The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients

This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information for predicting anaplastic lymphoma kinase (ALK) fusion status in non-small cell lung cancer (NSCLC) patients.Our findings showed that the DLM trained by both CT images and clinicopathological information could effectively predict the ALK fusion status and treatment responses of patients. For the small size of the ALK-target therapy cohort, larger data sets would be collected to further validate the performance of the model for predicting the response to ALK-TKI treatment. READ ARTICLE

European Journal of Nuclear Medicine and Molecular Imaging DOI:10.1007/s00259-020-04986-6

Authors: Zhengbo Song, Tianchi Liu, Lei Shi, Zongyang Yu, Qing Shen, Mengdi Xu, Zhangzhou Huang, Zhijian Cai, Wenxian Wang, Chunwei Xu, Jingjing Sun, Ming Chen