Evidence map›Paper›PMID 33492478›Full record

ArticleEJNMMI research2021

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

Zongyao Li, Kazuhiro Kitajima, Kenji Hirata, Ren Togo, Junki Takenaka, Yasuo Miyoshi, Kohsuke Kudo, Takahiro Ogawa, Miki Haseyama

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PubMed Full text at PMC Publisher
In one paragraph

Article in EJNMMI research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zongyao LiGraduate School of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan.
Kazuhiro KitajimaDepartment of Radiology, Division of Nuclear Medicine and PET Center, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
Kenji HirataDepartment 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.ORCID http://orcid.org/0000-0003-0036-8975
Ren TogoEducation and Research Center for Mathematical and Data Science, Hokkaido University, N-12, W-7, Kita-ku, Sapporo, 060-0812, Japan.
Junki TakenakaDepartment of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
Yasuo MiyoshiDepartment of Breast and Endocrine Surgery, Hyogo College of Medicine, 1-1 Mukogawa-cho, Nishinomiya, Hyogo, 663-8501, Japan.
Kohsuke KudoDepartment of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15, Nishi 7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan.
Takahiro OgawaFaculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan.
Miki HaseyamaFaculty of Information Science and Technology, Hokkaido University, N-14, W-9, Kita-ku, Sapporo, 060-0814, Japan.

Funding

Hokkaido University Grants-in-Aid for Cross-departmental Young Researcher GrantsJapan Society for the Promotion of Science JP17H01744Japan Society for the Promotion of Science JP20K08015Japan Society for the Promotion of Science JP20K19857
6 · The paper itself

Abstract

backgroundTo improve the diagnostic accuracy of axillary lymph node (LN) metastasis in breast cancer patients using 2-[ MATERIALS AND

methodsTwo clinicians and the new AI system retrospectively analyzed and diagnosed 414 axillae of 407 patients with biopsy-proven breast cancer who had undergone 2-[

resultsAlthough 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.

conclusionsIt is expected that AI using deep-learning technologies will be useful in diagnosing axillary LN metastasis using 2-[

Indexed as

2-[18f]FDG-PET/CTAI-assisted diagnosisAxillary lymph nodeBreast cancerDeep convolutional neural network

Identifiers

PMID33492478
PMCPMC7835273

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.