Evidence map›Paper›PMID 40608200›Full record

ArticleBreast cancer (Tokyo, Japan)2025

Development of a deep learning-based automated diagnostic system (DLADS) for classifying mammographic lesions - a first large-scale multi-institutional clinical trial in Japan.

Takeshi Yamaguchi, Yoichi Koyama, Kenichi Inoue, Kanako Ban, Koichi Hirokaga, Yuka Kujiraoka, Yuko Okanami, Norimitsu Shinohara, Hiroko Tsunoda, Takayoshi Uematsu and 1 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Breast cancer (Tokyo, Japan), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

11 authors.

Takeshi Yamaguchi *Department of Medical Oncology, Toranomon Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0003-3306-314X
Yoichi Koyama *Department of Breast Surgical Oncology, Tokyo Medical University Hospital, 6-7-1 Nishishinjuku, Shinjuku-Ku, Tokyo, 160-0023, Japan. peach@tokyo-med.ac.jp.ORCID http://orcid.org/0000-0001-7435-1346
Kenichi InoueBreast Cancer Center, Shonan Memorial Hospital, Kanagawa, Japan.ORCID http://orcid.org/0000-0002-0501-9945
Kanako BanYotsuya Medical Cube, Tokyo, Japan.ORCID http://orcid.org/0000-0002-7244-2436
Koichi HirokagaDepartment of Breast Surgery, Hyogo Cancer Center, Hyogo, Japan.ORCID http://orcid.org/0009-0000-7563-7370
Yuka KujiraokaDepartment of Radiology, Tsukuba Memorial Hospital, Ibaraki, Japan.
Yuko OkanamiYuko Breast Clinic Meieki, Aichi, Japan.ORCID http://orcid.org/0009-0008-2304-6518
Norimitsu ShinoharaDepartment of Radiological Technology, Faculty of Health Sciences, Gifu University of Medical Science, Gifu, Japan.ORCID http://orcid.org/0000-0002-0926-4235
Hiroko TsunodaDepartment of Radiology, St. Luke's International Hospital, Tokyo, Japan.ORCID http://orcid.org/0000-0002-6686-5133
Takayoshi UematsuDepartment of Breast Imaging and Breast Interventional Radiology, Shizuoka Cancer Center Hospital, Shizuoka, Japan.ORCID http://orcid.org/0000-0003-4462-7430
Hirofumi MukaiDivision of Medical Oncology, National Cancer Center Hospital East, Chiba, Japan.ORCID http://orcid.org/0000-0002-9292-5128

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRecently, western countries have built evidence on mammographic artificial Intelligence-computer-aided diagnosis (AI-CADx) systems; however, their effectiveness has not yet been sufficiently validated in Japanese women. In this study, we aimed to establish a Japanese mammographic AI-CADx system for the first time.

methodsWe retrospectively collected screening or diagnostic mammograms from 63 institutions in Japan. We then randomly divided the images into training, validation, and test datasets in a balanced ratio of 8:1:1 on a case-level basis. The gold standard of annotation for the AI-CADx system is mammographic findings based on pathologic references. The AI-CADx system was developed using SE-ResNet modules and a sliding window algorithm. A cut-off concentration gradient of the heatmap image was set at 15%. The AI-CADx system was considered accurate if it detected the presence of a malignant lesion in a breast cancer mammogram. The primary endpoint of the AI-CADx system was defined as a sensitivity and specificity of over 80% for breast cancer diagnosis in the test dataset.

resultsWe collected 20,638 mammograms from 11,450 Japanese women with a median age of 55 years. The mammograms included 5019 breast cancer (24.3%), 5026 benign (24.4%), and 10,593 normal (51.3%) mammograms. In the test dataset of 2059 mammograms, the AI-CADx system achieved a sensitivity of 83.5% and a specificity of 84.7% for breast cancer diagnosis. The AUC in the test dataset was 0.841 (DeLong 95% CI; 0.822-0.859). The Accuracy was almost consistent independent of breast density, mammographic findings, type of cancer, and mammography vendors (AUC (range); 0.639-0.906).

conclusionsThe developed Japanese mammographic AI-CADx system diagnosed breast cancer with a pre-specified sensitivity and specificity. We are planning a prospective study to validate the breast cancer diagnostic performance of Japanese physicians using this AI-CADx system as a second reader.

trial registrationUMIN, trial number UMIN000039009. Registered 26 December 2019, https://www.umin.ac.jp/ctr/.

Indexed as

Breast NeoplasmsDeep LearningDiagnosis, Computer-AssistedMammographyAdultAgedEarly Detection of CancerFemaleHumansJapanMiddle AgedRetrospective StudiesSensitivity and SpecificityArtificial intelligenceBreast cancerComputer-aided detectionComputer-aided diagnosisConvolutional neural networkMammography

Identifiers

PMID40608200
PMCPMC12394324

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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.