We trained a Random Forest classification model to classify amyotrophic lateral sclerosis (ALS) patients from those with mimicking clinical presentations based on QSM radiomic features extracted from the primary motor cortex. In a validation set, the model has 0.8 accuracy, 0.75 specificity of 0.75 and 0.84 sensitivity, which is superior to models using the mean QSM value as cutoff with 0.59 accuracy, 0.94 specificity and 0.14 sensitivity.
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