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AutoFibroNet: a deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD

AutoFibroNet: a deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD
AutoFibroNet: a deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD
Background: liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment.

Aim: to investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.

Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.

Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.

Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.



0269-2813
573-584
Zhan, Huiling
a39ec6ad-0100-4a5b-b528-a6cb32327b89
Chen, Siyu
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Gao, Feng
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Wang, Guangxing
99a21c4f-4f3b-4987-b743-6dc5f651abf3
Chen, Sui-Dan
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Xi, Gangqin
ef84faf8-500c-4b19-aecc-bf1c953ae8c8
Yuan, Hai-Yang
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Li, Xiaolu
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Liu, Wen-Yue
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Byrne, Christopher D.
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Targher, Giovanni
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Chen, Miao-Yang
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Yang, Yong-Feng
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Chen, Jun
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Fan, Zhiwen
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Sun, Xitai
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Cai, Guorong
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Zheng, Ming-Hua
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Zhuo, Shuangmu
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CHESS-MAFLD consortium
Zhan, Huiling
a39ec6ad-0100-4a5b-b528-a6cb32327b89
Chen, Siyu
6c65ef9b-6aa7-4721-b5ca-9972e1284ec2
Gao, Feng
e9218653-d975-4c2b-b51e-bc85333e8de4
Wang, Guangxing
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Chen, Sui-Dan
6f312897-981f-421b-9a1c-343606b94a66
Xi, Gangqin
ef84faf8-500c-4b19-aecc-bf1c953ae8c8
Yuan, Hai-Yang
7454a8fd-597b-469f-b3b8-ca73f78421d5
Li, Xiaolu
e361cab3-f5dd-49ce-956e-f6f0b0b21cc0
Liu, Wen-Yue
f1a3089e-aa65-458c-bbd5-5ba3552c5158
Byrne, Christopher D.
1370b997-cead-4229-83a7-53301ed2a43c
Targher, Giovanni
b4265f0b-b4ae-4145-b3cc-43389b7efba6
Chen, Miao-Yang
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Yang, Yong-Feng
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Chen, Jun
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Fan, Zhiwen
14835ff3-ae54-422d-b8b9-2e6a956ef77f
Sun, Xitai
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Cai, Guorong
8b64ccf8-215e-4ccb-bc6a-a6e68965040b
Zheng, Ming-Hua
f11af49c-c8d5-4e87-b11e-8f46c20ee764
Zhuo, Shuangmu
2f9da0cd-8155-4289-a638-f88c5bd28da2

Zhan, Huiling, Chen, Siyu and Gao, Feng , CHESS-MAFLD consortium (2023) AutoFibroNet: a deep learning and multi-photon microscopy-derived automated network for liver fibrosis quantification in MAFLD. Alimentary Pharmacology and Therapeutics, 58 (6), 573-584. (doi:10.1111/apt.17635).

Record type: Article

Abstract

Background: liver fibrosis is the strongest histological risk factor for liver-related complications and mortality in metabolic dysfunction-associated fatty liver disease (MAFLD). Second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) is a powerful tool for label-free two-dimensional and three-dimensional tissue visualisation that shows promise in liver fibrosis assessment.

Aim: to investigate combining multi-photon microscopy (MPM) and deep learning techniques to develop and validate a new automated quantitative histological classification tool, named AutoFibroNet (Automated Liver Fibrosis Grading Network), for accurately staging liver fibrosis in MAFLD.

Methods: AutoFibroNet was developed in a training cohort that consisted of 203 Chinese adults with biopsy-confirmed MAFLD. Three deep learning models (VGG16, ResNet34, and MobileNet V3) were used to train pre-processed images and test data sets. Multi-layer perceptrons were used to fuse data (deep learning features, clinical features, and manual features) to build a joint model. This model was then validated in two further independent cohorts.

Results: AutoFibroNet showed good discrimination in the training set. For F0, F1, F2 and F3-4 fibrosis stages, the area under the receiver operating characteristic curves (AUROC) of AutoFibroNet were 1.00, 0.99, 0.98 and 0.98. The AUROCs of F0, F1, F2 and F3-4 fibrosis stages for AutoFibroNet in the two validation cohorts were 0.99, 0.83, 0.80 and 0.90 and 1.00, 0.83, 0.80 and 0.94, respectively, showing a good discriminatory ability in different cohorts.

Conclusion: AutoFibroNet is an automated quantitative tool that accurately identifies histological stages of liver fibrosis in Chinese individuals with MAFLD.



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Accepted/In Press date: 23 June 2023
e-pub ahead of print date: 4 July 2023
Published date: September 2023
Additional Information: Funding Information: This paper was funded by grants from the National Key Research and Development Program of China (2019YFE0113700), 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 (NIHR203319), UK.

Identifiers

Local EPrints ID: 478800
URI: http://eprints.soton.ac.uk/id/eprint/478800
ISSN: 0269-2813
PURE UUID: 383b4338-8192-4a22-b833-1c60560af1a7
ORCID for Christopher D. Byrne: ORCID iD orcid.org/0000-0001-6322-7753

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Date deposited: 10 Jul 2023 16:50
Last modified: 23 Jun 2024 04:01

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Contributors

Author: Huiling Zhan
Author: Siyu Chen
Author: Feng Gao
Author: Guangxing Wang
Author: Sui-Dan Chen
Author: Gangqin Xi
Author: Hai-Yang Yuan
Author: Xiaolu Li
Author: Wen-Yue Liu
Author: Giovanni Targher
Author: Miao-Yang Chen
Author: Yong-Feng Yang
Author: Jun Chen
Author: Zhiwen Fan
Author: Xitai Sun
Author: Guorong Cai
Author: Ming-Hua Zheng
Author: Shuangmu Zhuo
Corporate Author: CHESS-MAFLD consortium

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