Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects
Background: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can ev...
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| Format: | Article |
| Language: | English |
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Elsevier
2021-07-01
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| Series: | EBioMedicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396421002644 |
| _version_ | 1818723680816988160 |
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| author | Juan Shi Guoqing Bao Jie Hong Simin Wang Yufei Chen Shaoqian Zhao Aibo Gao Ru Zhang Jingfen Hu Wenjie Yang Fuhua Yan Ankang Lyu Ruixin Liu Bin Cui Yuhong Chen Jiabin Jin Baiyong Shen Yifei Zhang Weiqiong Gu Dagan Feng Weiqing Wang Jiqiu Wang Xiuying Wang Guang Ning |
| author_facet | Juan Shi Guoqing Bao Jie Hong Simin Wang Yufei Chen Shaoqian Zhao Aibo Gao Ru Zhang Jingfen Hu Wenjie Yang Fuhua Yan Ankang Lyu Ruixin Liu Bin Cui Yuhong Chen Jiabin Jin Baiyong Shen Yifei Zhang Weiqiong Gu Dagan Feng Weiqing Wang Jiqiu Wang Xiuying Wang Guang Ning |
| author_sort | Juan Shi |
| collection | DOAJ |
| description | Background: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. Methods: 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. Findings: Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). Interpretation: This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. Funding: The complete list of funders can be found in the Acknowledgement section. |
| first_indexed | 2024-12-17T21:14:23Z |
| format | Article |
| id | doaj.art-cd7143805b874a15b01eff978cb4b16f |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-12-17T21:14:23Z |
| publishDate | 2021-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-cd7143805b874a15b01eff978cb4b16f2022-12-21T21:32:22ZengElsevierEBioMedicine2352-39642021-07-0169103471Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effectsJuan Shi0Guoqing Bao1Jie Hong2Simin Wang3Yufei Chen4Shaoqian Zhao5Aibo Gao6Ru Zhang7Jingfen Hu8Wenjie Yang9Fuhua Yan10Ankang Lyu11Ruixin Liu12Bin Cui13Yuhong Chen14Jiabin Jin15Baiyong Shen16Yifei Zhang17Weiqiong Gu18Dagan Feng19Weiqing Wang20Jiqiu Wang21Xiuying Wang22Guang Ning23Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSchool of Computer Science, The University of Sydney, Sydney, NSW, 2006, AustraliaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSAIC Volkswagen Automotive Company Limited, Shanghai, ChinaSAIC Volkswagen Automotive Company Limited, Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaHealth Examination Centre, Department of Cardiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of General Surgery, Pancreatic Disease Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Research Institute of Pancreatic Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaSJTU-USYD Joint Research Alliance for Translational Medicine, Shanghai Jiao Tong University, Shanghai, China; Biomedical and Multimedia Information Technology Research Group, School of Computer Science, The University of Sydney, Sydney, NSW 2006, AustraliaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authors.School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia; Corresponding authors.Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Corresponding authors.Background: Metabolic syndrome (MetS) is highly related to the excessive accumulation of visceral adipose tissue (VAT). Quantitative measurements of VAT are commonly applied in clinical practice for measurement of metabolic risks; however, it remains largely unknown whether the texture of VAT can evaluate visceral adiposity, stratify MetS and predict surgery-induced weight loss effects. Methods: 675 Chinese adult volunteers and 63 obese patients (with bariatric surgery) were enrolled. Texture features were extracted from VATs of the computed tomography (CT) scans and machine learning was applied to identify significant imaging biomarkers associated with metabolic-related traits. Findings: Combined with sex, ten VAT texture features achieved areas under the curve (AUCs) of 0.872, 0.888, 0.961, and 0.947 for predicting the prevalence of insulin resistance, MetS, central obesity, and visceral obesity, respectively. A novel imaging biomarker, RunEntropy, was identified to be significantly associated with major metabolic outcomes and a 3.5-year follow-up in 338 volunteers demonstrated its long-term effectiveness. More importantly, the preoperative imaging biomarkers yielded high AUCs and accuracies for estimation of surgery responses, including the percentage of excess weight loss (%EWL) (0.867 and 74.6%), postoperative BMI group (0.930 and 76.1%), postoperative insulin resistance (0.947 and 88.9%), and excess visceral fat loss (the proportion of visceral fat reduced over 50%; 0.928 and 84.1%). Interpretation: This study shows that the texture features of VAT have significant clinical implications in evaluating metabolic disorders and predicting surgery-induced weight loss effects. Funding: The complete list of funders can be found in the Acknowledgement section.http://www.sciencedirect.com/science/article/pii/S2352396421002644Visceral fatTexture featureBariatric surgeryMetabolic syndromeVisceral obesityImaging biomarkers |
| spellingShingle | Juan Shi Guoqing Bao Jie Hong Simin Wang Yufei Chen Shaoqian Zhao Aibo Gao Ru Zhang Jingfen Hu Wenjie Yang Fuhua Yan Ankang Lyu Ruixin Liu Bin Cui Yuhong Chen Jiabin Jin Baiyong Shen Yifei Zhang Weiqiong Gu Dagan Feng Weiqing Wang Jiqiu Wang Xiuying Wang Guang Ning Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects EBioMedicine Visceral fat Texture feature Bariatric surgery Metabolic syndrome Visceral obesity Imaging biomarkers |
| title | Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects |
| title_full | Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects |
| title_fullStr | Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects |
| title_full_unstemmed | Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects |
| title_short | Deciphering CT texture features of human visceral fat to evaluate metabolic disorders and surgery-induced weight loss effects |
| title_sort | deciphering ct texture features of human visceral fat to evaluate metabolic disorders and surgery induced weight loss effects |
| topic | Visceral fat Texture feature Bariatric surgery Metabolic syndrome Visceral obesity Imaging biomarkers |
| url | http://www.sciencedirect.com/science/article/pii/S2352396421002644 |
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