Abstract #1647

Prediction of Histological Subtypes Using Machine-Learning Model Based on UTE-MRI in Non-Small Cell Lung Cancer：A comparative study with CT

Pengyang Feng¹, Nan Meng², Xuan Yu², Yaping Wu², Fangfang Fu², Yu Luo², Han Jiang³, Ziqiang Li³, Jianmin Yuan⁴, Yang Yang⁵, Zhe Wang⁴, and Meiyun Wang*²

¹Department of Medical Imaging, Henan University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China, ²Department of Medical Imaging, Zhengzhou University People’s Hospital & Henan Provincial People’s Hospital, Zhengzhou, China, ³Department of Medical Imaging, Xinxiang Medical University Henan Provincial People’s Hospital, Zhengzhou, China, ⁴Central Research Institute, UIH Group, Shanghai, China, ⁵Beijing United Imaging Research Institute of Intelligent Imaging, UIH Group, Beijing, China

Synopsis

Keywords: Cancer, RadiomicsThree-dimensional ultrashort echo time (3D-UTE) is a novel MRI technique, which yields similar diagnostic results as conventional pulmonary computed tomography (CT). Our results showed that the predication model based on clinical factors and 3D-UTE radiomics features could noninvasively assess the subtype of in non-small cell lung cancer. Compared with the CT model, it has similar diagnostic efficiency but less radiation, which is expected to provide new ideas for related research.

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