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A convolutional neural network-based system to classify patients using FDG PET/CT examinations

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Title: A convolutional neural network-based system to classify patients using FDG PET/CT examinations
Authors: Kawauchi, Keisuke Browse this author
Furuya, Sho Browse this author
Hirata, Kenji Browse this author →KAKEN DB
Katoh, Chietsugu Browse this author →KAKEN DB
Manabe, Osamu Browse this author →KAKEN DB
Kobayashi, Kentaro Browse this author →KAKEN DB
Watanabe, Shiro Browse this author →KAKEN DB
Shiga, Tohru Browse this author →KAKEN DB
Keywords: FDG
PET
Convolutional neural network
Deep learning
Issue Date: 17-Mar-2020
Publisher: BioMed Central
Journal Title: BMC cancer
Volume: 20
Issue: 1
Start Page: 227
Publisher DOI: 10.1186/s12885-020-6694-x
Abstract: Background As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detection systems on artificial intelligence (AI) to prevent human oversight and misdiagnosis are rapidly growing. We aimed to develop a convolutional neural network (CNN)-based system that can classify whole-body FDG PET as 1) benign, 2) malignant or 3) equivocal. Methods This retrospective study investigated 3485 sequential patients with malignant or suspected malignant disease, who underwent whole-body FDG PET/CT at our institute. All the cases were classified into the 3 categories by a nuclear medicine physician. A residual network (ResNet)-based CNN architecture was built for classifying patients into the 3 categories. In addition, we performed a region-based analysis of CNN (head-and-neck, chest, abdomen, and pelvic region). Results There were 1280 (37%), 1450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In the patient-based analysis, CNN predicted benign, malignant and equivocal images with 99.4, 99.4, and 87.5% accuracy, respectively. In region-based analysis, the prediction was correct with the probability of 97.3% (head-and-neck), 96.6% (chest), 92.8% (abdomen) and 99.6% (pelvic region), respectively. Conclusion The CNN-based system reliably classified FDG PET images into 3 categories, indicating that it could be helpful for physicians as a double-checking system to prevent oversight and misdiagnosis.
Rights: http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/77819
Appears in Collections:医学院・医学研究院 (Graduate School of Medicine / Faculty of Medicine) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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