Os efeitos da utilização de atributos perinodulares na classificação de nódulos pulmonares
Resumo
Atualmente grande parte dos sistemas CADx vem utilizando apenas descritores oriundos da região do nódulo pulmonar. Trabalhos recentes indicam que há uma interação significativa entre o nódulo pulmonar e seu entorno, o parênquima, entretanto essa região tem sido pouco utilizada para o processo de diagnóstico do câncer pulmonar. O objetivo deste trabalho foi investigar o desempenho de descritores de imagem extraídos das regiões do nódulo, borda e parênquima (atributos intranodulares e perinodulares), na identificação de seu potencial para malignidade. Neste trabalho foram avaliados 897 nódulos pulmonares com 121 descritores de imagem extraídos da região tumoral. Os descritores foram selecionados por algoritmo genético e a avaliação de desempenho foi feita através da área sob a curva ROC (AUC) com validação cruzada de 10 folds e 5 repetições. Nosso melhor modelo avaliado obteve AUC média de 0,916, acurácia de 84,26%, sensibilidade de 84,45% e especificidade de 83,84%. Os resultados obtidos sustentam que a utilização de atributos perinodulares melhoram efetivamente o desempenho de classificação de nódulos pulmonares.
Referências
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Knight, S., Crosbie, P., Balata, H., Chudziak, J., Hussell, T., and Dive, C. (2017). Progress and prospects of early detection in lung cancer. Open Biology, 7:170070.
Paul, R., Hawkins, S. H., Schabath, M. B., Gillies, R. J., Hall, L. O., and Goldgof, D. B. (2018). Predicting malignant nodules by fusing deep features with classical radiomics features. Journal of Medical Imaging, 5(01):1.
Stec, N., Arje, D., Moody, A. R., Krupinski, E. A., and Tyrrell, P. N. (2018). A systematic review of fatigue in radiology: Is it a problem?
Uthoff, J., Stephens, M. J., Newell, J. D., Hoffman, E. A., Larson, J., Koehn, N., De Stefano, F. A., Lusk, C. M., Wenzlaff, A. S., Watza, D., Neslund-Dudas, C., Carr, L. L., Lynch, D. A., Schwartz, A. G., and Sieren, J. C. (2019). Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Medical Physics, page mp.13592.
Way, T. W., Sahiner, B., Chan, H.-P., Hadjiiski, L., Cascade, P. N., Chughtai, A., Bogot, N., and Kazerooni, E. (2009). Computer-aided diagnosis of pulmonary nodules on ct scans: Improvement of classification performance with nodule surface features. Medical Physics, 36(7):3086–3098.
Wei, G., Cao, H., Ma, H., Qi, S., Qian, W., and Ma, Z. (2018). Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric. Journal of Medical Systems, 42(1).
World Health Organisation (2019). Cancer. http://www.who.int/ mediacentre/factsheets/fs282/fr/, Last accessed on 2019-08-22.
Xu, J., Napel, S., Greenspan, H., Beaulieu, C., Agrawal, N., and Rubin, D. (2012). Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Medical physics, 39:5405–18.
Zhou, H., Dong, D., Chen, B., Fang, M., Cheng, Y., Gan, Y., Zhang, R., Zhang, L., Zang, Y., Liu, Z., Zheng, H., Li, W., and Tian, J. (2018). Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features. Translational Oncology, 11(1):31–36.
Akgul, C., Rubin, D., Napel, S., Beaulieu, C., Greenspan, H., and Acar, B. (2010).¨ Content-based image retrieval in radiology: Current status and future directions. Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology, 24:208–22.
Armato, S. G., McLennan, G., Bidaut, L., McNitt-Gray, M. F., Meyer, C. R., Reeves, A. P., Zhao, B., Aberle, D. R., Henschke, C. I., Hoffman, E. A., et al. (2011). The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics, 38(2):915– 931.
Bannister, N. and Broggio, J. (2016). Cancer survival by stage at diagnosis for england (experimental statistics): Adults diagnosed 2012, 2013, 2014 and followe d up to 2015. Produced in collaboration with Public Health England.
Beig, N., Khorrami, M., Alilou, M., Prasanna, P., Braman, N., Orooji, M., Rakshit, S., Bera, K., Rajiah, P., Ginsberg, J., Donatelli, C., Thawani, R., Yang, M., Jacono, F., Tiwari, P., Velcheti, V., Gilkeson, R., Linden, P., and Madabhushi, A. (2019). Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas. Radiology, 290(3):783–792.
Bray, F., Ferlay, J., Soerjomataram, I., L. Siegel, R., Torre, L., and Jemal, A. (2018). Global cancer statistics 2018: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries: Global cancer statistics 2018. CA: A Cancer Journal for Clinicians, 68.
Chen, C. H., Chang, C. K., Tu, C. Y., Liao, W. C., Wu, B. R., Chou, K. T., Chiou, Y. R., Yang, S. N., Zhang, G., and Huang, T. C. (2018). Radiomic features analysis in computed tomography images of lung nodule classification. PLoS ONE, 13(2).
Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., and Aerts, H. J. (2015). CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiotherapy and Oncology, 114(3):345–350.
Dhara, A. K., Mukhopadhyay, S., Dutta, A., Garg, M., and Khandelwal, N. (2016). A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images. Journal of Digital Imaging, 29(4):466–475.
Dilger, S., Uthoff, J., Judisch, A., Hammond, E., Mott, S., Smith, B., Newell, Jr, J., Hoffman, E., and (de Ryk) Sieren, J. (2015). Improved pulmonary nodule classification utilizing quantitative lung parenchyma features. Journal of Medical Imaging, 2:041004.
Dilger, S. K. N. (2013). The use of surrounding lung parenchyma for the automated classification of pulmonary nodules.
Felix, A., Oliveira, M., Machado, A., and Raniery, J. Using 3D Texture and Margin Sharpness Features on Classification of Small Pulmonary Nodules. Technical report.
Ferreira, J. R., de Azevedo-Marques, P. M., and Oliveira, M. C. (2017). Selecting relevant 3d image features of margin sharpness and texture for lung nodule retrieval. International Journal of Computer Assisted Radiology and Surgery, 12(3):509–517.
Firmino, M., Angelo, G., Morais, H., Dantas, M. R., and Valentim, R. (2016). Computeraided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. BioMedical Engineering OnLine, 15(1):2.
Haralick, R. M., Shanmugam, K., and Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6):610– 621.
Huang, P., Park, S., Yan, R., Lee, J., Chu, L. C., Lin, C. T., Hussien, A., Rathmell, J., Thomas, B., Chen, C., HalS, R., Ettinger, D. S., Brock, M., Hu, P., Fishman, E. K., Gabrielson, E., and Lam, S. (2018). Added value of computer-aided CT image features for early lung cancer diagnosis with small pulmonary nodules: A matched case-control study. Radiology, 286(1):286–295.
Junior, J. R. F., Oliveira, M. C., and de Azevedo-Marques, P. M. (2016). Cloud-based nosql open database of pulmonary nodules for computer-aided lung cancer diagnosis and reproducible research. Journal of digital imaging, 29(6):716–729.
Knight, S., Crosbie, P., Balata, H., Chudziak, J., Hussell, T., and Dive, C. (2017). Progress and prospects of early detection in lung cancer. Open Biology, 7:170070.
Paul, R., Hawkins, S. H., Schabath, M. B., Gillies, R. J., Hall, L. O., and Goldgof, D. B. (2018). Predicting malignant nodules by fusing deep features with classical radiomics features. Journal of Medical Imaging, 5(01):1.
Stec, N., Arje, D., Moody, A. R., Krupinski, E. A., and Tyrrell, P. N. (2018). A systematic review of fatigue in radiology: Is it a problem?
Uthoff, J., Stephens, M. J., Newell, J. D., Hoffman, E. A., Larson, J., Koehn, N., De Stefano, F. A., Lusk, C. M., Wenzlaff, A. S., Watza, D., Neslund-Dudas, C., Carr, L. L., Lynch, D. A., Schwartz, A. G., and Sieren, J. C. (2019). Machine learning approach for distinguishing malignant and benign lung nodules utilizing standardized perinodular parenchymal features from CT. Medical Physics, page mp.13592.
Way, T. W., Sahiner, B., Chan, H.-P., Hadjiiski, L., Cascade, P. N., Chughtai, A., Bogot, N., and Kazerooni, E. (2009). Computer-aided diagnosis of pulmonary nodules on ct scans: Improvement of classification performance with nodule surface features. Medical Physics, 36(7):3086–3098.
Wei, G., Cao, H., Ma, H., Qi, S., Qian, W., and Ma, Z. (2018). Content-based image retrieval for Lung Nodule Classification Using Texture Features and Learned Distance Metric. Journal of Medical Systems, 42(1).
World Health Organisation (2019). Cancer. http://www.who.int/ mediacentre/factsheets/fs282/fr/, Last accessed on 2019-08-22.
Xu, J., Napel, S., Greenspan, H., Beaulieu, C., Agrawal, N., and Rubin, D. (2012). Quantifying the margin sharpness of lesions on radiological images for content-based image retrieval. Medical physics, 39:5405–18.
Zhou, H., Dong, D., Chen, B., Fang, M., Cheng, Y., Gan, Y., Zhang, R., Zhang, L., Zang, Y., Liu, Z., Zheng, H., Li, W., and Tian, J. (2018). Diagnosis of Distant Metastasis of Lung Cancer: Based on Clinical and Radiomic Features. Translational Oncology, 11(1):31–36.
Publicado
15/09/2020
Como Citar
CALHEIROS, José; AMORIM, Lucas; LIMA, Lucas; OLIVEIRA, Marcelo.
Os efeitos da utilização de atributos perinodulares na classificação de nódulos pulmonares. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 155-166.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11510.
