AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer

Author:

Adachi Mio1ORCID,Fujioka Tomoyuki2,Ishiba Toshiyuki1ORCID,Nara Miyako3,Maruya Sakiko1,Hayashi Kumiko1,Kumaki Yuichi1,Yamaga Emi2,Katsuta Leona2,Hao Du4,Hartman Mikael456,Mengling Feng46ORCID,Oda Goshi1,Kubota Kazunori7ORCID,Tateishi Ukihide2

Affiliation:

1. Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan

2. Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan

3. Ohtsuka Breast Care Clinic, Tokyo 121-0813, Japan

4. Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore

5. Department of Surgery, National University Hospital, National University Health System, Singapore 119074, Singapore

6. Institute of Data Science, National University of Singapore, Singapore 117597, Singapore

7. Department of Radiology, Dokkyo Medical University Saitama Medical Center, Saitama 343-8555, Japan

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.

Funder

Japan Society for the Promotion of Science

Publisher

MDPI AG

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