Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder.
Eur Radiol
; 29(9): 4670-4677, 2019 Sep.
Article
en En
| MEDLINE
| ID: mdl-30770971
OBJECTIVE: To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). METHODS: We retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts. RESULTS: Nine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort). CONCLUSIONS: A validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD. KEY POINTS: ⢠Radiomic features of spinal cord lesions in MS and NMOSD were different. ⢠Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Médula Espinal
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Imagen por Resonancia Magnética
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Neuromielitis Óptica
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Esclerosis Múltiple
Tipo de estudio:
Diagnostic_studies
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Etiology_studies
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Incidence_studies
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Observational_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Año:
2019
Tipo del documento:
Article