Resum
BACKGROUND: The combination of anatomical MRI and deep learning-based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking.
PURPOSE: To compare whole-brain input sampling strategies and regional/specific-tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level.
STUDY TYPE: Retrospective.
SUBJECTS: Three hundred nineteen MS patients (382 brain MRI scans) with clinical assessment of disability level performed within the following 6 months (~70% training/~15% validation/~15% inference in-house dataset) and 440 MS patients from multiple centers (independent external validation cohort).
FIELD STRENGTH/SEQUENCE: Single vendor 1.5 T or 3.0 T. Magnetization-Prepared Rapid Gradient-Echo and Fluid-Attenuated Inversion Recovery sequences.
ASSESSMENT: A 7-fold patient cross validation strategy was used to train a 3D-CNN to classify patients into two groups, Expanded Disability Status Scale score (EDSS) ≥ 3.0 or EDSS < 3.0. Two strategies were investigated: 1) a global approach, taking the whole brain volume as input and 2) regional approaches using five different regions-of-interest: white matter, gray matter, subcortical gray matter, ventricles, and brainstem structures. The performance of the models was assessed in the in-house and the independent external cohorts.
STATISTICAL TESTS: Balanced accuracy, sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC).
RESULTS: With the in-house dataset, the gray matter regional model showed the highest stratification accuracy (81%), followed by the global approach (79%). In the external dataset, without any further retraining, an accuracy of 72% was achieved for the white matter model and 71% for the global approach.
DATA CONCLUSION: The global approach offered the best trade-off between internal performance and external validation to stratify MS patients based on accumulated disability.
EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.
PURPOSE: To compare whole-brain input sampling strategies and regional/specific-tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level.
STUDY TYPE: Retrospective.
SUBJECTS: Three hundred nineteen MS patients (382 brain MRI scans) with clinical assessment of disability level performed within the following 6 months (~70% training/~15% validation/~15% inference in-house dataset) and 440 MS patients from multiple centers (independent external validation cohort).
FIELD STRENGTH/SEQUENCE: Single vendor 1.5 T or 3.0 T. Magnetization-Prepared Rapid Gradient-Echo and Fluid-Attenuated Inversion Recovery sequences.
ASSESSMENT: A 7-fold patient cross validation strategy was used to train a 3D-CNN to classify patients into two groups, Expanded Disability Status Scale score (EDSS) ≥ 3.0 or EDSS < 3.0. Two strategies were investigated: 1) a global approach, taking the whole brain volume as input and 2) regional approaches using five different regions-of-interest: white matter, gray matter, subcortical gray matter, ventricles, and brainstem structures. The performance of the models was assessed in the in-house and the independent external cohorts.
STATISTICAL TESTS: Balanced accuracy, sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC).
RESULTS: With the in-house dataset, the gray matter regional model showed the highest stratification accuracy (81%), followed by the global approach (79%). In the external dataset, without any further retraining, an accuracy of 72% was achieved for the white matter model and 71% for the global approach.
DATA CONCLUSION: The global approach offered the best trade-off between internal performance and external validation to stratify MS patients based on accumulated disability.
EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.
| Idioma original | Anglès |
|---|---|
| Nombre de pàgines | 10 |
| Revista | Journal of Magnetic Resonance Imaging |
| Data online anticipada | 6 d’oct. 2023 |
| DOIs | |
| Estat de la publicació | Publicada - 6 d’oct. 2023 |
Paraules clau
- classification
- deep learning
- input sampling
- multiple sclerosis
- structural MRI