| Autor: |
van der Weijden CWJ; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.; Department of Radiology, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., Pitombeira MS; Laboratory of Nuclear Medicine, Department of Radiology and Oncology, University of Sao Paulo, São Paulo 05508-220, Brazil., Peretti DE; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., Campanholo KR; Laboratory of Nuclear Medicine, Department of Radiology and Oncology, University of Sao Paulo, São Paulo 05508-220, Brazil., Kolinger GD; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., Rimkus CM; Laboratory of Nuclear Medicine, Department of Radiology and Oncology, University of Sao Paulo, São Paulo 05508-220, Brazil., Buchpiguel CA; Laboratory of Nuclear Medicine, Department of Radiology and Oncology, University of Sao Paulo, São Paulo 05508-220, Brazil., Dierckx RAJO; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., Renken RJ; Department of Neuroscience, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., Meilof JF; Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands.; Department of Neurology, Martini Ziekenhuis, 9728 NT Groningen, The Netherlands., de Vries EFJ; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Hanzeplein 1, 9700 RB Groningen, The Netherlands., de Paula Faria D; Laboratory of Nuclear Medicine, Department of Radiology and Oncology, University of Sao Paulo, São Paulo 05508-220, Brazil. |
| Abstrakt: |
Background : Multiple sclerosis (MS) has two main phenotypes: relapse-remitting MS (RRMS) and progressive MS (PMS), distinguished by disability profiles and treatment response. Differentiating them using conventional MRI is challenging. Objective : This study explores the use of scaled subprofile modelling using principal component analysis (SSM/PCA) on MRI data to distinguish between MS phenotypes. Methods : MRI scans were performed on patients with RRMS (n = 30) and patients with PMS (n = 20), using the standard sequences T 1 w, T 2 w, T 2 w-FLAIR, and the myelin-sensitive sequences magnetisation transfer (MT) ratio (MTR), quantitative MT (qMT), inhomogeneous MT ratio (ihMTR), and quantitative inhomogeneous MT (qihMT). Results : SSM/PCA analysis of qihMT images best differentiated PMS from RRMS, with the highest specificity (87%) and positive predictive value (PPV) (83%), but a lower sensitivity (67%) and negative predictive value (NPV) (72%). Conversely, T 1 w data analysis showed the highest sensitivity (93%) and NPV (89%), with a lower PPV (67%) and specificity (53%). Phenotype classification agreement between T 1 w and qihMT was observed in 57% of patients. In the subset with concordant classifications, the sensitivity, specificity, PPV, and NPV were 100%, 88%, 90%, and 100%, respectively. Conclusions : SSM/PCA on MRI data revealed distinctive patterns for MS phenotypes. Optimal discrimination occurred with qihMT and T 1 w sequences, with qihMT identifying PMS and T 1 w identifying RRMS. When qihMT and T 1 w analyses align, MS phenotype prediction improves. |
