M. Luke Marinovich

5.1k total citations · 1 hit paper
65 papers, 3.4k citations indexed

About

M. Luke Marinovich is a scholar working on Cancer Research, Oncology and Artificial Intelligence. According to data from OpenAlex, M. Luke Marinovich has authored 65 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Cancer Research, 25 papers in Oncology and 23 papers in Artificial Intelligence. Recurrent topics in M. Luke Marinovich's work include Breast Cancer Treatment Studies (27 papers), Global Cancer Incidence and Screening (22 papers) and AI in cancer detection (22 papers). M. Luke Marinovich is often cited by papers focused on Breast Cancer Treatment Studies (27 papers), Global Cancer Incidence and Screening (22 papers) and AI in cancer detection (22 papers). M. Luke Marinovich collaborates with scholars based in Australia, United States and Norway. M. Luke Marinovich's co-authors include Nehmat Houssami, Petra Macaskill, Monica Morrow, Meagan Brennan, Gϋnter von Minckwitz, Eleftherios P. Mamounas, Les Irwig, Lawrence J. Solin, Stefano Ciatto and Francesco Sardanelli and has published in prestigious journals such as Journal of Clinical Oncology, PLoS ONE and JNCI Journal of the National Cancer Institute.

In The Last Decade

M. Luke Marinovich

60 papers receiving 3.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

The Association of Surgical Margins and Local Recurrence in Women with Early-Stage Invasive Breast Cancer Treated with Breast-Conserving Therapy: A Meta-Analysis

2014 349 citations Nehmat Houssami, Petra Macaskill et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
M. Luke Marinovich Australia	25	2.1k	1.4k	1.0k	998	809	65	3.4k
Marjolein L. Smidt Netherlands	40	2.3k 1.1×	1.6k 1.1×	1.1k 1.1×	1.4k 1.4×	943 1.2×	140	4.0k
Joerg Heil Germany	30	1.4k 0.7×	739 0.5×	846 0.8×	526 0.5×	582 0.7×	101	2.5k
Madeleine M.A. Tilanus‐Linthorst Netherlands	28	1.5k 0.7×	1.2k 0.9×	1.3k 1.2×	1.1k 1.1×	219 0.3×	61	3.9k
Mary S. Newell United States	28	856 0.4×	755 0.5×	1.1k 1.1×	915 0.9×	243 0.3×	72	2.7k
Cecile T.M. Brekelmans Netherlands	27	1.6k 0.7×	1.2k 0.8×	1.3k 1.2×	790 0.8×	264 0.3×	58	4.2k
Kyung Hwan Shin South Korea	33	1.5k 0.7×	833 0.6×	1.0k 1.0×	605 0.6×	930 1.1×	161	3.3k
Florentia Peintinger Austria	26	2.5k 1.2×	1.2k 0.9×	1.9k 1.8×	658 0.7×	818 1.0×	59	3.7k
Charles Coutant France	34	1.6k 0.7×	723 0.5×	1.5k 1.4×	413 0.4×	821 1.0×	205	4.4k
James Geraghty Ireland	28	2.1k 1.0×	1.6k 1.1×	1.0k 1.0×	270 0.3×	1.4k 1.7×	102	3.5k
Linda D. Bosserman United States	24	1.1k 0.5×	740 0.5×	1.8k 1.7×	527 0.5×	711 0.9×	89	3.3k

Countries citing papers authored by M. Luke Marinovich

Since Specialization

Citations

This map shows the geographic impact of M. Luke Marinovich's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Luke Marinovich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Luke Marinovich more than expected).

Fields of papers citing papers by M. Luke Marinovich

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Luke Marinovich. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Luke Marinovich. The network helps show where M. Luke Marinovich may publish in the future.

Co-authorship network of co-authors of M. Luke Marinovich

This figure shows the co-authorship network connecting the top 25 collaborators of M. Luke Marinovich. A scholar is included among the top collaborators of M. Luke Marinovich based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Luke Marinovich. M. Luke Marinovich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Nickel, Brooke, M. Luke Marinovich, David P. Smith, et al.. (2025). Are AI chatbots concordant with evidence-based cancer screening recommendations?. Patient Education and Counseling. 134. 108677–108677. 3 indexed citations

Rana, Md. Sohel, Md Mijanur Rahman, Jannah Baker, et al.. (2025). Association of lifestyle factors with breast cancer incidence: An overview of systematic reviews. Chinese Journal of Cancer Research. 37(4). 575–591.

