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A convolutional neural network-based system to classify patients using FDG PET/CT examinations
This item is licensed under:Creative Commons Attribution 4.0 International
| 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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