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Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports

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Abstract

Purpose

Basal/acetazolamide brain perfusion single-photon emission computed tomography (SPECT) has been used to evaluate functional hemodynamics in patients with carotid artery stenosis. We aimed to develop a deep learning model as a support system for interpreting brain perfusion SPECT leveraging unstructured text reports.

Methods

In total, 7345 basal/acetazolamide brain perfusion SPECT images and their text reports were retrospectively collected. A long short-term memory (LSTM) network was trained using 500 randomly selected text reports to predict manually labeled structured information, including abnormalities of basal perfusion and vascular reserve for each vascular territory. Using this trained LSTM model, we extracted structured information from the remaining 6845 text reports to develop a deep learning model for interpreting SPECT images. The model was based on a 3D convolutional neural network (CNN), and the performance was tested on the other 500 cases by measuring the area under the receiver-operating characteristic curve (AUC). We then applied the model to patients who underwent revascularization (n = 33) to compare the estimated output of the CNN model for pre- and post-revascularization SPECT and clinical outcomes.

Results

The AUC of the LSTM model for extracting structured labels was 1.00 for basal perfusion and 0.99 for vascular reserve for all 9 brain regions. The AUC of the CNN model designed to identify abnormal perfusion was 0.83 for basal perfusion and 0.89 for vascular reserve. The output of the CNN model was significantly improved according to the revascularization in the target vascular territory, and its changes in brain territories were concordant with clinical outcomes.

Conclusion

We developed a deep learning model to support the interpretation of brain perfusion SPECT by converting unstructured text reports into structured labels. This model can be used as a support system not only to identify perfusion abnormalities but also to provide quantitative scores of abnormalities, particularly for patients who require revascularization.

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Funding

This study was supported by the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) funded by the Korean government, MSIP (2017M3C7A1048079) and supported by the NRF funded by the Korea government (MSIT) (NRF-2019R1F1A1061412).

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Authors and Affiliations

  1. Department of Nuclear Medicine, Seoul National University Hospital, 28 Yongon-Dong, Jongno-Gu, Seoul, 110-744, South Korea

    Hyun Gee Ryoo, Hongyoon Choi & Dong Soo Lee

  2. Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea

    Hyun Gee Ryoo & Dong Soo Lee

Authors
  1. Hyun Gee Ryoo
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Contributions

Conceptualization: Hyun Gee Ryoo, Hongyoon Choi, Dong Soo Lee; Methodology: Hyun Gee Ryoo, Hongyoon Choi; Software: Hyun Gee Ryoo, Hongyoon Choi; Formal analysis and investigation: Hyun Gee Ryoo; Writing original draft preparation: Hyun Gee Ryoo; Writing, review, and editing: Hongyoon Choi, Dong Soo Lee; Supervision: Dong Soo Lee.

Corresponding authors

Correspondence to Hongyoon Choi or Dong Soo Lee.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

This retrospective study was approved by Seoul National University/Seoul National University Hospital Institutional Review Board. The needs for informed consent were waived by the committee.

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This article is part of the Topical Collection on Advanced Image Analyses (Radiomics and Artificial Intelligence).

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Ryoo, H.G., Choi, H. & Lee, D.S. Deep learning-based interpretation of basal/acetazolamide brain perfusion SPECT leveraging unstructured reading reports. Eur J Nucl Med Mol Imaging 47, 2186–2196 (2020). https://doi.org/10.1007/s00259-019-04670-4

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