Yoshitomo Kikuchi1, Osamu Togao2, Kazufumi Kikuchi1, Daichi Momosaka1, Makoto Obara3, Marc Van Cauteren4, Alexander Fischer5, Kousei Ishigami1, Akio Hiwatashi6. 1. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. 2. Department of Molecular Imaging and Diagnosis, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan. 3. MR Clinical Science, Philips Japan Ltd, Tokyo, Japan. 4. Philips Healthcare, Asia Pacific, Tokyo, Japan. 5. Philips Healthcare AI Informatics, Aachen, Germany. 6. Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan. hiwatashi.akio.428@m.kyushu-u.ac.jp.
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.
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.
Authors: Elizabeth A Sadowski; Atul B Shinagare; Hyesun Park; Olga R Brook; Rosemarie Forstner; Sumer K Wallace; Jeanne M Horowitz; Neil Horowitz; Marcia Javitt; Priyanka Jha; Aki Kido; Yulia Lakhman; Susanna I Lee; Lucia Manganaro; Katherine E Maturen; Stephanie Nougaret; Liina Poder; Gaiane M Rauch; Caroline Reinhold; Evis Sala; Isabelle Thomassin-Naggara; Herbert Alberto Vargas; Aradhana Venkatesan; Olivera Nikolic; Andrea G Rockall Journal: Eur Radiol Date: 2021-11-30 Impact factor: 7.034