Abstract #3998

Radiomic Features on Quantitative Susceptibility Mapping Classify Amyotrophic Lateral Sclerosis Patients from Mimics

Anja Samardzija¹, Thanh Nguyen², Elizabeth Sweeney², Kailyn Lee², Ilhami Kovanlikaya², Yi Wang ², Andrew Schweitzer², and Apostolos Tsiouris²

¹Electrical and Computer Engineering, Cornell University, Highlands, NJ, United States, ²Weill Cornell Medicine, New York City, NY, United States

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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