Abstract #2117

Grading brain astrocytoma using convolutional neural network: contrast-enhanced T1 and susceptibility-weighted imaging

Zong-Ze Chen¹, Yong-Han Lai¹, Ching-An Liao¹, Teng-Yi Huang², Ping-Hong Lai^1,3,4, and Tzu-Chao Chuang¹

¹National Sun Yat-Sen University, Kaohsiung, Taiwan, ²National Taiwan University of Science and Technology, Taipei, Taiwan, ³Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ⁴National Yang-Ming University, Taipei, Taiwan

Synopsis

Keywords: Tumors, Susceptibility

Susceptibility-weighted imaging (SWI) has shown its potential to discriminate between high-grade and low-grade astrocytoma. In this study, we developed a fully automatic diagnosis system for astrocytoma grading by using convolutional neural network with contrast-enhanced T1-weighted images and SWI, separately or jointly, as input data. The results show that the model with both imaging modalities as input data provides high accuracy in astrocytoma grading and is potentially helpful for clinical diagnosis.

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