, T. N., Kotkova, T. N., Martynova, D. M., and Uspenskaya, M. V. (2020). Breast cancer detection by ATR-FTIR spectroscopy of blood serum and multivariate data-analysis. Talanta, 214:120857.
Xu, D., Liu, S., Cai, Y., and Yang, C. (2019). Baseline correction method based on doubly reweighted penalized least squares. Applied optics, 58(14):3913–3920.
Zhang, L., Xiao, M., Wang, Y., Peng, S., Chen, Y., Zhang, D., Zhang, D., Guo, Y., Wang, X., Luo, H., et al. (2021). Fast screening and primary diagnosis of covid-19 by atr–ft-ir. Analytical chemistry, 93(4):2191–2199.
Baker, M. J., Trevisan, J., Bassan, P., Bhargava, R., Butler, H. J., Dorling, K. M., Fielden, P. R., Fogarty, S. W., Fullwood, N. J., Heys, K. A., et al. (2014). Using fourier transform ir spectroscopy to analyze biological materials. Nature protocols, 9(8):1771–1791.
Balakrishnama, S. and Ganapathiraju, A. (1998). Linear discriminant analysis-a brief tutorial. Institute for Signal and information Processing, 18(1998):1–8.
Berrar, D. (2019). Cross-validation.
Boelens, H. F., Eilers, P. H., and Hankemeier, T. (2005). Sign constraints improve the detection of differences between complex spectral data sets: Lcir as an example. Analytical chemistry, 77(24):7998–8007.
Bozkurt, O., Severcan, M., and Severcan, F. (2010). Diabetes induces compositional, structural and functional alterations on rat skeletal soleus muscle revealed by ftir spectroscopy: a comparative study with edl muscle. Analyst, 135(12):3110–3119.
Bunaciu, A. A., Hoang, V. D., and Aboul-Enein, H. Y. (2015). Applications of ft-ir spectrophotometry in cancer diagnostics. Critical reviews in analytical chemistry, 45(2):156–165.
Butler, H. J., Brennan, P. M., Cameron, J. M., Finlayson, D., Hegarty, M. G., Jenkinson, M. D., Palmer, D. S., Smith, B. R., and Baker, M. J. (2019). Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer. Nature communications, 10(1):1–9.
Caixeta, D. C., Carneiro, M. G., Rodrigues, R., Alves, D. C. T., Goulart, L. R., Cunha, T. M., Espindola, F. S., Vitorino, R., and Sabino-Silva, R. (2023). Salivary ATR-FTIR spectroscopy coupled with support vector machine classification for screening of type 2 diabetes mellitus. Diagnostics, 13(8):1396.
Carneiro, M. G. (2016). Redes complexas para classificação de dados via conformidade de padrão, caracterização de importância e otimização estrutural. PhD thesis, Universidade de São Paulo.
Falamas, A., Faur, C., Ciupe, S., Chirila, M., Rotaru, H., Hedesiu, M., and Pinzaru, S. C. (2021). Rapid and noninvasive diagnosis of oral and oropharyngeal cancer based on micro-raman and ft-ir spectra of saliva. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 252:119477.
Giamougiannis, P., Morais, C. L., Rodriguez, B., Wood, N. J., Martin-Hirsch, P. L., and Martin, F. L. (2021). Detection of ovarian cancer (±neo-adjuvant chemotherapy effects) via ATR-FTIR spectroscopy: comparative analysis of blood and urine biofluids in a large patient cohort. Analytical and bioanalytical chemistry, 413(20):5095–5107.
Janik, P. and Lobos, T. (2006). Automated classification of power-quality disturbances using svm and rbf networks. IEEE Transactions on Power Delivery, 21(3):1663–1669.
Jubair, F., Al-karadsheh, O., Malamos, D., Al Mahdi, S., Saad, Y., and Hassona, Y. (2022). A novel lightweight deep convolutional neural network for early detection of oral cancer. Oral Diseases, 28(4):1123–1130.
Kavarthapu, A. and Gurumoorthy, K. (2021). Linking chronic periodontitis and oral cancer: A review. Oral Oncology, 121:105375.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25.
Liaw, A., Wiener, M., et al. (2002). Classification and regression by randomforest. R news, 2(3):18–22.
Lobbezoo, F., Aarab, G., Verhoeff, M. C., and Volgenant, C. M. (2022). The value of oral care in dying and death. The Lancet, 399(10342):2187–2188.
Losq, C. L. (2018). Rampy: a Python library for processing spectroscopic (IR, Raman, XAS...) data.
Malamud, D. (2011). Saliva as a diagnostic fluid. Dental Clinics, 55(1):159–178.
