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

Authors :
Kawauchi K
Furuya S
Hirata K
Katoh C
Manabe O
Kobayashi K
Watanabe S
Shiga T
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.

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