Open AccessPreoperative contrast-enhanced computed tomography-based radiomics model for overall survival prediction in hepatocellular carcinoma
Author(s) -
Peng-Zhan Deng,
Bigeng Zhao,
Xianhui Huang,
Tingfeng Xu,
Zijun Chen,
Qiufeng Wei,
Xiaoyi Liu,
Yuqi Guo,
Shengguang Yuan,
Weijia Liao
Publication year - 2022
Publication title -
world journal of gastroenterology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.427
H-Index - 155
eISSN - 2219-2840
pISSN - 1007-9327
DOI - 10.3748/wjg.v28.i31.4376
Subject(s) - nomogram , medicine , radiomics , univariate , receiver operating characteristic , hepatocellular carcinoma , proportional hazards model , radiology , oncology , univariate analysis , multivariate statistics , multivariate analysis , machine learning , computer science
Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with a rising incidence worldwide. The prognosis of HCC patients after radical resection remains poor. Radiomics is a novel machine learning method that extracts quantitative features from medical images and provides predictive information of cancer, which can assist with cancer diagnosis, therapeutic decision-making and prognosis improvement.