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BMC Cancer. 2020 Mar 17;20(1):227. doi: 10.1186/s12885-020-6694-x.

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

Author information

1
Graduate School of Biomedical Science and Engineering, School of Medicine, Hokkaido University, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
2
Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan.
3
Department of Nuclear Medicine, Hokkaido University Hospital, N15 W7, Kita-ku, Sapporo, Hokkaido, 0608638, Japan.
4
Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, N15 W7, Kita-ku, Sapporo, 0608638, Japan. khirata@med.hokudai.ac.jp.
5
Department of Nuclear Medicine, Hokkaido University Hospital, N15 W7, Kita-ku, Sapporo, Hokkaido, 0608638, Japan. khirata@med.hokudai.ac.jp.
6
Faculty of Health Sciences Biomedical Science and Engineering, Hokkaido University, N15 W7, Kita-ku, Sapporo, 0608638, Japan.

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.

KEYWORDS:

Convolutional neural network; Deep learning; FDG; PET

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