Classificação de Gliomas Utilizando Índices de Biodiversidade e de Diversidade Filogenética em Imagens por Ressonância Magnética Através de uma Abordagem Radiomics
Resumo
Gliomas estão entre os tumores cerebrais malignos mais comuns. Eles podem ser classificados entre gliomas de baixo e alto grau e sua identificação precoce é fundamental para o direcionamento do tratamento apli- cado. Utilizando uma abordagem radiomics, o presente trabalho propõe o uso de ı́ndices de biodiversidade e de diversidade filogenética, definidos no campo da biologia, no problema de classificação de gliomas. O método proposto apre- sentou resultados promissores, com AUC, acurácia, sensibilidade e especifici- dade de 0,926, 0,902, 0,962 e 0,733, respectivamente.
Referências
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Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., Scheithauer, B. W., and Kleihues, P. (2007). The 2007 who classification of tumours of the central nervous system. Acta Neuropathologica, 114(2):97–109.
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NBTS (2017). Brain Tumors By The Numbers. http://events.braintumor.org/wp- content/uploads/2017/11/BrainTumorsBytheNumbers.pdf. Acesso em: dez. 2018.
Neto, A. C. d. S., Diniz, P. H. B., Diniz, J. O. B., Cavalcante, A. B., Silva, A. C., de Paiva, A. C., and de Almeida, J. D. S. (2018). Diagnosis of Non-Small Cell Lung Cancer Using Phylogenetic Diversity in Radiomics Context. In Campilho, A., Karray, F., and ter Haar Romeny, B., editors, Image Analysis and Recognition, pages 598–604, Cham. Springer International Publishing. 978-3-319-93000-8. http://dx.doi.org/10.1007/978-3-319-93000-8_68
Weitzman, M. L. (1992). On diversity. Quarterly Journal of Economics, 107(2):363– 405.
Zacharaki, E. I., Wang, S., Chawla, S., Yoo, D. S., Wolf, R., Melhem, E. R., and Davatzikos, C. (2009). Classification of Brain Tumor Type and Grade Using MRI. Magnetic Resonance in Medicine. http://dx.doi.org/10.1002/mrm.22147
Zhang, X., Yan, L.-F., Yu-Chuan Hu, G. L., Yang, Y., Han, Y., Sun, Y., Liu, Z.-C., Tian, Q., Han, Z.-Y., Liu, L.-D., Hu, B.-Q., Qiu, Z.-Y., Wang, W., and Cui, G.-B. (2017). Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget. http://dx.doi.org/10.18632/oncotarget.18001
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5–32. http://dx.doi.org/10.1023/A:1010933404324
Campos, D. and Isaza, J. F. (2009). A geometrical index for measuring species diversity. Ecological Indicators, 9(4):651 – 658. ISSN: 1470-160X. http://dx.doi.org/10.1016/j.ecolind.2008.07.007
Chen, W., Liu, B., Peng, S., Sun, J., and Qiao, X (2018). Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics. International Journal of Biomedical Imaging, 2018(2512037):11. http://dx.doi.org/10.1155/2018/2512037
Cho, H., Lee, S., Kim, J., and Park, H. (2018). Classification of the glioma grading using radiomics analysis. PeerJ 6:e5982. http://dx.doi.org/10.7717/peerj.5982.
Cho, H. and Park, H. (2017). Classification of low-grade and high-grade glioma using multi-modal image radiomics features. volume 2017, pages 3081–3084. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). http://dx.doi.org/10.1109/EMBC.2017.8037508.
Clarke, K. and Warwick, R. (2001). Change in Marine Communities: An Approach to Statistical Analysis and Interpretation. Primer-E Ltd: Plymouth, UK.
Clarke, K. R. and Warwick, R. M. (1998). A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology, 35(4):523–531. http://dx.doi.org/10.1046/j.1365-2664.1998.3540523.x
Claus, E. B., Walsh, K. M., Wiencke, J. K., Molinaro, A. M., Wiemels, J. L., Schildkraut, J. M., Bondy, M. L., Berger, M., Jenkins, R., and Wrensch, M. (2015). Survival and low-grade glioma: the emergence of genetic information. Neurosurgical Focus FOC, 38(1). http://dx.doi.org/10.3171/2014.10.FOCUS12367
Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine Learning,20(3):273–297. http://dx.doi.org/10.1007/BF00994018
de Carvalho Filho, A. O., Silva, A. C., Cardoso de Paiva, A., 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. ISSN 1618-727X. http://dx.doi.org/10.1007/s10278-017-9973-6
ETSUS (2012). Meios de Contraste em RM. http://rle.dainf.ct.utfpr.edu.br/hipermidia/index.php/ressonancia-magnetica/meios-de-contraste-em-rm. Acesso em: dez. 2018.
