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A convolutional neural network-based system to classify patients using FDG PET/CT examinations.
- Source :
-
BMC cancer [BMC Cancer] 2020 Mar 17; Vol. 20 (1), pp. 227. Date of Electronic Publication: 2020 Mar 17. - Publication Year :
- 2020
-
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.<br />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).<br />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.<br />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.
- Subjects :
- Abdominal Neoplasms classification
Adult
Aged
Aged, 80 and over
Artificial Intelligence
Female
Fluorodeoxyglucose F18
Head and Neck Neoplasms classification
Humans
Male
Middle Aged
Pelvic Neoplasms classification
Thoracic Neoplasms classification
Young Adult
Abdominal Neoplasms diagnostic imaging
Head and Neck Neoplasms diagnostic imaging
Neural Networks, Computer
Pelvic Neoplasms diagnostic imaging
Positron Emission Tomography Computed Tomography trends
Thoracic Neoplasms diagnostic imaging
Subjects
Details
- Language :
- English
- ISSN :
- 1471-2407
- Volume :
- 20
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- BMC cancer
- Publication Type :
- Academic Journal
- Accession number :
- 32183748
- Full Text :
- https://doi.org/10.1186/s12885-020-6694-x