Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Deep Segmentation Feature-Based Radiomics Improves Recurrence Prediction of Hepatocellular Carcinoma

Objective and Impact Statement. This study developed and validated a deep semantic segmentation feature-based radiomics (DSFR) model based on preoperative contrast-enhanced computed tomography (CECT) combined with clinical information to predict early recurrence (ER) of single hepatocellular carcino...

Full description

Bibliographic Details
Main Authors: Jifei Wang, Dasheng Wu, Meili Sun, Zhenpeng Peng, Yingyu Lin, Hongxin Lin, Jiazhao Chen, Tingyu Long, Zi-Ping Li, Chuanmiao Xie, Bingsheng Huang, Shi-Ting Feng
Format: Article
Language:English
Published: American Association for the Advancement of Science (AAAS) 2022-01-01
Series:BME Frontiers
Online Access:http://dx.doi.org/10.34133/2022/9793716

Internet

http://dx.doi.org/10.34133/2022/9793716

Similar Items