Faith, D. (1994). Phylogenetic pattern and the quantification of organismal biodiversity. Philosophical transactions of the Royal Society of London. Series B, Biological sciences, 345:45–58. http://dx.doi.org/10.1098/rstb.1994.0085
Geurts, P., Ernst, D., and Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1):3–42. ISSN 1573-0565. http://dx.doi.org/10.1007/s10994-006-6226-1
Gillies, R. J., Kinahan, P. E., and Hricak, H. (2016). Radiomics: Images Are More Than Pictures, They Are Data. Radiology, 278(2). http://dx.doi.org/10.1148/radiol.2015151169
Gonzalez, R. C. and Woods, R. E. (2006). Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle River, NJ, USA.
Izsák, J. and Papp, L. (2000). A link between ecological diversity indices and measures of biodiversity. Ecological Modelling, 130(1):151 – 156. http://dx.doi.org/10.1016/S0304-3800(00)00203-9
Karydis, M. and Tsirtsis, G. (1996). Ecological indices: A biometric approach for assessing eutrophication levels in the marine environment. Science of The Total Environment, 186:209–219. http://dx.doi.org/10.1016/0048-9697(96)05114-5
Lamb, E., Bayne, E., Holloway, G., Schieck, J., Boutin, S., Herbers, J., and Haughland, D. (2009). Indices for monitoring biodiversity change: Are some more effective than others? Ecological Indicators, 9:432–444. http://dx.doi.org/10.1016/j.ecolind.2008.06.001
Louis, D. N., Ohgaki, H., Wiestler, O. D., Cavenee, W. K., Burger, P. C., Jouvet, A., Scheithauer, B. W., and Kleihues, P. (2007). The 2007 who classification of tumours of the central nervous system. Acta Neuropathologica, 114(2):97–109.
Louis, D. N., Perry, A., Reifenberger, G., von Deimling, A., Figarella-Branger, D., Cavenee, W. K., Ohgaki, H., Wiestler, O. D., Kleihues, P., and Ellison, D. W. (2016). The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathologica, 131(6):803–820.
McGranahan, N. and Swanton, C. (2015). Biological and Therapeutic Impact of Intratumor Heterogeneity in Cancer Evolution. Cancer Cell, 28, Issue 1:141. http://dx.doi.org/10.1016/j.ccell.2014.12.001
Menze, B. H. et al. (2015). The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Transactions on Medical Imaging, 34(10):1993–2024. http://dx.doi.org/10.1109/TMI.2014.2377694
Morris, E. K., Caruso, T., Buscot, F., Fischer, M., Hancock, C., Maier, T. S., Meiners, T., Müller, C., Obermaier, E., Prati, D., Socher, S. A., Sonnemann, I., Wäschke, N., Wubet, T., Wurst, S., and Rillig, M. C. (2014). Choosing and using diversity indices: insights for ecological applications from the german biodiversity exploratories. Ecology and Evolution, 4(18):3514–3524. http://dx.doi.org/10.1002/ece3.1155
NBTS (2017). Brain Tumors By The Numbers. http://events.braintumor.org/wp- content/uploads/2017/11/BrainTumorsBytheNumbers.pdf. Acesso em: dez. 2018.
Neto, A. C. d. S., Diniz, P. H. B., Diniz, J. O. B., Cavalcante, A. B., Silva, A. C., de Paiva, A. C., and de Almeida, J. D. S. (2018). Diagnosis of Non-Small Cell Lung Cancer Using Phylogenetic Diversity in Radiomics Context. In Campilho, A., Karray, F., and ter Haar Romeny, B., editors, Image Analysis and Recognition, pages 598–604, Cham. Springer International Publishing. 978-3-319-93000-8. http://dx.doi.org/10.1007/978-3-319-93000-8_68
Weitzman, M. L. (1992). On diversity. Quarterly Journal of Economics, 107(2):363– 405.
Zacharaki, E. I., Wang, S., Chawla, S., Yoo, D. S., Wolf, R., Melhem, E. R., and Davatzikos, C. (2009). Classification of Brain Tumor Type and Grade Using MRI. Magnetic Resonance in Medicine. http://dx.doi.org/10.1002/mrm.22147
Zhang, X., Yan, L.-F., Yu-Chuan Hu, G. L., Yang, Y., Han, Y., Sun, Y., Liu, Z.-C., Tian, Q., Han, Z.-Y., Liu, L.-D., Hu, B.-Q., Qiu, Z.-Y., Wang, W., and Cui, G.-B. (2017). Optimizing a machine learning based glioma grading system using multi-parametric MRI histogram and texture features. Oncotarget. http://dx.doi.org/10.18632/oncotarget.18001
Publicado
11/06/2019
Como Citar
DUARTE, Fernanda; SILVA, Aristófanes C.; GATTASS, Marcelo; C. DOS S. NETO, Antonino.
Classificação de Gliomas Utilizando Índices de Biodiversidade e de Diversidade Filogenética em Imagens por Ressonância Magnética Através de uma Abordagem Radiomics. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 22-33.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6239.
