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Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network

  • Retinal Disorders
  • Published:
Graefe's Archive for Clinical and Experimental Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

To predict short-term anti-vascular endothelial growth factor (anti-VEGF) treatment responder/non-responder for neovascular age-related macular degeneration (nAMD) patients based on optical coherence tomography (OCT) images.

Methods

A total of 4944 OCT scans from 206 patients with nAMD were involved to develop and evaluate a responder/non-responder prediction method for the short-term effect of anti-VEGF therapy. A deep learning architecture named sensitive structure guided network (SSG-Net) was proposed to make the prediction leveraging a sensitive structure guidance module trained from pre- and post-treatment images. To verify its clinical efficiency, other 2 deep learning methods and 4 experienced ophthalmologists were involved to evaluate the performance of the developed model.

Results

For the testing dataset, SSG-Net could predict the response by an accuracy of 84.6% and an area under the receiver curve (AUC) of 0.83, with a sensitivity of 0.692 and specificity of 1. In contrast, the 2 compared deep learning methods achieved an accuracy of 65.4% with a sensitivity of 0.461 and specificity of 0.846, and an accuracy of 73.1% with a sensitivity of 0.692 and specificity of 0.846, respectively. The predicted accuracy for 4 experienced ophthalmologists was 53.8 to 76.9%, with sensitivity of 0.538 to 0.923 and specificity of 0.385 to 0.846, respectively.

Conclusion

Our proposed SSG-Net shows effective prediction on the short-term efficacy of anti-VEGF treatment for nAMD patients. This technique could potentially help clinicians explain the necessity of anti-VEGF treatment to the potential responder and avoid unnecessary treatment for the non-responder.

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Data availability

All relevant data was shown in the manuscript.

Code availability

Acquired by contacting the corresponding author.

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Acknowledgements

We would like to thank the editor and anonymous reviewers for the valuable comments and suggestions.

Funding

No funding existed for this study.

Author information

Author notes

  1. Xinyu Zhao and Xiaoyue Zhang contributed equally to this work.

Authors and Affiliations

  1. Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China

    Xinyu Zhao, Lihui Meng & Youxin Chen

  2. Key Lab of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Beijing, 100730, China

    Xinyu Zhao, Lihui Meng & Youxin Chen

  3. Ping An Healthcare Technology, Beijing, China

    Xiaoyue Zhang, Bin Lv, Chengfen Zhang, Yang Liu, Chuanfeng Lv & Guotong Xie

  4. Ping An Health and Technology Company Limited, Shanghai, China

    Guotong Xie

  5. Ping An International Smart City Technology Company Limited, Shenzhen, China

    Guotong Xie

Authors
  1. Xinyu Zhao
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Corresponding authors

Correspondence to Guotong Xie or Youxin Chen.

Ethics declarations

Ethics approval

The study was approved by the Ethics Committee of Peking Union Medical College Hospital, Chinese Academy of Medical Sciences (No. S-K1611). The whole process adhered to the tenets of the Declaration of Helsinki.

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A written informed consent was obtained from each participant before treatment.

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A written informed consent for publication was obtained from each participant before treatment.

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All authors have contributed significantly and are in agreement with the content of the manuscript. All authors have no relevant financial relationships to disclose.

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Supplementary Information

Figure S1.

For the encoder, we utilized SE-ResNet blocks to achieve a channel-wise attention and consistent training. The encoder is then connected by global average pooling and fully connected layers to output classification results. The decoder is a top-down architecture with lateral connections and outputs spatial transform field. Then the image of pre-treatment is warped with spatial transform field using a spatial transformer function and transformed to an artificial OCT image of post-treatment. Encoders could get the back propagation by skip connection from decoders, absorbing more guiding information from the back propagation and concentrate more on the structural changes from semantic feature maps at all scales when outputting results. (PNG 1075 kb)

High resolution image (TIF 630 kb)

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Zhao, X., Zhang, X., Lv, B. et al. Optical coherence tomography-based short-term effect prediction of anti-vascular endothelial growth factor treatment in neovascular age-related macular degeneration using sensitive structure guided network. Graefes Arch Clin Exp Ophthalmol 259, 3261–3269 (2021). https://doi.org/10.1007/s00417-021-05247-4

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  • DOI: https://doi.org/10.1007/s00417-021-05247-4

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