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      Radiomics analysis of contrast-enhanced CT for staging liver fibrosis: an update for image biomarker

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          Abstract

          Background

          To establish and validate a radiomics-based model for staging liver fibrosis at contrast-enhanced CT images.

          Materials and methods

          This retrospective study developed two radiomics-based models (R-score: radiomics signature; R-fibrosis: integrate radiomic and serum variables) in a training cohort of 332 patients (median age, 59 years; interquartile range, 51–67 years; 256 men) with biopsy-proven liver fibrosis who underwent contrast-enhanced CT between January 2017 and December 2020. Radiomic features were extracted from non-contrast, arterial and portal phase CT images and selected using the least absolute shrinkage and selection operator (LASSO) logistic regression to differentiate stage F3–F4 from stage F0–F2. Optimal cutoffs to diagnose significant fibrosis (stage F2–F4), advanced fibrosis (stage F3–F4) and cirrhosis (stage F4) were determined by receiver operating characteristic curve analysis. Diagnostic performance was evaluated by area under the curve, Obuchowski index, calibrations and decision curve analysis. An internal validation was conducted in 111 randomly assigned patients (median age, 58 years; interquartile range, 49–66 years; 89 men).

          Results

          In the validation cohort, R-score and R-fibrosis (Obuchowski index, 0.843 and 0.846, respectively) significantly outperformed aspartate transaminase-to-platelet ratio (APRI) (Obuchowski index, 0.651; p < .001) and fibrosis-4 index (FIB-4) (Obuchowski index, 0.676; p < .001) for staging liver fibrosis. Using the cutoffs, R-fibrosis and R-score had a sensitivity range of 70–87%, specificity range of 71–97%, and accuracy range of 82–86% in diagnosing significant fibrosis, advanced fibrosis and cirrhosis.

          Conclusion

          Radiomic analysis of contrast-enhanced CT images can reach great diagnostic performance of liver fibrosis.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s12072-022-10326-7.

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          Most cited references44

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          Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach

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            Computational Radiomics System to Decode the Radiographic Phenotype

            Radiomics aims to quantify phenotypic characteristics on medical imaging through the use of automated algorithms. Radiomic artificial intelligence (AI) technology, either based on engineered hard-coded algorithms or deep learning methods, can be used to develop non-invasive imaging-based biomarkers. However, lack of standardized algorithm definitions and image processing severely hampers reproducibility and comparability of results. To address this issue, we developed PyRadiomics , a flexible open-source platform capable of extracting a large panel of engineered features from medical images. PyRadiomics is implemented in Python and can be used standalone or using 3D-Slicer. Here, we discuss the workflow and architecture of PyRadiomics and demonstrate its application in characterizing lung-lesions. Source code, documentation, and examples are publicly available at www.radiomics.io . With this platform, we aim to establish a reference standard for radiomic analyses, provide a tested and maintained resource, and to grow the community of radiomic developers addressing critical needs in cancer research.
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              Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases

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                Author and article information

                Contributors
                hjxueren@163.com
                jyinjyin@sina.com
                Journal
                Hepatol Int
                Hepatol Int
                Hepatology International
                Springer India (New Delhi )
                1936-0533
                1936-0541
                28 March 2022
                28 March 2022
                June 2022
                : 16
                : 3
                : 627-639
                Affiliations
                [1 ]GRID grid.428392.6, ISNI 0000 0004 1800 1685, Department of Hepatobiliary Surgery, , Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, ; 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
                [2 ]GRID grid.428392.6, ISNI 0000 0004 1800 1685, Department of Hepatobiliary Surgery, , Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, ; Nanjing, China
                [3 ]GRID grid.428392.6, ISNI 0000 0004 1800 1685, Department of Nuclear Medicine, , Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, ; 321 Zhongshan Road, Nanjing, 210008 Jiangsu Province China
                [4 ]GRID grid.428392.6, ISNI 0000 0004 1800 1685, Department of Pathology, , Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, ; Nanjing, China
                [5 ]Preparatory School for Chinese Students To Japan, The Training Center of Ministry of Education for Studying Overseas, Changchun, China
                Author information
                http://orcid.org/0000-0001-9345-2388
                Article
                10326
                10.1007/s12072-022-10326-7
                9174317
                35347597
                ec5dd448-99a1-4fba-b77a-a9b8fdaf9945
                © The Author(s) 2022

                Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 1 November 2021
                : 3 March 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100014717, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China;
                Award ID: 81902415
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100010035, Outstanding Youth Foundation of Jiangsu Province of China;
                Award ID: BK20190116
                Award Recipient :
                Funded by: Key Laboratory of Imaging Diagnosis and Minimally Invasive Interventional Research of Zhejiang Province
                Award ID: YXJR202002
                Award Recipient :
                Categories
                Original Article
                Custom metadata
                © Asian Pacific Association for the Study of the Liver 2022

                Gastroenterology & Hepatology
                radiomics,contrast-enhanced ct,liver fibrosis,prediction model,cirrhosis,noninvasive,machine learning,obuchowski index,calibration,decision curve analysis

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