Martinez-Cuazitl, A., Vazquez-Zapien, G. J., Sanchez-Brito, M., Limon-Pacheco, J. H., Guerrero-Ruiz, M., Garibay-Gonzalez, F., Delgado-Macuil, R. J., de Jesus, M. G. G., Corona-Perezgrovas, M. A., Pereyra-Talamantes, A., et al. (2021). ATR-FTIR spectrum analysis of saliva samples from covid-19 positive patients. Scientific Reports, 11(1):1–14.
Mikkonen, J. J., Raittila, J., Rieppo, L., Lappalainen, R., Kullaa, A. M., and Myllymaa, S. (2016). Fourier transform infrared spectroscopy and photoacoustic spectroscopy for saliva analysis. Applied Spectroscopy, 70(9):1502–1510.
Morais, C. L., Paraskevaidi, M., Cui, L., Fullwood, N. J., Isabelle, M., Lima, K. M., Martin-Hirsch, P. L., Sreedhar, H., Trevisan, J., Walsh, M. J., et al. (2019). Standardization of complex biologically derived spectrochemical datasets. Nature protocols, 14(5):1546–1577.
Neville, B. W. and Day, T. A. (2002). Oral cancer and precancerous lesions. CA: a cancer journal for clinicians, 52(4):195–215.
Noble, W. S. (2006). What is a support vector machine? Nature biotechnology, 24(12):1565–1567.
Oliveira, S. W., Cardoso-Sousa, L., Georjutti, R. P., Shimizu, J. F., Silva, S., Caixeta, D. C., Guevara-Vega, M., Cunha, T. M., Carneiro, M. G., Goulart, L. R., et al. (2023). Salivary detection of zika virus infection using ATR-FTIR spectroscopy coupled with machine learning algorithms and univariate analysis: A proof-of-concept animal study. Diagnostics, 13(8):1443.
Peng, J., Peng, S., Xie, Q., and Wei, J. (2011). Baseline correction combined partial least squares algorithm and its application in on-line fourier transform infrared quantitative analysis. Analytica chimica acta, 690(2):162–168.
Peng, X., Dai, R., Ma, Y., Lin, B., Hui, X., Chen, X., and Lv, R. (2022). Early diagnosis and bioimaging of lung adenocarcinoma cells/organs based on spectroscopy machine learning. Journal of Innovative Optical Health Sciences, 15(02):2250011.
Ralbovsky, N. M. and Lednev, I. K. (2020). Towards development of a novel universal medical diagnostic method: Raman spectroscopy and machine learning. Chemical Society Reviews, 49(20):7428–7453.
Rivera, C. (2015). Essentials of oral cancer. International journal of clinical and experimental pathology, 8(9):11884.
Sala, A., Anderson, D. J., Brennan, P. M., Butler, H. J., Cameron, J. M., Jenkinson, M. D., Rinaldi, C., Theakstone, A. G., and Baker, M. J. (2020). Biofluid diagnostics by ftir spectroscopy: A platform technology for cancer detection. Cancer letters, 477:122–130.
Shaikh, S., Yadav, D. K., and Rawal, R. (2021). Saliva based non invasive screening of oral submucous fibrosis using ATR-FTIR spectroscopy. Journal of Pharmaceutical and Biomedical Analysis, 203:114202.
Silva, L. G., Péres, A. F., Freitas, D. L., Morais, C. L., Martin, F. L., Crispim, J. C., and Lima, K. M. (2020). ATR-FTIR spectroscopy in blood plasma combined with multivariate analysis to detect hiv infection in pregnant women. Scientific reports, 10(1):1–7.
Sitnikova, V. E., Kotkova, M. A., Nosenko, T. N., Kotkova, T. N., Martynova, D. M., and Uspenskaya, M. V. (2020). Breast cancer detection by ATR-FTIR spectroscopy of blood serum and multivariate data-analysis. Talanta, 214:120857.
Xu, D., Liu, S., Cai, Y., and Yang, C. (2019). Baseline correction method based on doubly reweighted penalized least squares. Applied optics, 58(14):3913–3920.
Zhang, L., Xiao, M., Wang, Y., Peng, S., Chen, Y., Zhang, D., Zhang, D., Guo, Y., Wang, X., Luo, H., et al. (2021). Fast screening and primary diagnosis of covid-19 by atr–ft-ir. Analytical chemistry, 93(4):2191–2199.
Publicado
25/09/2023
Como Citar
MUNDIM FILHO, Anagê C.; FERNANDES, Janayna M.; SABINO-SILVA, Robinson; CARNEIRO, Murillo G..
OCANSpectra: an oral cancer detection system from salivary ATR-FTIR spectroscopy. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 984-996.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234549.
