See the published version of this preprint at BMC Nephrology.
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Research article
Xiaowen Tu
PLA Rocket Force Characteristic Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2364-0110
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues.
Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition.
Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms.
Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.
Keywords
membranous nephropathy, idiopathic membranous nephropathy, hepatitis B virus, hyperspectral imagery, deep learning
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CURRENT STATUS: Published
View the published article at BMC Nephrology.
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Posted 19 Jan, 2021
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Review #3 received
Received 28 Mar, 2021
Editorial decision: Minor revision
On 28 Mar, 2021
Reviewer #3 agreed
On 09 Mar, 2021
Review #2 received
Received 06 Feb, 2021
Review #1 received
Received 29 Jan, 2021
Reviewer #2 agreed
On 13 Jan, 2021
Reviewer #1 agreed
On 11 Jan, 2021
Editor assigned
On 10 Jan, 2021
Reviewers invited
Invitations sent on 10 Jan, 2021
Submission checks complete
On 10 Jan, 2021
Editor invited
On 10 Jan, 2021
No comments provided
Review #3 received
Received 25 Nov, 2020
Editorial decision: Major revision
On 25 Nov, 2020
Reviewer #3 agreed
On 07 Nov, 2020
Review #2 received
Received 05 Oct, 2020
Reviewer #2 agreed
On 09 Sep, 2020
Review #1 received
Received 06 Sep, 2020
Reviewer #1 agreed
On 09 Jul, 2020
Reviewers invited
Invitations sent on 06 Jul, 2020
Editor assigned
On 06 Jul, 2020
Submission checks complete
On 05 Jul, 2020
Editor invited
On 05 Jul, 2020
No comments provided
Editor assigned
On 07 Jun, 2020
Editorial decision: Revise before sending to peer reviewers
On 07 Jun, 2020
Submission checks complete
On 06 Jun, 2020
Editor invited
On 06 Jun, 2020
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Urology & Nephrology
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See the published version of this preprint at BMC Nephrology.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research article
Xiaowen Tu
PLA Rocket Force Characteristic Medical Center
Corresponding Author
ORCiD: https://orcid.org/0000-0002-2364-0110
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at BMC Nephrology.
Version 3
Posted 19 Jan, 2021
No community comments so far
Review #3 received
Received 28 Mar, 2021
Editorial decision: Minor revision
On 28 Mar, 2021
Reviewer #3 agreed
On 09 Mar, 2021
Review #2 received
Received 06 Feb, 2021
Review #1 received
Received 29 Jan, 2021
Reviewer #2 agreed
On 13 Jan, 2021
Reviewer #1 agreed
On 11 Jan, 2021
Editor assigned
On 10 Jan, 2021
Reviewers invited
Invitations sent on 10 Jan, 2021
Submission checks complete
On 10 Jan, 2021
Editor invited
On 10 Jan, 2021
No comments provided
Review #3 received
Received 25 Nov, 2020
Editorial decision: Major revision
On 25 Nov, 2020
Reviewer #3 agreed
On 07 Nov, 2020
Review #2 received
Received 05 Oct, 2020
Reviewer #2 agreed
On 09 Sep, 2020
Review #1 received
Received 06 Sep, 2020
Reviewer #1 agreed
On 09 Jul, 2020
Reviewers invited
Invitations sent on 06 Jul, 2020
Editor assigned
On 06 Jul, 2020
Submission checks complete
On 05 Jul, 2020
Editor invited
On 05 Jul, 2020
No comments provided
Editor assigned
On 07 Jun, 2020
Editorial decision: Revise before sending to peer reviewers
On 07 Jun, 2020
Submission checks complete
On 06 Jun, 2020
Editor invited
On 06 Jun, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Urology & Nephrology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at BMC Nephrology.
Background: Common subtypes seen in Chinese patients with membranous nephropathy (MN) include idiopathic membranous nephropathy (IMN) and hepatitis B virus-related membranous nephropathy (HBV-MN). However, the morphologic differences are not visible under the light microscope in certain renal biopsy tissues.
Methods: We propose here a deep learning-based framework for processing hyperspectral images of renal biopsy tissue to define the difference between IMN and HBV-MN based on the component of their immune complex deposition.
Results: The proposed framework can achieve an overall accuracy of 95.04% in classification, which also leads to better performance than support vector machine (SVM)-based algorithms.
Conclusion: IMN and HBV-MN can be correctly separated via the deep learning framework using hyperspectral imagery. Our results suggest the potential of the deep learning algorithm as a new method to aid in the diagnosis of MN.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
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