Abstract #3200

Automated differentiation between benign and malignant vertebral compression fracture using a deep convolutional neural network on MRI

Takafumi Yoda¹, Satoshi Maki², Koji Matsumoto¹, Hajime Yokota³, Yoshitada Masuda¹, and Takashi Uno³

¹Department of Radiology, Chiba University Hospital, Chiba, Japan, ²Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan, ³Diagnostic Radiology and Radiation Oncology, Graduate School of Medicine, Chiba University, Chiba, Japan

The Differentiating between osteoporotic vertebral fractures (OVFs) and malignant vertebral compression fractures (MVFs) due to spinal metastasis is a challenging problem for the spine surgeons and the radiologists. We evaluated the performance of our CNN model in differentiating between OVFs and MVFs on short-TI inversion recovery (STIR) and T1-weighted (T1WI) images compared with the performance of three spine surgeons. The sensitivity, specificity, and accuracy of the CNN on both STIR and T1WI were equal to or better than those of the three spine surgeons.

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