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 3,485 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 region-based analysis of the CNN (head-and-neck, chest, abdomen, and pelvic region).
Results: There were 1,280 (37%), 1,450 (42%), and 755 (22%) patients classified as benign, malignant and equivocal, respectively. In patient-based analysis, the 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.
Deep learning with deep convolutional neural network using FDG-PET/CT for malignant pleural mesothelioma diagnosis