Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study
Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study
Rationale: since non-invasive tests for prediction of liver fibrosis have a poor diagnostic performance for detecting low levels of fibrosis, it is important to explore the diagnostic capabilities of other non-invasive tests to diagnose low levels of fibrosis. We aimed to evaluate the performance of radiomics based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in predicting any liver fibrosis in individuals with biopsy-proven metabolic dysfunction-associated fatty liver disease (MAFLD).
Methods: a total of 22 adults with biopsy-confirmed MAFLD, who underwent 18F-FDG PET/CT, were enrolled in this study. Sixty radiomics features were extracted from whole liver region of interest in 18F-FDG PET images. Subsequently, the minimum redundancy maximum relevance (mRMR) method was performed and a subset of two features mostly related to the output classes and low redundancy between them were selected according to an event per variable of 5. Logistic regression, Support Vector Machine, Naive Bayes, 5-Nearest Neighbor and linear discriminant analysis models were built based on selected features. The predictive performances were assessed by the receiver operator characteristic (ROC) curve analysis.
Results: the mean (SD) age of the subjects was 38.5 (10.4) years and 17 subjects were men. 12 subjects had histological evidence of any liver fibrosis. The coarseness of neighborhood grey-level difference matrix (NGLDM) and long-run emphasis (LRE) of grey-level run length matrix (GLRLM) were selected to predict fibrosis. The logistic regression model performed best with an AUROC of 0.817 [95% confidence intervals, 0.595-0.947] for prediction of liver fibrosis.
Conclusion: these preliminary data suggest that 18F-FDG PET radiomics may have clinical utility in assessing early liver fibrosis in MAFLD.
F-FDG PET/CT, Fibrosis, Metabolic dysfunction-associated fatty liver disease, Radiomics
3624-3630
Chen, Zhong-Wei
63973770-9e2a-4249-afd2-084ad0da15e8
Tang, Kun
2fe27df4-2f3b-4795-b467-29a2caa1635f
Zhao, You-Fan
85617022-26f7-4178-8c32-ae35cb46d6d9
Chen, Yang-Zong
b506fe03-2aeb-4784-898f-cdba354fb356
Tang, Liang-Jie
1e364be5-1016-470c-8bab-cda8da6bf9e2
Li, Gang
4b9a2554-7810-411b-881b-31798a4680c2
Huang, Ou-Yang
6cda4f11-5826-4bf8-80d5-3818722ddbaa
Wang, Xiao-Dong
4a485ec7-d10f-4beb-aea7-aa907f3904ac
Targher, Giovanni
043e0811-b389-4922-974e-22e650212c5f
Byrne, Christopher
1370b997-cead-4229-83a7-53301ed2a43c
Zheng, Xiang-Wu
29480258-9456-4674-9581-8cf200a72302
Zheng, Ming-Hua
6fb14aaf-c765-4050-a15a-6b4be380257e
7 September 2021
Chen, Zhong-Wei
63973770-9e2a-4249-afd2-084ad0da15e8
Tang, Kun
2fe27df4-2f3b-4795-b467-29a2caa1635f
Zhao, You-Fan
85617022-26f7-4178-8c32-ae35cb46d6d9
Chen, Yang-Zong
b506fe03-2aeb-4784-898f-cdba354fb356
Tang, Liang-Jie
1e364be5-1016-470c-8bab-cda8da6bf9e2
Li, Gang
4b9a2554-7810-411b-881b-31798a4680c2
Huang, Ou-Yang
6cda4f11-5826-4bf8-80d5-3818722ddbaa
Wang, Xiao-Dong
4a485ec7-d10f-4beb-aea7-aa907f3904ac
Targher, Giovanni
043e0811-b389-4922-974e-22e650212c5f
Byrne, Christopher
1370b997-cead-4229-83a7-53301ed2a43c
Zheng, Xiang-Wu
29480258-9456-4674-9581-8cf200a72302
Zheng, Ming-Hua
6fb14aaf-c765-4050-a15a-6b4be380257e
Chen, Zhong-Wei, Tang, Kun, Zhao, You-Fan, Chen, Yang-Zong, Tang, Liang-Jie, Li, Gang, Huang, Ou-Yang, Wang, Xiao-Dong, Targher, Giovanni, Byrne, Christopher, Zheng, Xiang-Wu and Zheng, Ming-Hua
(2021)
Radiomics based on fluoro-deoxyglucose positron emission tomography predicts liver fibrosis in biopsy-proven MAFLD: a pilot study.
International Journal of Medical Sciences, 18 (16), .
(doi:10.7150/ijms.64458).
Abstract
Rationale: since non-invasive tests for prediction of liver fibrosis have a poor diagnostic performance for detecting low levels of fibrosis, it is important to explore the diagnostic capabilities of other non-invasive tests to diagnose low levels of fibrosis. We aimed to evaluate the performance of radiomics based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in predicting any liver fibrosis in individuals with biopsy-proven metabolic dysfunction-associated fatty liver disease (MAFLD).
Methods: a total of 22 adults with biopsy-confirmed MAFLD, who underwent 18F-FDG PET/CT, were enrolled in this study. Sixty radiomics features were extracted from whole liver region of interest in 18F-FDG PET images. Subsequently, the minimum redundancy maximum relevance (mRMR) method was performed and a subset of two features mostly related to the output classes and low redundancy between them were selected according to an event per variable of 5. Logistic regression, Support Vector Machine, Naive Bayes, 5-Nearest Neighbor and linear discriminant analysis models were built based on selected features. The predictive performances were assessed by the receiver operator characteristic (ROC) curve analysis.
Results: the mean (SD) age of the subjects was 38.5 (10.4) years and 17 subjects were men. 12 subjects had histological evidence of any liver fibrosis. The coarseness of neighborhood grey-level difference matrix (NGLDM) and long-run emphasis (LRE) of grey-level run length matrix (GLRLM) were selected to predict fibrosis. The logistic regression model performed best with an AUROC of 0.817 [95% confidence intervals, 0.595-0.947] for prediction of liver fibrosis.
Conclusion: these preliminary data suggest that 18F-FDG PET radiomics may have clinical utility in assessing early liver fibrosis in MAFLD.
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Accepted/In Press date: 30 August 2021
e-pub ahead of print date: 7 September 2021
Published date: 7 September 2021
Keywords:
F-FDG PET/CT, Fibrosis, Metabolic dysfunction-associated fatty liver disease, Radiomics
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Local EPrints ID: 451607
URI: http://eprints.soton.ac.uk/id/eprint/451607
ISSN: 1449-1907
PURE UUID: 52eb6e6f-dbb9-41a3-9eb5-e593bda7e23b
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Date deposited: 14 Oct 2021 16:30
Last modified: 06 Jun 2024 01:38
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Author:
Zhong-Wei Chen
Author:
Kun Tang
Author:
You-Fan Zhao
Author:
Yang-Zong Chen
Author:
Liang-Jie Tang
Author:
Gang Li
Author:
Ou-Yang Huang
Author:
Xiao-Dong Wang
Author:
Giovanni Targher
Author:
Xiang-Wu Zheng
Author:
Ming-Hua Zheng
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