Browsing by author "Marinovich, M Luke"
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Artificial intelligence (AI) to enhance breast cancer screening: protocol for population-based cohort study of cancer detection
Marinovich, M Luke; Wylie, Elizabeth Jane; Lotter, William; Pearce, Alison; Carter, Stacy M; Lund, Helen; Waddell, Andrew; Kim, Jiye G; Pereira, Gavin F; Lee, Christoph I; Zackrisson, Sophia; Brennan, Meagan; Houssami, NehmatPublished 2022Introduction Artificial intelligence (AI) algorithms for interpreting mammograms have the potential to improve the effectiveness of population breast cancer screening programmes if they can detect cancers, including interval ...Article -
Differential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysis
Li, Tong; Houssami, Nehmat; Noguchi, Naomi; Zeng, Aileen; Marinovich, M LukePublished 2022Background We examined whether digital breast tomosynthesis (DBT) detects differentially in high- or low-density screens. Methods We searched six databases (2009–2020) for studies comparing DBT and digital mammography ...Article -
Evidence from a BreastScreen cohort does not support a longer inter-screen interval in women who have no conventional risk factors for breast cancer
Noguchi, Naomi; Marinovich, M Luke; Wylie, Elizabeth Jane; Lund, Helen; Houssami, NehmatPublished 2022Objectives: To determine screening outcomes in women who have no recorded risk factors for breast cancer. Methods: A retrospective population-based cohort study included all 1,026,137 mammography screening episodes in ...Article -
Independent External Validation of Artificial Intelligence Algorithms for Automated Interpretation of Screening Mammography: A Systematic Review
Anderson, Anna W; Marinovich, M Luke; Houssami, Nehmat; Lowry, Kathryn P; Elmore, Joann G; Buist, Diana S M; Hofvind, Solveig; Lee, Christoph IPublished 2022Purpose: The aim of this study was to describe the current state of science regarding independent external validation of artificial intelligence (AI) technologies for screening mammography. Methods: A systematic review ...Article -
Rates of reoperation after breast conserving cancer surgery in Western Australia before and after publication of the SSO-ASTRO margins guideline
Marinovich, M Luke; Saunders, Christobel; Pereira, Gavin; Houssami, NehmatPublished 2023Background A 2014 SSO-ASTRO guideline on surgical margins aimed to reduce unnecessary reoperation after breast conserving surgery (BCS). We investigate whether publication of the guideline was associated with a reduction ...Article -
A systematic review of the impact of the COVID-19 pandemic on breast cancer screening and diagnosis
Li, Tong; Nickel, Brooke; Ngo, Preston; McFadden, Kathleen; Brennan, Meagan; Marinovich, M Luke; Houssami, NehmatPublished 2023Background Breast cancer care has been affected by the COVID-19 pandemic. This systematic review aims to describe the observed pandemic-related changes in clinical and health services outcomes for breast screening and ...Article -
Two-year follow-up of participants in the BreastScreen Victoria pilot trial of tomosynthesis versus mammography: breast density-stratified screening outcomes
Houssami, Nehmat; Lockie, Darren; Giles, Michelle; Noguchi, Naomi; Marr, Georgina; Marinovich, M LukePublished 2023This follow-up study of BreastScreen Victoria’s pilot trial of digital breast tomosynthesis aimed to report interval cancer rates, screening sensitivity, and density-stratified outcomes for tomosynthesis vs mammography ...Open AccessArticle
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