Marinovich, M. Luke, Nehmat Houssami, Andrew J. Spillane, et al.. (2025). Changes in patient management after preoperative MRI for newly diagnosed breast cancer: a multicentre prospective observational study. The Medical Journal of Australia. 223(11). 602–610.

Noguchi, Naomi, et al.. (2025). Risk Models to Predict Screen-Detected and Interval Breast Cancers in Population Mammography Screening Participants. Cancers. 17(5). 810–810. 1 indexed citations

Li, Tong, Jennifer Isautier, Janie M. Lee, M. Luke Marinovich, & Nehmat Houssami. (2024). Performance of Digital Breast Tomosynthesis Versus Digital Mammography in Women With a Family History of Breast Cancer: A Systematic Review. Clinical Breast Cancer. 25(2). e103–e112. 1 indexed citations

Baker, Jannah, Naomi Noguchi, M. Luke Marinovich, et al.. (2024). Atypical ductal or lobular hyperplasia, lobular carcinoma in-situ, flat epithelial atypia, and future risk of developing breast cancer: Systematic review and meta-analysis. The Breast. 78. 103807–103807. 1 indexed citations

Houssami, Nehmat, et al.. (2024). Frequency and characteristics of errors by artificial intelligence (AI) in reading screening mammography: a systematic review. Breast Cancer Research and Treatment. 207(1). 1–13. 8 indexed citations

Li, Tong, et al.. (2024). Factors associated with private or public breast cancer screening attendance in Queensland, Australia: A retrospective cross-sectional study. Journal of Medical Screening. 31(4). 258–262.

Marinovich, M. Luke, Elizabeth Wylie, William Lotter, et al.. (2023). Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection. EBioMedicine. 90. 104498–104498. 41 indexed citations

10.

Li, Tong, Brooke Nickel, Kathleen L. McFadden, et al.. (2023). A systematic review of the impact of the COVID-19 pandemic on breast cancer screening and diagnosis. The Breast. 67. 78–88. 55 indexed citations

11.

Li, Tong, et al.. (2022). Differential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysis. British Journal of Cancer. 127(1). 116–125. 16 indexed citations

12.

Wright, Cameron, Rachael Moorin, Christobel Saunders, et al.. (2021). Cost-effectiveness of radioguided occult lesion localization using 125I seeds versus hookwire localization before breast-conserving surgery for non-palpable breast cancer. British journal of surgery. 108(7). 843–850. 8 indexed citations

13.

Noguchi, Naomi, M. Luke Marinovich, Elizabeth Wylie, Helen Lund, & Nehmat Houssami. (2021). Screening outcomes by risk factor and age: evidence from BreastScreen WA for discussions of risk‐stratified population screening. The Medical Journal of Australia. 215(8). 359–365. 13 indexed citations

14.

Fancellu, Alessandro, et al.. (2021). Outcomes after breast-conserving surgery or mastectomy in patients with triple-negative breast cancer: meta-analysis. British journal of surgery. 108(7). 760–768. 33 indexed citations

15.

Houssami, Nehmat, Gemma Jacklyn, M. Luke Marinovich, et al.. (2020). Impact of Full-Field Digital Mammography Versus Film-Screen Mammography in Population Screening: A Meta-Analysis. JNCI Journal of the National Cancer Institute. 113(1). 16–26. 24 indexed citations

16.

Malacova, Eva, Helen D. Bailey, Kevin Chai, et al.. (2020). Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015. Scientific Reports. 10(1). 5354–5354. 35 indexed citations

17.

Marinovich, M. Luke, Annette K. Regan, Mika Gissler, et al.. (2019). Developing evidence-based recommendations for optimal interpregnancy intervals in high-income countries: protocol for an international cohort study. BMJ Open. 9(1). e027941–e027941. 14 indexed citations

18.

Gebremedhin, Amanuel Tesfay, Annette K. Regan, Stephen Ball, et al.. (2019). Effect of interpregnancy interval on gestational diabetes: a retrospective matched cohort study. Duo Research Archive (University of Oslo). 1 indexed citations

19.

Houssami, Nehmat, Petra Macaskill, Gϋnter von Minckwitz, M. Luke Marinovich, & Eleftherios P. Mamounas. (2012). Meta-analysis of the association of breast cancer subtype and pathologic complete response to neoadjuvant chemotherapy. European Journal of Cancer. 48(18). 3342–3354. 383 indexed citations

20.

Brennan, Meagan, Robin Turner, Stefano Ciatto, et al.. (2011). Ductal Carcinoma in Situ at Core-Needle Biopsy: Meta-Analysis of Underestimation and Predictors of Invasive Breast Cancer. Radiology. 260(1). 119–128. 286 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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