Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis
Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis
Background/purpose of the study
There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH.
Methods
Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module.
Results
Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70–0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48–0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51–0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups.
Conclusions
Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers (http://www.pan-chess.cn/calculator/RAMAN_score).
NAFLD (non-alcoholic fatty liver disease), NASH (non-alcoholic steatohepatitis), Raman, SERS (surface-enhanced Raman spectroscopy)
339-349
Gao, Feng
b70fc7ee-1c00-4b32-aa1a-272e603a3add
Lu, De-Chan
5d81e1ac-44ac-4c52-afcc-a48b2f0b16d8
Zheng, Tian-Lei
f7ddfbfb-8131-4998-9827-f4e2997eff39
Geng, Shi
becd475a-ad0c-40bf-87f5-d6c32578e69d
Sha, Jun-Cheng
817c7317-422b-49dd-a431-611cd233e820
Huang, Ou-Yang
097128dc-8f93-4b3c-9c60-d76751e1e858
Tang, Liang-Jie
36c15fd5-08e4-4bcc-85e7-a2d76cbd542f
Zhu, Pei-Wu
e711e0f1-d164-4879-a85e-6ca71543236d
Li, Yang-Yang
87dcf3ab-090b-48e7-aa15-20eb5c950a9a
Chen, Li-Li
92013496-00f2-4a14-9777-cf89d37b01e6
Targher, Giovanni
170f2682-8811-416c-ad2a-c7fd0edbfbec
Byrne, Christopher
1370b997-cead-4229-83a7-53301ed2a43c
Huang, Zu-Fang
9118b228-9524-4e04-b410-0b00210b2b00
Zheng, Ming-Hua
e4e24459-832c-465c-8f29-d1fbabb2dc3c
1 April 2023
Gao, Feng
b70fc7ee-1c00-4b32-aa1a-272e603a3add
Lu, De-Chan
5d81e1ac-44ac-4c52-afcc-a48b2f0b16d8
Zheng, Tian-Lei
f7ddfbfb-8131-4998-9827-f4e2997eff39
Geng, Shi
becd475a-ad0c-40bf-87f5-d6c32578e69d
Sha, Jun-Cheng
817c7317-422b-49dd-a431-611cd233e820
Huang, Ou-Yang
097128dc-8f93-4b3c-9c60-d76751e1e858
Tang, Liang-Jie
36c15fd5-08e4-4bcc-85e7-a2d76cbd542f
Zhu, Pei-Wu
e711e0f1-d164-4879-a85e-6ca71543236d
Li, Yang-Yang
87dcf3ab-090b-48e7-aa15-20eb5c950a9a
Chen, Li-Li
92013496-00f2-4a14-9777-cf89d37b01e6
Targher, Giovanni
170f2682-8811-416c-ad2a-c7fd0edbfbec
Byrne, Christopher
1370b997-cead-4229-83a7-53301ed2a43c
Huang, Zu-Fang
9118b228-9524-4e04-b410-0b00210b2b00
Zheng, Ming-Hua
e4e24459-832c-465c-8f29-d1fbabb2dc3c
Gao, Feng, Lu, De-Chan, Zheng, Tian-Lei, Geng, Shi, Sha, Jun-Cheng, Huang, Ou-Yang, Tang, Liang-Jie, Zhu, Pei-Wu, Li, Yang-Yang, Chen, Li-Li, Targher, Giovanni, Byrne, Christopher, Huang, Zu-Fang and Zheng, Ming-Hua
,
CHESS-MAFLD consortium
(2023)
Fully connected neural network-based serum surface-enhanced Raman spectroscopy accurately identifies non-alcoholic steatohepatitis.
Hepatology International, 17 (2), .
(doi:10.1007/s12072-022-10444-2).
Abstract
Background/purpose of the study
There is a need to find a standardized and low-risk diagnostic tool that can non-invasively detect non-alcoholic steatohepatitis (NASH). Surface enhanced Raman spectroscopy (SERS), which is a technique combining Raman spectroscopy (RS) with nanotechnology, has recently received considerable attention due to its potential for improving medical diagnostics. We aimed to investigate combining SERS and neural network approaches, using a liver biopsy dataset to develop and validate a new diagnostic model for non-invasively identifying NASH.
Methods
Silver nanoparticles as the SERS-active nanostructures were mixed with blood serum to enhance the Raman scattering signals. The spectral data set was used to train the NASH classification model by a neural network primarily consisting of a fully connected residual module.
Results
Data on 261 Chinese individuals with biopsy-proven NAFLD were included and a prediction model for NASH was built based on SERS spectra and neural network approaches. The model yielded an AUROC of 0.83 (95% confidence interval [CI] 0.70–0.92) in the validation set, which was better than AUROCs of both serum CK-18-M30 levels (AUROC 0.63, 95% CI 0.48–0.76, p = 0.044) and the HAIR score (AUROC 0.65, 95% CI 0.51–0.77, p = 0.040). Subgroup analyses showed that the model performed well in different patient subgroups.
Conclusions
Fully connected neural network-based serum SERS analysis is a rapid and practical tool for the non-invasive identification of NASH. The online calculator website for the estimated risk of NASH is freely available to healthcare providers and researchers (http://www.pan-chess.cn/calculator/RAMAN_score).
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More information
Accepted/In Press date: 23 October 2022
e-pub ahead of print date: 11 November 2022
Published date: 1 April 2023
Additional Information:
Funding Information:
This work was supported by grants from the National Natural Science Foundation of China (82070588), High Level Creative Talents from Department of Public Health in Zhejiang Province (S2032102600032) and Project of New Century 551 Talent Nurturing in Wenzhou. GT is supported in part by grants from the School of Medicine, University of Verona, Verona, Italy. CDB is supported in part by the Southampton NIHR Biomedical Research Centre (IS-BRC-20004).
Funding Information:
We thank Herui Biomed Company Limited (Suzhou, China) for providing CK-18 M30 ELISA kits.
Publisher Copyright:
© 2022, Asian Pacific Association for the Study of the Liver.
Keywords:
NAFLD (non-alcoholic fatty liver disease), NASH (non-alcoholic steatohepatitis), Raman, SERS (surface-enhanced Raman spectroscopy)
Identifiers
Local EPrints ID: 471756
URI: http://eprints.soton.ac.uk/id/eprint/471756
ISSN: 1936-0533
PURE UUID: b7573159-7809-4074-b52d-3c2372a024aa
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Date deposited: 17 Nov 2022 17:45
Last modified: 17 Mar 2024 07:34
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Contributors
Author:
Feng Gao
Author:
De-Chan Lu
Author:
Tian-Lei Zheng
Author:
Shi Geng
Author:
Jun-Cheng Sha
Author:
Ou-Yang Huang
Author:
Liang-Jie Tang
Author:
Pei-Wu Zhu
Author:
Yang-Yang Li
Author:
Li-Li Chen
Author:
Giovanni Targher
Author:
Zu-Fang Huang
Author:
Ming-Hua Zheng
Corporate Author: CHESS-MAFLD consortium
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