Classificação do câncer de pulmão de células não pequenas usando índice de diversidade filogenética e índices de forma em uma abordagem Radiomics
Resumo
O câncer de pulmão é o tipo de câncer mais comum e tem a maior taxa de mortalidade no mundo. O processo automático para o diagnóstico por sistemas de visão por computador, através de imagens médicas, fornece uma interpretação sobre a patologia. Este trabalho propõe a classificação do câncer de pulmão de células não pequenas utilizando como descritor de textura o índice de diversidade filogenética baseado em topologia e alguns índices de forma baseados em região e contorno, adaptando-os para a abordagem Radiomics.Os testes mostraram resultados promissores de 98,83% de acurácia, um índice Kappa de 0,993 e umaárea sob a curva ROC de 0,999.
Referências
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Huynh, E., Coroller, T. P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I., Mak, R. H., and Aerts, H. J. (2016). Ct-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiotherapy and Oncology, 120(2):258–266.
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Oliveira, M. C., de Lucena, D. J. F., and Felix, A. (2017). Recuperação de nódulos pulmonares por conteúdo: uma abordagem radiomics em pesquisa reprodutível.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B. (2016). Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging, 35(5):1160–1169.
Shen, C., Liu, Z., Guan, M., Song, J., Lian, Y., Wang, S., Tang, Z., Dong, D., Kong, L., Wang, M., et al. (2017). 2d and 3d ct radiomics features prognostic performance comparison in non-small cell lung cancer. Translational oncology, 10(6):886–894.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013). Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 847–855. ACM.
van Timmeren, J. E., Leijenaar, R. T., van Elmpt, W., Reymen, B., Oberije, C., Monshouwer, R., Bussink, J., Brink, C., Hansen, O., and Lambin, P. (2017). Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam ct images. Radiotherapy and Oncology, 123(3):363–369.
Vane-Wright, R. I., Humphries, C. J., and Williams, P. H. (1991). What to protect?—systematics and the agony of choice. Biological conservation, 55(3):235–254.
Azuaje, F. (2006). Witten ih, frank e: Data mining: Practical machine learning tools and techniques 2nd edition.
Cook, R. J. (1998). Kappa. Encyclopedia of biostatistics.
Coroller, T. P., Agrawal, V., Narayan, V., Hou, Y., Grossmann, P., Lee, S. W., Mak, R. H., and Aerts, H. J. (2016). Radiomic phenotype features predict pathological response in non-small cell lung cancer. Radiotherapy and Oncology, 119(3):480–486.
da Silva Sousa, J. R. F., Silva, A. C., and De Paiva, A. C. (2007). Lung structure classification using 3d geometric measurements and svm. In Iberoamerican Congress on Pattern Recognition, pages 783–792. Springer.
de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2017). Computer-aided diagnosis of lung nodules in computed tomography by using phylogenetic diversity, genetic algorithm, and svm. Journal of digital imaging, 30(6):812–822.
de Oliveira, F. S. S., de Carvalho Filho, A. O., Silva, A. C., de Paiva, A. C., and Gattass, M. (2015). Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and svm. Computers in biology and medicine, 57:42–53.
Dean, J. (2014). Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley & Sons.
Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., Parkin, D. M., Forman, D., and Bray, F. (2015). Cancer incidence and mortality worldwide: sources, methods and major patterns in globocan 2012. International journal of cancer, 136(5).
Giger, M. L., Huo, Z., Kupinski, M. A., and Vyborny, C. J. (2000). Computer-aided diagnosis in mammography. Handbook of medical imaging, 2:915–1004.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The weka data mining software: an update. ACM SIGKDD explorations newsletter, 11(1):10–18.
Hanley, J. A. and McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1):29–36.
Huynh, E., Coroller, T. P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I., Mak, R. H., and Aerts, H. J. (2016). Ct-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiotherapy and Oncology, 120(2):258–266.
Keith, M., Chimimba, C., Reyers, B., and Van Jaarsveld, A. (2005). Taxonomic and phylogenetic distinctiveness in regional conservation assessments: a case study based on extant south african chiroptera and carnivora. In Animal Conservation forum, volume 8, pages 279–288. Cambridge University Press.
Oliveira, M. C., de Lucena, D. J. F., and Felix, A. (2017). Recuperação de nódulos pulmonares por conteúdo: uma abordagem radiomics em pesquisa reprodutível.
Setio, A. A. A., Ciompi, F., Litjens, G., Gerke, P., Jacobs, C., van Riel, S. J., Wille, M. M. W., Naqibullah, M., Sánchez, C. I., and van Ginneken, B. (2016). Pulmonary nodule detection in ct images: false positive reduction using multi-view convolutional networks. IEEE transactions on medical imaging, 35(5):1160–1169.
Shen, C., Liu, Z., Guan, M., Song, J., Lian, Y., Wang, S., Tang, Z., Dong, D., Kong, L., Wang, M., et al. (2017). 2d and 3d ct radiomics features prognostic performance comparison in non-small cell lung cancer. Translational oncology, 10(6):886–894.
Thornton, C., Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2013). Auto-weka: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 847–855. ACM.
van Timmeren, J. E., Leijenaar, R. T., van Elmpt, W., Reymen, B., Oberije, C., Monshouwer, R., Bussink, J., Brink, C., Hansen, O., and Lambin, P. (2017). Survival prediction of non-small cell lung cancer patients using radiomics analyses of cone-beam ct images. Radiotherapy and Oncology, 123(3):363–369.
Vane-Wright, R. I., Humphries, C. J., and Williams, P. H. (1991). What to protect?—systematics and the agony of choice. Biological conservation, 55(3):235–254.
Publicado
22/07/2018
Como Citar
DOS S. NETO, Antonino Calisto; DINIZ, João O. B.; DINIZ, Pedro H. B.; CAVALCANTE, André B.; SILVA, Aristófanes C.; DE PAIVA, Anselmo C..
Classificação do câncer de pulmão de células não pequenas usando índice de diversidade filogenética e índices de forma em uma abordagem Radiomics. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 70-81.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2018.3676.
