Open AccessRadiomics Assessment of SPAIR and STIR MRI Sequences to Predict Axial and Peripheral Spondyloarthritis
Author(s) -
Ariane Priscilla Magalhães Tenório,
José Ferreira,
Vitor Dalto,
Matheus Carvalho Faleiros,
Rodrigo Luppino Assad,
Marcello Henrique Nogueira-Barbosa,
Paulo Mazzoncini de Azevedo–Marques
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbcas.2020.11532
Subject(s) - magnetic resonance imaging , computer science , radiology , medicine
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.