A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data.
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ABSTRACT: Background
To establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT).Methods
This retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly reproducible features. Support vector machine (SVM) was adopted to build a radiomics signature. Multivariate logistic regression analysis was used to establish a radiomics-based nomogram that integrated radiomics signature and other independent clinical predictors. Performance of models was evaluated through discrimination ability, calibration and clinical benefits. An internal validation was conducted in 150 consecutive patients.Results
The radiomics signature comprised 25 cirrhosis-related features and showed significant differences between cirrhosis and non-cirrhosis cohorts (P?ConclusionsOur developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBV-infected patients, that may help clinical decision-making.
SUBMITTER: Wang JC
PROVIDER: S-EPMC7499912 | biostudies-literature | 2020
REPOSITORIES: biostudies-literature
Publications
A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data.
Wang Jin-Cheng JC Fu Rao R Tao Xue-Wen XW Mao Ying-Fan YF Wang Fei F Zhang Ze-Chuan ZC Yu Wei-Wei WW Chen Jun J He Jian J Sun Bei-Cheng BC
Biomarker research 20200917
<h4>Background</h4>To establish and validate a radiomics-based model for predicting liver cirrhosis in patients with hepatitis B virus (HBV) by using non-contrast computed tomography (CT).<h4>Methods</h4>This retrospective study developed a radiomics-based model in a training cohort of 144 HBV-infected patients. Radiomic features were extracted from abdominal non-contrast CT scans. Features selection was performed with the least absolute shrinkage and operator (LASSO) method based on highly repr ...[more]
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