{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T22:57:38Z","timestamp":1776898658103,"version":"3.51.2"},"reference-count":46,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Medical Radiation Sci"],"published-print":{"date-parts":[[2024,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Breast cancer (BC), the most frequently diagnosed malignancy among women worldwide, presents a public health challenge and affects mortality rates. Breast\u2010conserving therapy (BCT) is a common treatment, but the risk from residual disease necessitates radiotherapy. Digital mammography monitors treatment response by identifying post\u2010operative and radiotherapy tissue alterations, but accurate assessment of mammographic density remains a challenge. This study used OpenBreast to measure percent density (PD), offering insights into changes in mammographic density before and after BCT with radiation therapy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This retrospective analysis included 92 female patients with BC who underwent BCT, chemotherapy, and radiotherapy, excluding those who received hormonal therapy or bilateral BCT. Percent\/percentage density measurements were extracted using OpenBreast, an automated software that applies computational techniques to density analyses. Data were analysed at baseline, 3 months, and 15\u2009months post\u2010treatment using standardised mean difference (SMD) with Cohen's <jats:italic>d<\/jats:italic>, chi\u2010square, and paired sample <jats:italic>t<\/jats:italic>\u2010tests. The predictive power of PD changes for BC was measured based on the receiver operating characteristic (ROC) curve analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The mean age was 53.2\u2009years. There were no significant differences in PD between the periods. Standardised mean difference analysis revealed no significant changes in the SMD for PD before treatment compared with 3\u2010 and 15\u2010months post\u2010treatment. Although PD increased numerically after radiotherapy, ROC analysis revealed optimal sensitivity at 15\u2009months post\u2010treatment for detecting changes in breast density.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This study utilised an automated breast density segmentation tool to assess the changes in mammographic density before and after BC treatment. No significant differences in the density were observed during the short\u2010term follow\u2010up period. However, the results suggest that quantitative density assessment could be valuable for long\u2010term monitoring of treatment effects. The study underscores the necessity for larger and longitudinal studies to accurately measure and validate the effectiveness of quantitative methods in clinical BC management.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmrs.788","type":"journal-article","created":{"date-parts":[[2024,4,4]],"date-time":"2024-04-04T06:35:49Z","timestamp":1712212549000},"page":"375-383","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Breast percent density changes in digital mammography pre\u2010 and post\u2010radiotherapy"],"prefix":"10.1002","volume":"71","author":[{"given":"Sana","family":"Mohammadi","sequence":"first","affiliation":[{"name":"Department of Medicine Hamadan University of Medical Sciences  Hamadan Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1334-8826","authenticated-orcid":false,"given":"Sadegh","family":"Ghaderi","sequence":"additional","affiliation":[{"name":"Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mahdi","family":"Mohammadi","sequence":"additional","affiliation":[{"name":"Department of Medical Physics and Biomedical Engineering, School of Medicine Tehran University of Medical Sciences  Tehran Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamid","family":"Ghaznavi","sequence":"additional","affiliation":[{"name":"Department of Radiology, Faculty of Paramedical Sciences Kurdistan University of Medical Sciences  Sanandaj Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamal","family":"Mohammadian","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Hamadan University of Medical Sciences, Mahdieh Center  Hamadan Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.715"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.405"},{"key":"e_1_2_10_4_1","first-page":"15","article-title":"Current and future burden of breast cancer: Global statistics for 2020 and 2040","volume":"66","author":"Arnold M","year":"2022","journal-title":"Breast Off J Eur Soc Mastology"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/cac2.12207"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.breast.2023.01.008"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.230"},{"key":"e_1_2_10_8_1","first-page":"8","article-title":"Breast edema, from diagnosis to treatment: State of the art","volume":"11","author":"Verbelen H","year":"2021","journal-title":"Arch Phys Ther"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2009.10.5.496"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.487"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2019.108710"},{"key":"e_1_2_10_12_1","doi-asserted-by":"crossref","unstructured":"PertuzS TorresGF TamimiR KamarainenJ.Open framework for mammography\u2010based breast cancer risk assessment. 2019 IEEE EMBS Int Conf Biomed Health Inform BHI 2019 \u2013 Proc.2019.","DOI":"10.1109\/BHI.2019.8834599"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-16141-2"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.4048\/jbc.2023.26.e45"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1117\/1.JMI.5.1.010901"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2021.22810"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4736530"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.567"},{"key":"e_1_2_10_19_1","first-page":"157","volume-title":"In VII Latin American Congress on Biomedical Engineering CLAIB 2016. 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