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AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer.

Authors :
Adachi, Mio
Fujioka, Tomoyuki
Ishiba, Toshiyuki
Nara, Miyako
Maruya, Sakiko
Hayashi, Kumiko
Kumaki, Yuichi
Yamaga, Emi
Katsuta, Leona
Hao, Du
Hartman, Mikael
Mengling, Feng
Oda, Goshi
Kubota, Kazunori
Tateishi, Ukihide
Source :
Journal of Imaging; Sep2024, Vol. 10 Issue 9, p211, 13p
Publication Year :
2024

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2313433X
Volume :
10
Issue :
9
Database :
Complementary Index
Journal :
Journal of Imaging
Publication Type :
Academic Journal
Accession number :
180017311
Full Text :
https://doi.org/10.3390/jimaging10090211