A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data
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RESEARCH
Open Access
A radiomics-based model on non-contrast CT for predicting cirrhosis: make the most of image data Jin-Cheng Wang1,2† , Rao Fu1,2†, Xue-Wen Tao1,2†, Ying-Fan Mao3, Fei Wang1,2, Ze-Chuan Zhang1,2, Wei-Wei Yu1, Jun Chen4*, Jian He3* and Bei-Cheng Sun1,2*
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 < 0.001). A radiomics-based nomogram that integrates radiomics signature, alanine transaminase, aspartate aminotransferase, globulin and international normalized ratio showed great calibration and discrimination ability in the training cohort (area under the curve [AUC]: 0.915) and the validation cohort (AUC: 0.872). Decision curve analysis confirmed the most clinical benefits can be provided by the nomogram compared with other methods. Conclusions: Our developed radiomics-based nomogram can successfully diagnose the status of cirrhosis in HBVinfected patients, that may help clinical decision-making. Keywords: Hepatitis B virus (HBV), Liver cirrhosis, Non-contrast computed tomography (CT), Radiomics model
* Correspondence: [email protected]; [email protected]; [email protected] † Jin-Cheng Wang, Rao Fu and Xue-Wen Tao share co-first authorship. 4 Department of Pathology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China 3 Department of Radiology, The Affiliated Drum Tower Hospital of Nanjing University Medical School, 321 Zhongshan Road, Nanjing 210008, Jiangsu Province, China 1 Department of Hepatobiliary Surgery of Drum Tower Clinical Medical College, Nanjing Medical University, Nanjing, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licenc
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