Evidence map›Paper›PMID 39330431›Full record

ArticleJournal of imaging2024

AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.

Mio Adachi, Tomoyuki Fujioka, Toshiyuki Ishiba, Miyako Nara, Sakiko Maruya, Kumiko Hayashi, Yuichi Kumaki, Emi Yamaga, Leona Katsuta, Du Hao and 5 more

Abstract read
In one paragraph

Article in Journal of imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
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

15 authors.

Mio AdachiDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.ORCID 0000-0002-9359-0972
Tomoyuki FujiokaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Toshiyuki IshibaDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.ORCID 0000-0002-8709-4972
Miyako NaraOhtsuka Breast Care Clinic, Tokyo 121-0813, Japan.
Sakiko MaruyaDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Kumiko HayashiDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Yuichi KumakiDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Emi YamagaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.ORCID 0000-0002-5931-6507
Leona KatsutaDepartment of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Du HaoSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore.
Mikael HartmanSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore.
Feng MenglingSaw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore.ORCID 0000-0002-5338-6248
Goshi OdaDepartment of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.
Kazunori KubotaDepartment of Radiology, Dokkyo Medical University Saitama Medical Center, Saitama 343-8555, Japan.ORCID 0000-0002-3240-4910
Ukihide TateishiDepartment of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan.

Funding

Japan Society for the Promotion of Science 120239002
6 · The paper itself

Abstract

Although several studies have been conducted on artificial intelligence (AI) use in mammography (MG), there is still a paucity of research on the diagnosis of metachronous bilateral breast cancer (BC), which is typically more challenging to diagnose. This study aimed to determine whether AI could enhance BC detection, achieving earlier or more accurate diagnoses than radiologists in cases of metachronous contralateral BC. We included patients who underwent unilateral BC surgery and subsequently developed contralateral BC. This retrospective study evaluated the AI-supported MG diagnostic system called FxMammo™. We evaluated the capability of FxMammo™ (FathomX Pte Ltd., Singapore) to diagnose BC more accurately or earlier than radiologists' assessments. This evaluation was supplemented by reviewing MG readings made by radiologists. Out of 1101 patients who underwent surgery, 10 who had initially undergone a partial mastectomy and later developed contralateral BC were analyzed. The AI system identified malignancies in six cases (60%), while radiologists identified five cases (50%). Notably, two cases (20%) were diagnosed solely by the AI system. Additionally, for these cases, the AI system had identified malignancies a year before the conventional diagnosis. This study highlights the AI system's effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer earlier than radiological assessments.

Indexed as

artificial intelligencebreast cancermammographymetachronous contralateral breast cancer

Identifiers

PMID39330431
PMCPMC11432939

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Registered trials

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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.