{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:24:25Z","timestamp":1760145865382,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Japan Society for the Promotion of Science","award":["1235688"],"award-info":[{"award-number":["1235688"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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\u2122. We evaluated the capability of FxMammo\u2122 (FathomX Pte Ltd., Singapore) to diagnose BC more accurately or earlier than radiologists\u2019 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\u2019s effectiveness in diagnosing metachronous contralateral BC via MG. In some cases, the AI system consistently diagnosed cancer earlier than radiological assessments.<\/jats:p>","DOI":"10.3390\/jimaging10090211","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T03:57:06Z","timestamp":1724817426000},"page":"211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI Use in Mammography for Diagnosing Metachronous Contralateral Breast Cancer"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9359-0972","authenticated-orcid":false,"given":"Mio","family":"Adachi","sequence":"first","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tomoyuki","family":"Fujioka","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-4972","authenticated-orcid":false,"given":"Toshiyuki","family":"Ishiba","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miyako","family":"Nara","sequence":"additional","affiliation":[{"name":"Ohtsuka Breast Care Clinic, Tokyo 121-0813, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sakiko","family":"Maruya","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kumiko","family":"Hayashi","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuichi","family":"Kumaki","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Emi","family":"Yamaga","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leona","family":"Katsuta","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Du","family":"Hao","sequence":"additional","affiliation":[{"name":"Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mikael","family":"Hartman","sequence":"additional","affiliation":[{"name":"Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore"},{"name":"Department of Surgery, National University Hospital, National University Health System, Singapore 119074, Singapore"},{"name":"Institute of Data Science, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5338-6248","authenticated-orcid":false,"given":"Feng","family":"Mengling","sequence":"additional","affiliation":[{"name":"Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 119074, Singapore"},{"name":"Institute of Data Science, National University of Singapore, Singapore 117597, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Goshi","family":"Oda","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3240-4910","authenticated-orcid":false,"given":"Kazunori","family":"Kubota","sequence":"additional","affiliation":[{"name":"Department of Radiology, Dokkyo Medical University Saitama Medical Center, Saitama 343-8555, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ukihide","family":"Tateishi","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital, Tokyo 113-8510, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.breast.2016.10.004","article-title":"Breast Cancer Diagnosis and Mortality by Tumor Stage and Migration Background in a Nationwide Cohort Study in Sweden","volume":"31","author":"Abdoli","year":"2017","journal-title":"Breast"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"367","DOI":"10.3961\/jpmph.16.054","article-title":"Breast Density and Risk of Breast Cancer in Asian Women: A Meta-Analysis of Observational Studies","volume":"49","author":"Bae","year":"2016","journal-title":"J. Prev. Med. Public Health"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1186\/s13058-015-0525-z","article-title":"Benefits and Harms of Mammography Screening","volume":"17","author":"Lousdal","year":"2015","journal-title":"Breast Cancer Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3000","DOI":"10.1016\/j.ejca.2009.08.007","article-title":"The Value of Surveillance Mammography of the Contralateral Breast in Patients with a History of Breast Cancer","volume":"45","author":"Lu","year":"2009","journal-title":"Eur. J. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2331","DOI":"10.1016\/j.ejca.2005.01.029","article-title":"Risks of Second Primary Breast and Urogenital Cancer Following Female Breast Cancer in the South of The Netherlands, 1972\u20132001","volume":"41","author":"Soerjomataram","year":"2005","journal-title":"Eur. J. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1002\/bjs.1800710924","article-title":"Bilateral Primary Breast Cancer: A Prospective Study of Disease Incidence","volume":"71","author":"Chaudary","year":"1984","journal-title":"Br. J. Surg."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4210","DOI":"10.1200\/JCO.2006.10.5056","article-title":"Incidence and Prognosis of Synchronous and Metachronous Bilateral Breast Cancer","volume":"25","author":"Hartman","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2484","DOI":"10.1007\/s00330-011-2226-z","article-title":"Surveillance Mammography for Detecting Ipsilateral Breast Tumour Recurrence and Metachronous Contralateral Breast Cancer: A Systematic Review","volume":"21","author":"Robertson","year":"2011","journal-title":"Eur. Radiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1080","DOI":"10.1200\/JCO.1999.17.3.1080","article-title":"American Society of Clinical Oncology 1998 Update of Recommended Breast Cancer Surveillance Guidelines","volume":"3","author":"Smith","year":"1999","journal-title":"Am. Soc. Clin. Oncol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e61","DOI":"10.3802\/jgo.2022.33.e61","article-title":"Current Status of Hereditary Breast and Ovarian Cancer Practice among Gynecologic Oncologists in Japan: A Nationwide Survey by the Japan Society of Gynecologic Oncology (JSGO)","volume":"33","author":"Kobayashi","year":"2022","journal-title":"J. Gynecol. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1828","DOI":"10.1001\/jamainternmed.2015.5231","article-title":"Diagnostic Accuracy of Digital Screening Mammography with and without Computer-Aided Detection","volume":"175","author":"Lehman","year":"2015","journal-title":"JAMA Intern Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e190208","DOI":"10.1148\/ryai.2020190208","article-title":"Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool","volume":"2","author":"Lopez","year":"2020","journal-title":"Radiol. