Zongyao Li1, Kazuhiro Kitajima2, Kenji Hirata3, Ren Togo4, Junki Takenaka5, Yasuo Miyoshi6, Kohsuke Kudo5,7, Takahiro Ogawa8, Miki Haseyama8. 1. Graduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan. 2. Department of Radiology, Division of Nuclear Medicine and PET Center, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan. 3. Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan. khirata@med.hokudai.ac.jp. 4. Education and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-ku, Sapporo, 060-0812, Japan. 5. Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan. 6. Department of Breast and Endocrine Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan. 7. Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan. 8. Faculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan.
Abstract
BACKGROUND: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. MATERIALS AND METHODS: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. RESULTS: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. CONCLUSIONS: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.
BACKGROUND: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancerpatients using 2-[18F]FDG-PET/CT, we constructed an artificial intelligence (AI)-assisted diagnosis system that uses deep-learning technologies. MATERIALS AND METHODS: Two clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[18F]FDG-PET/CT before a mastectomy or breast-conserving surgery with a sentinel lymph node (LN) biopsy and/or axillary LN dissection. We designed and trained a deep 3D convolutional neural network (CNN) as the AI model. The diagnoses from the clinicians were blended with the diagnoses from the AI model to improve the diagnostic accuracy. RESULTS: Although the AI model did not outperform the clinicians, the diagnostic accuracies of the clinicians were considerably improved by collaborating with the AI model: the two clinicians' sensitivities of 59.8% and 57.4% increased to 68.6% and 64.2%, respectively, whereas the clinicians' specificities of 99.0% and 99.5% remained unchanged. CONCLUSIONS: It is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[18F]FDG-PET/CT. Even if the diagnostic performance of AI is not better than that of clinicians, taking AI diagnoses into consideration may positively impact the overall diagnostic accuracy.
Entities:
Keywords:
2-[18f]FDG-PET/CT; AI-assisted diagnosis; Axillary lymph node; Breast cancer; Deep convolutional neural network
Authors: Till A Heusner; Sherko Kuemmel; Steffen Hahn; Angela Koeninger; Friedrich Otterbach; Monia E Hamami; Klaus R Kimmig; Michael Forsting; Andreas Bockisch; Gerald Antoch; Alexander Stahl Journal: Eur J Nucl Med Mol Imaging Date: 2009-05-05 Impact factor: 9.236