Detecção de Mudanças de Densidade de Lesões Pulmonares Através do Método de Análise de Correspondência Múltipla
Resumo
O Câncer de Pulmão é um dos tipos de câncer com maior ocorrência e morbidade devido ao seu pobre prognostico, causado, em grande parte, pela dificuldade de realizar o diagnóstico de forma visual pelo médico especialista. Além disso, o grande número de pacientes e a quantidade ainda maior de imagens geradas por cada um, retardam a identificação de mudanças temporais nas lesões indeterminadas ou mesmo na resposta ao tratamento para lesões malignas. Visando reduzir a fadiga e facilitar a identificação dessas mudanças sutis, apresenta-se um método de avaliação temporal de lesões pulmonares a partir da Análise de Correspondência Múltipla (ACM). Como resultado, tem-se um método que realça as mudanças de densidade para lesões pulmonares.
Referências
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Rose, P. W. and Watson, E. (2009). What is the value of routine follow-up after diagnosis and treatment of cancer? British Journal of General Practice, 59(564):482–483.
Simon, A. F., Holmes, J. H., and Schwartz, E. S. (2020). Decreasing radiologist burnout through informatics-based solutions. Clinical Imaging, 59(2):167 – 171.
Trujillo-Ortiz, A. (2009). Multiple correspondence analysis based on the burt matrix. MATLAB Central File Exchange.
Wang, Y., Dong, C., Hu, Y., Li, C., Ren, Q., Zhang, X., Shi, H., and Zhou, M. (2020). Temporal changes of ct findings in 90 patients with covid-19 pneumonia: A longitudinal study. Radiology, 0(0):200843. PMID: 32191587.
Bertram, R., Kaakinen, J., Bensch, F., Helle, L., Lantto, E., Niemi, P., and Lundbom, N. (2016). Eye movements of radiologists reflect expertise in ct study interpretation: A potential tool to measure resident development. Radiology, 281(3):805–815. PMID: 27409563.
Brodersen, J., Voss, T., Martiny, F., Siersma, V., Barratt, A., and Heleno, B. (2020). Overdiagnosis of lung cancer with low-dose computed tomography screening: metaanalysis of the randomised clinical trials. Breathe, 16(1).
Calabro, L., Peters, S., Soria, J.-C., Di Giacomo, A. M., Barlesi, F., Covre, A., Altomonte,` M., Vegni, V., Gridelli, C., Reck, M., Rizvi, N., and Maio, M. (2020). Challenges in lung cancer therapy during the covid-19 pandemic. The Lancet Respiratory Medicine.
Calman, L., Beaver, K., Hind, D., Lorigan, P., Roberts, C., and Lloyd-Jones, M. (2011). Survival benefits from follow-up of patients with lung cancer: A systematic review and meta-analysis. Journal of Thoracic Oncology, 6(12):1993 – 2004.
Coche, E. (2016). Evaluation of lung tumor response to therapy: Current and emerging techniques. Diagnostic and Interventional Imaging, 97(10):1053 – 1065. Lung cancer.
Dandan, O. A., Hassan, A., Shammari, M. A., Jawad, M. A., Alsaif, H. S., and Alarfaj, K. (2020). Digital eye strain among radiologists: A survey-based cross-sectional study. Academic Radiology.
Ellis, P. M. and Vandermeer, R. (2011). Delays in the diagnosis of lung cancer. Journal of Thoracic Disease, 3(3).
Gonzalez, R. and Woods, R. (2009). Processamento Digital De Imagens. ADDISON WESLEY BRA.
Husson, F. and Josse, J. (2014). Multiple Correspondence Analysis. In The Visualization and Verbalization of Data, pages 164–181. CRC/PRESSE: Boca Raton.
Ibanez, L., Lorensen, B., McCormick, M., King, B., Blezek, D., Johnson, H., Lowekamp, B., jjomier, Kitware, I., Lehmann, G., Gelas, A., karthikkrishnan, Malaterre, M., Hoffman, B., Zukic, D., Will, Aylward, S. R., Liu, X., stnava, Popoff, M., Tustison, N., gabehart, Dekker, N., alex gouaillard, Sean, andinet enquobahrie, Helba, B., Vercauteren, T., Hernandez-Cerdan, P., and Doria, D. (2019). InsightSoftwareConsortium/ITK: ITK 5.0.1. Zenodo.
INCA (2020). Estimativa 2020: incidencia de c ancer no brasil.
Kim, H., Park, C. M., Park, S. J., Song, Y. S., Lee, J. H., Hwang, E. J., and Goo, J. M. (2016). Temporal changes of texture features extracted from pulmonary nodules on dynamic contrast-enhanced chest computed tomography: How influential is the scan delay? Investigative Radiology, 51(9).
Lee, S.-C., Chen, Y.-Y., Huang, T.-W., and Chang, H. (2016). Optimal delivery of followup care following pulmonary lobectomy for lung cancer. Lung Cancer: Targets and Therapy, 7:29.
McDonald, R. J., Schwartz, K. M., Eckel, L. J., Diehn, F. E., Hunt, C. H., Bartholmai, B. J., Erickson, B. J., and Kallmes, D. F. (2015). The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Academic Radiology, 22(9):1191 – 1198.
NCI (2020). Annual report to the nation 2020.
Quintanilha, D. B. P., Silva, A. f. C., Paiva, A. C. d., and Gattass, M. (2018). Mixture of dynamic textures applied to temporal analysis of lung lesions. Journal of Computational and Theoretical Nanoscience, 15(6-7):1839–1852.
Rose, P. W. and Watson, E. (2009). What is the value of routine follow-up after diagnosis and treatment of cancer? British Journal of General Practice, 59(564):482–483.
Simon, A. F., Holmes, J. H., and Schwartz, E. S. (2020). Decreasing radiologist burnout through informatics-based solutions. Clinical Imaging, 59(2):167 – 171.
Trujillo-Ortiz, A. (2009). Multiple correspondence analysis based on the burt matrix. MATLAB Central File Exchange.
Wang, Y., Dong, C., Hu, Y., Li, C., Ren, Q., Zhang, X., Shi, H., and Zhou, M. (2020). Temporal changes of ct findings in 90 patients with covid-19 pneumonia: A longitudinal study. Radiology, 0(0):200843. PMID: 32191587.
Publicado
15/09/2020
Como Citar
MORAES, Isabel; BARROS NETO, Stelmo.
Detecção de Mudanças de Densidade de Lesões Pulmonares Através do Método de Análise de Correspondência Múltipla. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 273-284.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11520.
