@article{TCR23050,
author = {Jie Peng and Xiaolong Qi and Qifan Zhang and Zhijiao Duan and Yikai Xu and Jing Zhang and Yanna Liu and Jie Zhou and Li Liu},
title = {A radiomics nomogram for preoperatively predicting prognosis of patients in hepatocellular carcinoma},
journal = {Translational Cancer Research},
volume = {7},
number = {4},
year = {2018},
keywords = {},
abstract = {Background: Increasing studies have indicated that biomarkers based on quantitative radiomics features are related to clinical prognosis across a range of cancer types, but the association between radiomics and prognosis in hepatocellular carcinoma (HCC) is unclear. We aimed to develop and validate a radiomics nomogram for the preoperative prediction of prognosis for patients with HCC undergoing partial hepatectomy.
Methods: In total, 177 patients were randomly divided into training (n=113) and validation (n=64) cohorts. A total number of 980 radiomics features were extracted from computed tomography images. And the least absolute shrinkage and selection operator algorithm was used to select the optimal features and build a radiomics signature in the training set. Besides, a radiomics nomogram was developed using multivariate regression analysis. The performance of the radiomics nomogram was estimated regarding its discrimination and calibration abilities, and clinical usefulness.
Results: The radiomics signature was significantly associated with disease-free survival (DFS) (P},
issn = {2219-6803}, url = {https://tcr.amegroups.org/article/view/23050}
}