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Abstract #3995

Semi-Automated Assessment for Distinguishing Glioblastoma and Solitary Brain Metastasis: A Machine Learning Approach

Nathaniel Swinburne1, Javin Schefflein1, Yu Sakai2, Iris Chen2, Ehsan Tadayon2, and Kambiz Nael1

1Department of Radiology, Mount Sinai Medical Center, New York, NY, United States, 2Mount Sinai Medical Center, New York, NY, United States

Although machine learning applications for non-medical imaging are well-established, its use in radiologic imaging interpretation remains nascent. We trained a support vector machine using advanced MR imaging to differentiate glioblastoma and brain metastasis with 72.6% balanced accuracy. The ability for machine learning to aid radiologists in differentiating pathologies with similar appearance on conventional imaging appears promising.

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