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

EJNMMI Research

Yasuo Miyoshi¹  Junki Takenaka²  Kohsuke Kudo²  Kenji Hirata²  Kazuhiro Kitajima³  Ren Togo⁴  Miki Haseyama⁵  Takahiro Ogawa⁵  Zongyao Li⁶ 

[1] Department of Breast and Endocrine Surgery, Hyogo College of Medicine;Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University;Department of Radiology, Division of Nuclear Medicine and PET Center, Hyogo College of Medicine;Education and Research Center for Mathematical and Data Science, Hokkaido University;Faculty of Information Science and Technology, Hokkaido University;Graduate School of Information Science and Technology, Hokkaido University;

关键词: Breast cancer;    Axillary lymph node;    2-[18f]FDG-PET/CT;    AI-assisted diagnosis;    Deep convolutional neural network;   

DOI  :  10.1186/s13550-021-00751-4

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown