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A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression

  • Magnetic Resonance
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To develop an automated model to detect brain metastases using a convolutional neural network (CNN) and volume isotropic simultaneous interleaved bright-blood and black-blood examination (VISIBLE) and to compare its diagnostic performance with the observer test.

Methods

This retrospective study included patients with clinical suspicion of brain metastases imaged with VISIBLE from March 2016 to July 2019 to create a model. Images with and without blood vessel suppression were used for training an existing CNN (DeepMedic). Diagnostic performance was evaluated using sensitivity and false-positive results per case (FPs/case). We compared the diagnostic performance of the CNN model with that of the twelve radiologists.

Results

Fifty patients (30 males and 20 females; age range 29–86 years; mean 63.3 ± 12.8 years; a total of 165 metastases) who were clinically diagnosed with brain metastasis on follow-up were used for the training. The sensitivity of our model was 91.7%, which was higher than that of the observer test (mean ± standard deviation; 88.7 ± 3.7%). The number of FPs/case in our model was 1.5, which was greater than that by the observer test (0.17 ± 0.09).

Conclusions

Compared to radiologists, our model created by VISIBLE and CNN to diagnose brain metastases showed higher sensitivity. The number of FPs/case by our model was greater than that by the observer test of radiologists; however, it was less than that in most of the previous studies with deep learning.

Key Points

• Our convolutional neural network based on bright-blood and black-blood examination to diagnose brain metastases showed a higher sensitivity than that by the observer test.

• The number of false-positives/case by our model was greater than that by the previous observer test; however, it was less than those from most previous studies.

• In our model, false-positives were found in the vessels, choroid plexus, and image noise or unknown causes.

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Abbreviations

3D :

Three-dimensional

CNN :

Convolution neural network

FP :

False-positive

MSDE :

Motion-sensitized driven equilibrium

VISIBLE:

Volume isotropic simultaneous interleaved bright and black-blood examination

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Funding

This work was supported by JSPS KAKENHI Grant Number JP21K07645.

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Authors and Affiliations

  1. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan

    Yoshitomo Kikuchi, Kazufumi Kikuchi, Daichi Momosaka, Kousei Ishigami & Akio Hiwatashi

  2. Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan

    Osamu Togao

  3. MR Clinical Science, Philips Japan Ltd, Tokyo, Japan

    Makoto Obara

  4. Philips Healthcare, Asia Pacific, Tokyo, Japan

    Marc Van Cauteren

  5. Philips Healthcare AI Informatics, Aachen, Germany

    Alexander Fischer

Authors
  1. Yoshitomo Kikuchi
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  2. Osamu Togao
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  3. Kazufumi Kikuchi
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  4. Daichi Momosaka
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  5. Makoto Obara
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  6. Marc Van Cauteren
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  7. Alexander Fischer
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  8. Kousei Ishigami
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  9. Akio Hiwatashi
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Corresponding author

Correspondence to Akio Hiwatashi.

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Guarantor

The scientific guarantor of this publication is Associate Professor Akio Hiwatashi, MD, PhD, from the Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University (hiwatashi.akio.428@m.kyushu-u.ac.jp).

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Philips Japan and Philips Healthcare. The authors of this manuscript declare that MO is an employee of Philips Japan and MVC and AF are employee of Philips Healthcare. They were not involved in data analysis in this study.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

There is an overlap with previous publication (Kikuchi K, Hiwatashi A, Togao O, et al 3D MR Sequence Capable of Simultaneous Image Acquisitions with and without Blood Vessel Suppression: Utility in Diagnosing Brain Metastases. Eur Radiol 2015;25:901–910.) We compared the diagnostic ability of the newly developed brain metastasis diagnosing system with deep learning and that of the previous observer test in this publication.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

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Cite this article

Kikuchi, Y., Togao, O., Kikuchi, K. et al. A deep convolutional neural network-based automatic detection of brain metastases with and without blood vessel suppression. Eur Radiol 32, 2998–3005 (2022). https://doi.org/10.1007/s00330-021-08427-2

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  • DOI: https://doi.org/10.1007/s00330-021-08427-2

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