Novel deep learning radiomics model for preoperative evaluation of hepatocellular carcinoma differentiation based on computed tomography data.
- Resource Type
- Article
- Authors
- Ding, Yong; Ruan, Shijian; Wang, Yubizhuo; Shao, Jiayuan; Sun, Rui; Tian, Wuwei; Xiang, Nan; Ge, Weigang; Zhang, Xiuming; Su, Kunkai; Xia, Jingwen; Huang, Qiang; Liu, Weihai; Sun, Qinxue; Dong, Haibo; Farias, Mylène C. Q.; Guo, Tiannan; Krylov, Andrey S.; Liang, Wenjie; Xiao, Wenbo
- Source
- Clinical & Translational Medicine. Nov2021, Vol. 11 Issue 11, p1-8. 8p.
- Subject
- *DEEP learning
*COMPUTED tomography
*RADIOMICS
*ALPHA fetoproteins
*HEPATOCELLULAR carcinoma
*FEATURE extraction
- Language
- ISSN
- 2001-1326
The evaluation of tumor differentiation is an urgent clinical issue that would facilitate the establishment of individualized therapeutic strategies.1-3 Our team developed a deep learning radiomics model based on computed tomography (CT) data for preoperative evaluation of hepatocellular carcinoma (HCC) differentiation (low vs. high grade) and preliminarily explored the biological basis of the radiomics model. Data from Institution 1 were divided into training and internal validation cohorts by stratified sampling at a 3:1 ratio, while data from Institution 2 constituted the independent test cohort (Figure S1). [Extracted from the article]