{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T06:49:32Z","timestamp":1791528572873,"version":"4.3.4"},"reference-count":39,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T00:00:00Z","timestamp":1791417600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100011050","name":"Sydney Southeast Asia Centre, University of Sydney","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100011050","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001026","name":"National Breast Cancer Foundation","doi-asserted-by":"publisher","award":["IIRS\u201022\u2010087"],"award-info":[{"award-number":["IIRS\u201022\u2010087"]}],"id":[{"id":"10.13039\/501100001026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Asia-Pac J Clncl Oncology"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Population\u2010based screening in Australia for breast cancer using mammography has delivered key outcomes in reducing deaths, but such programs are usually not present in Vietnam. Breast cancer is common in both countries, with Vietnamese women having high breast density and Vietnam having low radiology expertise. This paper investigated the performance of two state\u2010of\u2010the\u2010art Artificial Intelligence (AI) models for cancer detection on Datasets 1 and 2 (Vietnam and Australian mammographic cases) and determined whether the breast density in the two datasets with different vendors affected the AI performance. Both datasets consisted of the same number of mammographic cases (865 malignant; 865 normal). Mammographic images were enhanced using the contrast\u2010limited adaptive histogram equalization algorithm. Transfer learning of the Globally\u2010aware Multiple Instance Classifier (GMIC) and Global\u2013Local Activation Maps (GLAM) AI models was conducted on enhanced images in the two national datasets. The breast density was classified as four levels (A, B, C, and D) by three radiologists. We combined Levels A and B into 0%\u201350%, and Levels C and D into 50%\u2013100% breast density. The performance of these two AI models, both with and without transfer learning, was evaluated on the two datasets using McNemar's test. The GMIC in the pre\u2010trained and transfer learning modes outperformed the GLAM in terms of specificity and sensitivity in the two datasets. The performance of the GMIC and GLAM with transfer learning in Dataset 2 was better, with 86.7% specificity and 88.2% sensitivity, outperforming their transfer learning model with 80.4% specificity and 81.2% sensitivity in Dataset 1. The specificity and sensitivity of the two AI models (\n                    <jats:italic>p<\/jats:italic>\n                    \u2010values &lt;0.05) were significantly improved using transfer learning in the two datasets. The specificity and sensitivity of GMIC with transfer learning (\n                    <jats:italic>p<\/jats:italic>\n                    \u2010values &lt;0.05) on the 0%\u201350% and 50%\u2013100% breast density in Dataset 2 were significantly higher than those in Dataset 1, respectively.\n                  <\/jats:p>","DOI":"10.1111\/ajco.70203","type":"journal-article","created":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:59:04Z","timestamp":1791525544000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Investigating Artificial Intelligence Performance on Mammographic Cases in High\u2010 and Low\u2010Resourced Countries"],"prefix":"10.1111","author":[{"given":"Zhengqiang","family":"Jiang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering Suzhou University of Technology  Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Phuong D.","family":"Trieu","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health The University of Sydney  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa L.","family":"Barron","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health The University of Sydney  Sydney Australia"},{"name":"School of Health Sciences Western Sydney University  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sarah J.","family":"Lewis","sequence":"additional","affiliation":[{"name":"Faculty of Medicine and Health The University of Sydney  Sydney Australia"},{"name":"School of Health Sciences Western Sydney University  Sydney Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,8]]},"reference":[{"key":"e_1_2_11_2_1","volume-title":"Global Cancer Observatory: Cancer Today","author":"Ferlay J.","year":"2024"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMsr1504363"},{"key":"e_1_2_11_4_1","unstructured":"E.Bj\u00f8rnson \u00c5.Holen S.Sagstad et\u00a0al. \u201cBreastScreen Norway: 25 Years of Organized Screening \u201d Cancer Registry of Norway (2022)."},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.7326\/0003\u20104819\u2010146\u20107\u2010200704030\u201000006"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/ajco.70033"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101908"},{"key":"e_1_2_11_8_1","first-page":"268","article-title":"Weakly\u2010supervised High\u2010resolution Segmentation of Mammography Images for Breast Cancer Diagnosis","volume":"143","author":"Liu K.","year":"2021","journal-title":"Proceedings of Machine Learning Research"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230375"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.232479"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jmir.2022.10.078"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.10.2.025502"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1117\/12.2655182"},{"key":"e_1_2_11_14_1","volume-title":"The NYU Breast Cancer Screening Dataset v1.0","author":"Wu N.","year":"2019"},{"key":"e_1_2_11_15_1","first-page":"1","article-title":"Comprehensive Survey on Transfer Learning","author":"Zhuang F.","year":"2019","journal-title":"arXiv"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41597\u2010023\u201002100\u20107"},{"key":"e_1_2_11_17_1","volume-title":"ACR BI\u2010RADS Atlas: Breast Imaging Reporting and Data System","author":"American College of Radiology","year":"2013"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrai.2025.100055"},{"key":"e_1_2_11_19_1","doi-asserted-by":"crossref","unstructured":"K.He X.Zhang S.Ren andJ.Sun \u201cDeep Residual Learning for Image Recognition \u201d inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition(2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_11_20_1","unstructured":"D. P.KingmaandJ. A.Ba \u201cA Method for Stochastic Optimization \u201d in International Conference on Learning Representations (2015)."},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2979450"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfa.2013.05.001"},{"key":"e_1_2_11_23_1","first-page":"474","volume-title":"Graphics Gems IV","author":"Karel Z.","year":"1994"},{"key":"e_1_2_11_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02295996"},{"key":"e_1_2_11_25_1","doi-asserted-by":"publisher","DOI":"10.2307\/2531595"},{"key":"e_1_2_11_26_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2533082308"},{"key":"e_1_2_11_27_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180071"},{"key":"e_1_2_11_28_1","doi-asserted-by":"publisher","DOI":"10.31557\/APJCP.2019.20.3.727"},{"key":"e_1_2_11_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2021.04.004"},{"key":"e_1_2_11_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radi.2021.07.016"},{"key":"e_1_2_11_31_1","unstructured":"Australian Government Department of Health Disability and Aging.BreastScreen Australia National Accreditation Standards (NAS)(2021)."},{"key":"e_1_2_11_32_1","unstructured":"https:\/\/www.bcrf.org\/about\u2010breast\u2010cancer\/screening\u2010vs\u2010diagnostic\u2010mammogram\/."},{"key":"e_1_2_11_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2018.11.012"},{"key":"e_1_2_11_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330\u2010024\u201010951\u2010w"},{"key":"e_1_2_11_35_1","doi-asserted-by":"publisher","DOI":"10.1186\/s43055\u2010023\u201001129\u20103"},{"key":"e_1_2_11_36_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.33138"},{"key":"e_1_2_11_37_1","doi-asserted-by":"publisher","DOI":"10.1067\/j.cpradiol.2024.10.012"},{"key":"e_1_2_11_38_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1600249"},{"key":"e_1_2_11_39_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers16020322"},{"key":"e_1_2_11_40_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers17233878"}],"container-title":["Asia-Pacific Journal of Clinical Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ajco.70203","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/ajco.70203","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ajco.70203","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T05:59:06Z","timestamp":1791525546000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ajco.70203"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,8]]},"references-count":39,"alternative-id":["10.1111\/ajco.70203"],"URL":"https:\/\/doi.org\/10.1111\/ajco.70203","archive":["Portico"],"relation":{},"ISSN":["1743-7555","1743-7563"],"issn-type":[{"value":"1743-7555","type":"print"},{"value":"1743-7563","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,8]]},"assertion":[{"value":"2026-01-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"ajco.70203"}}