Arcabouço para Classificação, Recuperação por Conteúdo e Radiômica de Imagens Médicas: uma investigação de biomarcadores quantitativos para o câncer de pulmão
Resumo
Este trabalho tentou diminuir as limitações de característica (representação numérica ineficiente das imagens) e de desempenho (qualidade baixa de integração) que sistemas de suporte a decisões clínicas podem apresentar. Alguns conjuntos de imagens foram produzidos para a pesquisa. Modelos semiautomáticos de reconhecimento de padrões, recuperação por conteúdo, classificação e avaliação prognóstica foram desenvolvidos para compor um arcabouço de análise de imagens médicas. O arcabouço mostrou potencial de integração com sistemas de informação em saúde e identificou diversos biomarcadores. Por fim, esta tese contribuiu para a identificação de uma assinatura radiômica do câncer de pulmão para a estratificação de risco do paciente.
Referências
Aerts, H., Velazquez, E., Leijenaar, R., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Kains, B., Rietveld, D., Hoebers, F., Rietbergen, M., Leemans, C., Dekker, A., Gillies, R., and Lambin, P. (2014). Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications, 5:4006.
Coroller, T., Grossmann, P., Hou, Y., Velazquez, E., Leijenaar, R., Hermann, G., Lambin, P., Kains, B., Mak, R., and Aerts, H. (2015). CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 114(3):345–350.
Digumarthy, S., Padole, A., Gullo, R., Sequist, L., and Kalra, M. (2019). Can CT radiomic analysis in nsclc predict histology and EGFR mutation status? Medicine, 98:e13963.
Ferreira, J., Santos, M., Cipriano, F., Fabro, A., and Marques, P. (2018). Radiomicsbased features for pattern recognition of lung cancer histopathology and metastases. Computer Methods and Programs in Biomedicine, 159:23–30.
Ferreira, J., Santos, M., Hironaka, T., Cipriano, F., Fabro, A., Yoshida, H., and Marques, P. (2019a). Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis. Sup Int J Comput Assist Radiol Surg, page S79.
Ferreira, J., Santos, M., Kikuchi, Y., Faleiros, M., Cipriano, F., Fabro, A., Yoshida, H., and Marques, P. (2019b). A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors. Sup Int J Comput Assist Radiol Surg, page S78.
Ferreira, J., Santos, M., Tenório, A., Faleiros, M., Cipriano, F., Fabro, A., Näppi, J.,
Yoshida, H., and Marques, P. (2020). CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J Comput Assist Radiol Surg, 15:163–172.
Ferreira, J. R., Oliveira, M. C., and 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.
Ferreira, J. R., Oliveira, M. C., and Azevedo-Marques, P. M. (2017). Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine. The Journal of Supercomputing, 73(8):3451–3467.
Ferreira, Junior, J. R. and Oliveira, M. C. (2015). Evaluating margin sharpness analysis on similar pulmonary nodule retrieval. In Proceedings of 28th IEEE International Symposium on Computer-Based Medical Systems, pages 60–65.
Ferreira, Junior, J. R. and Oliveira, M. C. (2017). Unidade de processamento gráfico no diagnóstico auxiliado por computador. Journal of Health Informatics, 9(3):103–108.
Ferreira Jr, J., Santos, M., Machado, C., Faleiros, M., Correia, N., Cipriano, F., Fabro, A., and Marques, P. (2020). Análise radiômica do câncer de pulm˜ao para avaliação prognóstica do paciente e da heterogeneidade intratumoral. Radiol Bras, Ahead.
Ferreira Junior, J. R., Azevedo-Marques, P. M., and Oliveira, M. C. (2017). Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. Int J Comput Assist Radiol Surg, 12(3):509–517.
Ferreira Junior, J. R., Oliveira, M. C., and Azevedo-Marques, P. M. (2018). Characterization of pulmonary nodules based on features of margin sharpness and texture. Journal of Digital Imaging, 31(4):451–463.
Gillies, R., Kinahan, P., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–577.
Sacconi, B., Anzidei, M., Leonardi, A., Boni, F., Saba, L., Scipione, R., Anile, M., Rengo, M., Longo, F., Bezzi, M., Venuta, F., Napoli, A., Laghi, A., and Catalano, C. (2017).
Analysis of ct features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with egfr mutations and survival rates. Clin Radiol, 72:443–50.
Santos, M., Ferreira, J.,Wada, D., Tenório, A., Barbosa, M., and Marques, P. (2019). Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387–396.
Wu, J., Shultz, D., Gudur, M., Rubin, D., Loo, B., Diehn, M., and Li, R. (2016). Earlystage non–small cell lung cancer: Quantitative imaging characteristics of 18f fluorodeoxyglucose pet/ct allow prediction of distant metastasis. Radiology, 281:270–78.
Zhou, H., Dong, D., Chen, B., Fang, M., Cheng, Y., Gan, Y., Zhang, R., Zhang, L., Zang, Y., Liu, Z., et al. (2018). Diagnosis of distant metastasis of lung cancer: Based on clinical and radiomic features. Translational Oncology, 11(1):31–36.
Zhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018). Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. European Radiology, 28(7):2772–2778.
Coroller, T., Grossmann, P., Hou, Y., Velazquez, E., Leijenaar, R., Hermann, G., Lambin, P., Kains, B., Mak, R., and Aerts, H. (2015). CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol, 114(3):345–350.
Digumarthy, S., Padole, A., Gullo, R., Sequist, L., and Kalra, M. (2019). Can CT radiomic analysis in nsclc predict histology and EGFR mutation status? Medicine, 98:e13963.
Ferreira, J., Santos, M., Cipriano, F., Fabro, A., and Marques, P. (2018). Radiomicsbased features for pattern recognition of lung cancer histopathology and metastases. Computer Methods and Programs in Biomedicine, 159:23–30.
Ferreira, J., Santos, M., Hironaka, T., Cipriano, F., Fabro, A., Yoshida, H., and Marques, P. (2019a). Deep learning-based radiomics of primary lung tumors in CT images for prediction of distant metastasis. Sup Int J Comput Assist Radiol Surg, page S79.
Ferreira, J., Santos, M., Kikuchi, Y., Faleiros, M., Cipriano, F., Fabro, A., Yoshida, H., and Marques, P. (2019b). A radiomics approach for differentiation of pseudocavitation from cavitation on lung cancer tumors. Sup Int J Comput Assist Radiol Surg, page S78.
Ferreira, J., Santos, M., Tenório, A., Faleiros, M., Cipriano, F., Fabro, A., Näppi, J.,
Yoshida, H., and Marques, P. (2020). CT-based radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. Int J Comput Assist Radiol Surg, 15:163–172.
Ferreira, J. R., Oliveira, M. C., and 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.
Ferreira, J. R., Oliveira, M. C., and Azevedo-Marques, P. M. (2017). Integrating 3D image descriptors of margin sharpness and texture on a GPU-optimized similar pulmonary nodule retrieval engine. The Journal of Supercomputing, 73(8):3451–3467.
Ferreira, Junior, J. R. and Oliveira, M. C. (2015). Evaluating margin sharpness analysis on similar pulmonary nodule retrieval. In Proceedings of 28th IEEE International Symposium on Computer-Based Medical Systems, pages 60–65.
Ferreira, Junior, J. R. and Oliveira, M. C. (2017). Unidade de processamento gráfico no diagnóstico auxiliado por computador. Journal of Health Informatics, 9(3):103–108.
Ferreira Jr, J., Santos, M., Machado, C., Faleiros, M., Correia, N., Cipriano, F., Fabro, A., and Marques, P. (2020). Análise radiômica do câncer de pulm˜ao para avaliação prognóstica do paciente e da heterogeneidade intratumoral. Radiol Bras, Ahead.
Ferreira Junior, J. R., Azevedo-Marques, P. M., and Oliveira, M. C. (2017). Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval. Int J Comput Assist Radiol Surg, 12(3):509–517.
Ferreira Junior, J. R., Oliveira, M. C., and Azevedo-Marques, P. M. (2018). Characterization of pulmonary nodules based on features of margin sharpness and texture. Journal of Digital Imaging, 31(4):451–463.
Gillies, R., Kinahan, P., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–577.
Sacconi, B., Anzidei, M., Leonardi, A., Boni, F., Saba, L., Scipione, R., Anile, M., Rengo, M., Longo, F., Bezzi, M., Venuta, F., Napoli, A., Laghi, A., and Catalano, C. (2017).
Analysis of ct features and quantitative texture analysis in patients with lung adenocarcinoma: a correlation with egfr mutations and survival rates. Clin Radiol, 72:443–50.
Santos, M., Ferreira, J.,Wada, D., Tenório, A., Barbosa, M., and Marques, P. (2019). Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387–396.
Wu, J., Shultz, D., Gudur, M., Rubin, D., Loo, B., Diehn, M., and Li, R. (2016). Earlystage non–small cell lung cancer: Quantitative imaging characteristics of 18f fluorodeoxyglucose pet/ct allow prediction of distant metastasis. Radiology, 281:270–78.
Zhou, H., Dong, D., Chen, B., Fang, M., Cheng, Y., Gan, Y., Zhang, R., Zhang, L., Zang, Y., Liu, Z., et al. (2018). Diagnosis of distant metastasis of lung cancer: Based on clinical and radiomic features. Translational Oncology, 11(1):31–36.
Zhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018). Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer. European Radiology, 28(7):2772–2778.
Publicado
15/09/2020
Como Citar
FERREIRA JUNIOR, José Raniery; SANTOS, Marcel Koenigkam; AZEVEDO-MARQUES, Paulo Mazzoncini.
Arcabouço para Classificação, Recuperação por Conteúdo e Radiômica de Imagens Médicas: uma investigação de biomarcadores quantitativos para o câncer de pulmão. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 13-18.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas.2020.11551.