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1148\/radiol.210948","article-title":"An Artificial Intelligence\u2013Based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload","volume":"304","author":"Lauritzen","year":"2022","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e468","DOI":"10.1016\/S2589-7500(20)30185-0","article-title":"Effect of Artificial Intelligence-Based Triaging of Breast Cancer Screening Mammograms on Cancer Detection and Radiologist Workload: A Retrospective Simulation Study","volume":"2","author":"Dembrower","year":"2020","journal-title":"Lancet Digit. Health"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4825","DOI":"10.1007\/s00330-019-06186-9","article-title":"Can We Reduce the Workload of Mammographic Screening by Automatic Identification of Normal Exams with Artificial Intelligence? A Feasibility Study","volume":"29","author":"Teuwen","year":"2019","journal-title":"Eur. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1148\/radiol.212381","article-title":"Artificial Intelligence Evaluation of 122969 Mammography Examinations from a Population-Based Screening Program","volume":"303","author":"Larsen","year":"2022","journal-title":"Radiology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5940","DOI":"10.1007\/s00330-021-07686-3","article-title":"Can Artificial Intelligence Reduce the Interval Cancer Rate in Mammography Screening?","volume":"31","author":"Hofvind","year":"2021","journal-title":"Eur. Radiol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"110321","DOI":"10.1016\/j.ejrad.2022.110321","article-title":"AI-Based Prevention of Interval Cancers in a National Mammography Screening Program","volume":"152","author":"Byng","year":"2022","journal-title":"Eur. J. Radiol"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"German Consortium for Hereditary Breast and Ovarian Cancer (2002). Comprehensive analysis of 989 patients with breast or ovarian cancer provides BRCA1 and BRCA2 mutation profiles and frequencies for the German population. Int. J. Cancer, 97, 472\u2013480.","DOI":"10.1002\/ijc.1626"},{"key":"ref_20","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Kim, H.E., Cosa-Linan, A., Santhanam, N., Jannesari, M., Maros, M.E., and Ganslandt, T. (2022). Transfer Learning for Medical Image Classification: A Literature Review. BMC Med. Imaging, 22.","DOI":"10.1186\/s12880-022-00793-7"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s10549-017-4137-4","article-title":"Quantification of Masking Risk in Screening Mammography with Volumetric Breast Density Maps","volume":"162","author":"Holland","year":"2017","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1038\/bmt.2012.244","article-title":"Investigation of the Freely Available Easy-to-Use Software \u201cEZR\u201d for Medical Statistics","volume":"48","author":"Kanda","year":"2013","journal-title":"Bone Marrow Transpl."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ranieri, J., Guerra, F., and Di Giacomo, D. (2020). Role of Metacognition Thinking and Psychological Traits in Breast Cancer Survivorship. Behav. Sci., 10.","DOI":"10.3390\/bs10090135"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1007\/s11604-023-01504-0","article-title":"CT and MRI of Abdominal Cancers: Current Trends and Perspectives in the Era of Radiomics and Artificial Intelligence","volume":"42","author":"Barat","year":"2024","journal-title":"Jpn. J. Radiol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1016\/j.diii.2023.06.011","article-title":"Recent Advances in Artificial Intelligence for Cardiac CT: Enhancing Diagnosis and Prognosis Prediction","volume":"104","author":"Tatsugami","year":"2023","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s11604-022-01325-7","article-title":"Radiomics of Locally Advanced Rectal Cancer: Machine Learning-Based Prediction of Response to Neoadjuvant Chemoradiotherapy Using Pre-Treatment Sagittal T2-Weighted MRI","volume":"41","author":"Yardimci","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"401","DOI":"10.2463\/mrms.rev.2023-0047","article-title":"Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging","volume":"22","author":"Fujima","year":"2023","journal-title":"Magn. Reson. Med. Sci."},{"key":"ref_29","first-page":"245","article-title":"Application of Radiomics in Precision Prediction of Diagnosis and Treatment of Gastric Cancer","volume":"41","author":"Du","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/s12149-023-01865-6","article-title":"From FDG and beyond: The Evolving Potential of Nuclear Medicine","volume":"37","author":"Hirata","year":"2023","journal-title":"Ann. Nucl. Med."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1007\/s11604-022-01261-6","article-title":"Deep Learning Method with a Convolutional Neural Network for Image Classification of Normal and Metastatic Axillary Lymph Nodes on Breast Ultrasonography","volume":"40","author":"Ozaki","year":"2022","journal-title":"Jpn. J. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1094","DOI":"10.1007\/s11604-023-01435-w","article-title":"Use of a Deep Learning Algorithm for Non-Mass Enhancement on Breast MRI: Comparison with Radiologists\u2019 Interpretations at Various Levels","volume":"41","author":"Goto","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1007\/s11547-021-01423-y","article-title":"Radiomics in Breast MRI: Current Progress toward Clinical Application in the Era of Artificial Intelligence","volume":"127","author":"Satake","year":"2022","journal-title":"Radiol. Medica"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1007\/s11604-022-01320-y","article-title":"Prediction of Breast Cancer Risk by Automated Volumetric Breast Density Measurement","volume":"41","author":"Nara","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11604-022-01327-5","article-title":"Comparisons between Artificial Intelligence Computer-Aided Detection Synthesized Mammograms and Digital Mammograms When Used Alone and in Combination with Tomosynthesis Images in a Virtual Screening Setting","volume":"41","author":"Uematsu","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e200265","DOI":"10.1001\/jamanetworkopen.2020.0265","article-title":"Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms","volume":"3","author":"Schaffter","year":"2020","journal-title":"JAMA Netw. 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