Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images | Zendy

Yunyun Dong | Zendy; Lina Hou | Zendy; Wenkai Yang | Zendy; Jiahao Han | Zendy; Jiawen Wang | Zendy; Yan Qiang | Zendy; Juanjuan Zhao | Zendy; Jiaxin Hou | Zendy; Kai Song | Zendy; Yulan Ma | Zendy; Ntikurako Guy Fernand Kazihise | Zendy; Yanfen Cui | Zendy; Xiaotang Yang | Zendy

Open Access

Multi-channel multi-task deep learning for predicting EGFR and KRAS mutations of non-small cell lung cancer on CT images

Author(s) -

Yunyun Dong,

Lina Hou,

Wenkai Yang,

Jiahao Han,

Jiawen Wang,

Yan Qiang,

Juanjuan Zhao,

Jiaxin Hou,

Kai Song,

Yulan Ma,

Ntikurako Guy Fernand Kazihise,

Yanfen Cui,

Xiaotang Yang

Publication year - 2021

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims-20-600

Subject(s) - kras , deep learning , artificial intelligence , computer science , machine learning , lung cancer , epidermal growth factor receptor , task (project management) , mutation , nodule (geology) , gene mutation , cancer , computational biology , medicine , gene , oncology , biology , colorectal cancer , paleontology , biochemistry , management , economics

Predicting the mutation statuses of 2 essential pathogenic genes [epidermal growth factor receptor ( EGFR ) and Kirsten rat sarcoma ( KRAS )] in non-small cell lung cancer (NSCLC) based on CT is valuable for targeted therapy because it is a non-invasive and less costly method. Although deep learning technology has realized substantial computer vision achievements, CT imaging being used to predict gene mutations remains challenging due to small dataset limitations.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research