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Automated detection of brain metastases on non-enhanced CT using single-shot detectors

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

To develop and investigate deep learning–based detectors for brain metastases detection on non-enhanced (NE) CT.

Methods

The study included 116 NECTs from 116 patients (81 men, age 66.5 ± 10.6 years) to train and test single-shot detector (SSD) models using 89 and 27 cases, respectively. The annotation was performed by three radiologists using bounding-boxes defined on contrast-enhanced CT (CECT) images. NECTs were coregistered and resliced to CECTs. The detection performance was evaluated at the SSD’s 50% confidence threshold using sensitivity, positive-predictive value (PPV), and the false-positive rate per scan (FPR). For false negatives and true positives, binary logistic regression was used to examine the possible contributing factors.

Results

For lesions 6 mm or larger, the SSD achieved a sensitivity of 35.4% (95% confidence interval (CI): [32.3%, 33.5%]); 51/144) with an FPR of 14.9 (95% CI [12.4, 13.9]). The overall sensitivity was 23.8% (95% CI: [21.3%, 22.8%]; 55/231) and PPV was 19.1% (95% CI: [18.5%, 20.4%]; 98/ of 513), with an FPR of 15.4 (95% CI [12.9, 14.5]). Ninety-five percent of the lesions that SSD failed to detect were also undetectable to radiologists (168/176). Twenty-four percent of the lesions (13/50) detected by the SSD were undetectable to radiologists. Logistic regression analysis indicated that density, necrosis, and size contributed to the lesions’ visibility for radiologists, while for the SSD, the surrounding edema also enhanced the detection performance.

Conclusion

The SSD model we developed could detect brain metastases larger than 6 mm to some extent, a quarter of which were even retrospectively unrecognizable to radiologists.

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Data availability

The datasets and materials are available upon a reasonable request.

Code availability

The codes are available upon a reasonable request.

Abbreviations

CE:

Contrast-enhanced

DL:

Deep learning

NE:

Non-enhanced

R-CNN:

Region-based convolutional neural network

SSD:

Single-shot detector

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Funding

No funding was received for this study.

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Authors and Affiliations

  1. Department of Radiology, The Graduate School of Medicine, University of Tokyo, 7‑3‑1 Hongo, Bunkyo‑ku, Tokyo, 113‑8655, Japan

    Shimpei Kato, Shiori Amemiya, Hidemasa Takao, Naoya Sakamoto & Osamu Abe

  2. Department of Radiology, Teikyo University Hospital, Mizonokuchi, 5-1-1 Futago, Takatsu-ku, Kawasaki, Kanagawa, 213-8507, Japan

    Hiroshi Yamashita

Authors
  1. Shimpei Kato
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  2. Shiori Amemiya
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  3. Hidemasa Takao
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  4. Hiroshi Yamashita
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  5. Naoya Sakamoto
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  6. Osamu Abe
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Corresponding author

Correspondence to Shiori Amemiya.

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All generating datasets and analysis were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was waived off due to the retrospective nature of the study.

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Kato, S., Amemiya, S., Takao, H. et al. Automated detection of brain metastases on non-enhanced CT using single-shot detectors. Neuroradiology 63, 1995–2004 (2021). https://doi.org/10.1007/s00234-021-02743-6

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  • DOI: https://doi.org/10.1007/s00234-021-02743-6

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