Radiomics Assessment of SPAIR and STIR MRI Sequences to Predict Axial and Peripheral Spondyloarthritis

  • Ariane Tenorio FMRP/USP
  • José Ferreira Junior FMRP/USP
  • Vitor Dalto FMRP/USP
  • Matheus Faleiros FMRP/USP
  • Rodrigo Assad FMRP/USP
  • Marcello Nogueira-Barbosa FMRP/USP
  • Paulo Azevedo-Marques FMRP/USP

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

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Publicado
15/09/2020
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.