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

Yoshitomo Kikuchi, Osamu Togao, Kazufumi Kikuchi, Daichi Momosaka, Makoto Obara, Marc Van Cauteren, Alexander Fischer, Kousei Ishigami, Hiwatashi Akio

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)2998-3005
Number of pages8
JournalEuropean Radiology
Volume32
Issue number5
DOIs
Publication statusPublished - May 2022

All Science Journal Classification (ASJC) codes

  • Radiology Nuclear Medicine and imaging

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