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Original research
Kenji Hirata
Department of Diagnostic Imaging, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0036-8975
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 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 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 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.
Keywords
breast cancer, axillary lymph node, FDG-PET/CT, AI-assisted diagnosis, deep convolutional neural network
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A new preprint design is coming!
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See the published version of this preprint at EJNMMI Research.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Original research
Kenji Hirata
Department of Diagnostic Imaging, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido 060-8638, Japan.
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0036-8975
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at EJNMMI Research.
Version 1
Posted 21 Oct, 2020
No community comments so far
Editorial decision: Major Revision
On 17 Nov, 2020
Review #2 received
Received 16 Nov, 2020
Review #1 received
Received 01 Nov, 2020
Reviewer #2 agreed
On 19 Oct, 2020
Reviewer #1 agreed
On 18 Oct, 2020
Reviewers invited
Invitations sent on 16 Oct, 2020
Editor assigned
On 15 Oct, 2020
First submitted
On 14 Oct, 2020
Submission checks complete
On 14 Oct, 2020
Editor invited
On 14 Oct, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at EJNMMI Research.
Background: To improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 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 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 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
The full text of this article is available to read as a PDF.
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