Radiomics Assessment of SPAIR and STIR MRI Sequences to Predict Axial and Peripheral Spondyloarthritis
Resumo
In an attempt to aid the subtyping of spondyloarthritis (SpA), this work assessed neural nets and magnetic resonance imaging (MRI) features to predict SpA. Patients underwent SPAIR and STIR MRI sequences. Radiologists manually segmented sacroiliac joints images for extracting MRI features. A neural net used these features to predict SpA. The STIR-based model yielded higher performance than SPAIR to diagnose SpA, although no statistical difference was found between them. The SPAIR model yielded an area under the curve of 0.83 to differentiate axial and peripheral subtypes, while STIR yielded 0.57 (p < 0.05 on curves difference). Therefore, neural nets modeled with SPAIR-extracted features distinguished SpA using a single MRI exam of the sacroiliac joints.
Referências
Faleiros, M. C., Nogueira-Barbosa, M. H., Dalto, V. F., Ferreira-Junior, J. R., Tenorio, A. P. M., Assad, R. L., Louzada-Junior, P., Rangayyan, R. M., and Azevedo-Marques, P. M. (2020). Machine learning techniques for computer-aided classification of active inflammatory sacroiliitis in magnetic resonance imaging. Advances in Rheumatology, 60:25.
Ferreira Junior, J. R., Santos, M. K., Tenorio, A. P. M., Faleiros, M. C., Cipriano, F. E. G., Fabro, A. T., Näppi, J., Yoshida, H., and Azevedo Marques, P. M. (2020). CTbased radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. International Journal of Computer Assisted Radiology and Surgery, 15:163–172.
Gillies, R., Kinahan, P., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–77.
Lambert, R. G., Bakker, P. A., van der Heijde, D., Weber, U., Rudwaleit, M., Hermann, K.-G. A., Sieper, J., Baraliakos, X., Bennett, A., Braun, J., et al. (2016). Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group. Annals of the Rheumatic Diseases, 75(11):1958–1963.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., and Kim, N. (2017). Deep learning in medical imaging: general overview. Korean Journal of Radiology, 18(4):570–584.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88.
Malaviya, A. N., Rawat, R., Agrawal, N., and Patil, N. S. (2017). The nonradiographic axial spondyloarthritis, the radiographic axial spondyloarthritis, and ankylosing spondylitis: the tangled skein of rheumatology. International Journal of Rheumatology, 2017.
Santos, M. K., Ferreira Junior, J. R., Wada, D. T., Tenorio, A. P. M., Barbosa, M. H. N., and Azevedo Marques, P. M. (2019). Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387–396.
Sieper, J., Rudwaleit, M., Baraliakos, X., Brandt, J., Braun, J., Burgos-Vargas, R., ougados, M., Hermann, K., Landewe, R., Maksymowych,W., et al. (2009). The assessment of spondyloarthritis international society (asas) handbook: a guide to assess spondyloarthritis. Annals of the Rheumatic Diseases, 68(Suppl 2):ii1–ii44.
Tenorio, A. P. M., Faleiros, M. C., Ferreira Junior, J. R., Dalto, V. F., Assad, R. L., Louzada-Junior, P., Yoshida, H., Nogueira-Barbosa, M. H., and Azevedo-Marques, P. M. (2020). A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery, DOI:10.1007/s11548-020-02219-7.
van der Heijde, D., Ramiro, S., Landewe, R., Baraliakos, X., Van den Bosch, F., Sepriano, A., Regel, A., Ciurea, A., Dagfinrud, H., Dougados, M., et al. (2017). 2016 update of the ASAS-EULAR management recommendations for axial spondyloarthritis. Annals of the Rheumatic Diseases, 76(6):978–991.
Ferreira Junior, J. R., Santos, M. K., Tenorio, A. P. M., Faleiros, M. C., Cipriano, F. E. G., Fabro, A. T., Näppi, J., Yoshida, H., and Azevedo Marques, P. M. (2020). CTbased radiomics for prediction of histologic subtype and metastatic disease in primary malignant lung neoplasms. International Journal of Computer Assisted Radiology and Surgery, 15:163–172.
Gillies, R., Kinahan, P., and Hricak, H. (2016). Radiomics: Images are more than pictures, they are data. Radiology, 278(2):563–77.
Lambert, R. G., Bakker, P. A., van der Heijde, D., Weber, U., Rudwaleit, M., Hermann, K.-G. A., Sieper, J., Baraliakos, X., Bennett, A., Braun, J., et al. (2016). Defining active sacroiliitis on MRI for classification of axial spondyloarthritis: update by the ASAS MRI working group. Annals of the Rheumatic Diseases, 75(11):1958–1963.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(7553):436–444
Lee, J., Jun, S., Cho, Y.-W., Lee, H., Kim, G. B., Seo, J. B., and Kim, N. (2017). Deep learning in medical imaging: general overview. Korean Journal of Radiology, 18(4):570–584.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42:60–88.
Malaviya, A. N., Rawat, R., Agrawal, N., and Patil, N. S. (2017). The nonradiographic axial spondyloarthritis, the radiographic axial spondyloarthritis, and ankylosing spondylitis: the tangled skein of rheumatology. International Journal of Rheumatology, 2017.
Santos, M. K., Ferreira Junior, J. R., Wada, D. T., Tenorio, A. P. M., Barbosa, M. H. N., and Azevedo Marques, P. M. (2019). Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine. Radiologia Brasileira, 52(6):387–396.
Sieper, J., Rudwaleit, M., Baraliakos, X., Brandt, J., Braun, J., Burgos-Vargas, R., ougados, M., Hermann, K., Landewe, R., Maksymowych,W., et al. (2009). The assessment of spondyloarthritis international society (asas) handbook: a guide to assess spondyloarthritis. Annals of the Rheumatic Diseases, 68(Suppl 2):ii1–ii44.
Tenorio, A. P. M., Faleiros, M. C., Ferreira Junior, J. R., Dalto, V. F., Assad, R. L., Louzada-Junior, P., Yoshida, H., Nogueira-Barbosa, M. H., and Azevedo-Marques, P. M. (2020). A study of MRI-based radiomics biomarkers for sacroiliitis and spondyloarthritis. International Journal of Computer Assisted Radiology and Surgery, DOI:10.1007/s11548-020-02219-7.
van der Heijde, D., Ramiro, S., Landewe, R., Baraliakos, X., Van den Bosch, F., Sepriano, A., Regel, A., Ciurea, A., Dagfinrud, H., Dougados, M., et al. (2017). 2016 update of the ASAS-EULAR management recommendations for axial spondyloarthritis. Annals of the Rheumatic Diseases, 76(6):978–991.
Publicado
15/09/2020
Como Citar
TENORIO, Ariane; FERREIRA JUNIOR, José; DALTO, Vitor; FALEIROS, Matheus; ASSAD, Rodrigo ; NOGUEIRA-BARBOSA, Marcello; AZEVEDO-MARQUES, Paulo.
Radiomics Assessment of SPAIR and STIR MRI Sequences to Predict Axial and Peripheral Spondyloarthritis. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 20. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 410-415.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2020.11532.
