Preliminary study of AI-assisted diagnosis using FDG-PET/CT for axillary lymph node metastasis in patients with breast cancer

• Published in: EJNMMI research Vol. 11; no. 1; p. 10
• Main Authors: Li, Zongyao, Kitajima, Kazuhiro, Hirata, Kenji, Togo, Ren, Takenaka, Junki, Miyoshi, Yasuo, Kudo, Kohsuke, Ogawa, Takahiro, Haseyama, Miki
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 25.01.2021; Springer Nature B.V; SpringerOpen
• Subjects: 2-[18f]FDG-PET/CT; Accuracy; Advanced Image Analysis (Artificial Intelligence; AI-assisted diagnosis; Artificial intelligence; Artificial neural networks; Axillary lymph node; Biopsy; Breast cancer; Cardiac Imaging; Computed tomography; Deep convolutional neural network; Diagnosis; Diagnostic systems; Fluorine isotopes; Imaging; Learning; Lymphatic system; Mastectomy; Medicine; Medicine & Public Health; Metastasis; Model accuracy; Nuclear Medicine; Oncology; Original Research; Orthopedics; Radiology; Radiomics; Breast cancer; Axillary lymph node; AI-assisted diagnosis; 2-; Deep convolutional neural network; f]FDG-PET/CT; 2-[18f]FDG-PET/CT
• DOI: 10.1186/s13550-021-00751-4
• ISSN: 2191-219X

Background To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ 18 F]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-[ 18 F]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-[ 18 F]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.