Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis.
10.1097/CM9.0000000000001113
- VernacularTitle:Development and validation of radiomics model built by incorporating machine learning for identifying liver fibrosis and early-stage cirrhosis
- Author:
Qing-Tao QIU
1
;
Jing ZHANG
2
;
Jing-Hao DUAN
1
;
Shi-Zhang WU
1
;
Jia-Lin DING
3
;
Yong YIN
1
Author Information
1. Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong 250117, China.
2. Department of Radiation Oncology, Cancer Institute and Hospital, Tianjin Medical University, Tianjin 300060, China.
3. School of Physics and Electronics, Shandong Normal University, Jinan, Shandong 250358, China.
- Publication Type:Journal Article
- MeSH:
Diffusion Magnetic Resonance Imaging;
Humans;
Liver Cirrhosis/diagnostic imaging*;
Machine Learning;
ROC Curve;
Retrospective Studies
- From:
Chinese Medical Journal
2020;133(22):2653-2659
- CountryChina
- Language:English
-
Abstract:
BACKGROUND:Liver fibrosis (LF) continues to develop and eventually progresses to cirrhosis. However, LF and early-stage cirrhosis (ESC) can be reversed in some cases, while advanced cirrhosis is almost impossible to cure. Advances in quantitative imaging techniques have made it possible to replace the gold standard biopsy method with non-invasive imaging, such as radiomics. Therefore, the purpose of this study is to develop a radiomics model to identify LF and ESC.
METHODS:Patients with LF (n = 108) and ESC (n = 116) were enrolled in this study. As a control, patients with healthy livers were involved in the study (n = 145). Diffusion-weighted imaging (DWI) data sets with three b-values (0, 400, and 800 s/mm) of enrolled cases were collected in this study. Then, radiomics features were extracted from manually delineated volumes of interest. Two modeling strategies were performed after univariate analysis and feature selection. Finally, an optimal model was determined by the receiver operating characteristic area under the curve (AUC).
RESULTS:The optimal models were built in plan 1. For model 1 in plan 1, the AUCs of the training and validation cohorts were 0.973 (95% confidence interval [CI] 0.946-1.000) and 0.948 (95% CI 0.903-0.993), respectively. For model 2 in plan 1, the AUCs of the training and validation cohorts were 0.944, 95% CI 0.905 to 0.983, and 0.968, 95% CI 0.940 to 0.996, respectively.
CONCLUSIONS:Radiomics analysis of DWI images allows for accurate identification of LF and ESC, and the non-invasive biomarkers extracted from the functional DWI images can serve as a better alternative to biopsy.
