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Abstract #3444

Interpreting a machine learning model: radiomics in cervical spondylotic myelopathy postoperative recovery prediction

Mengze Zhang1, Hanqiang Ouyang1, Dan Jing1, Jiangfang Liu1, Chunjie Wang1, Huishu Yuan1, and Liang Jiang1
1Peking University Third Hospital, Beijing, China

Previous studies have confirmed that conventional MRI parameters lack stability, and the evaluation of the prognosis of CSM sometimes is controversial. In our study, we first introduced radiomics, a quantitative analysis of image features, into the study of CSM and obtained a reliable and stable model. By analysis features' importance and unboxing the extremely randomized trees model, we came up with assumptions of the relationship between specific features and post-surgical recovery prediction.

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