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.
Assessment of Ki67 in Breast Cancer: Recommendations from the International Ki67 in Breast Cancer Working Group
20111.5k citationsTorsten O. Nielsen, Roger A’Hern et al.JNCI Journal of the National Cancer Instituteprofile →
Circulating Tumor Cells: A Novel Prognostic Factor for Newly Diagnosed Metastatic Breast Cancer
2005857 citationsMassimo Cristofanilli, Daniel F. Hayes et al.Journal of Clinical Oncologyprofile →
Circulating Tumor Cells versus Imaging—Predicting Overall Survival in Metastatic Breast Cancer
2006594 citationsG. Thomas Budd, Massimo Cristofanilli et al.Clinical Cancer Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mathew J. Ellis
Since
Specialization
Citations
This map shows the geographic impact of Mathew J. Ellis'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 Mathew J. Ellis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathew J. Ellis more than expected).
This network shows the impact of papers produced by Mathew J. Ellis. 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 Mathew J. Ellis. The network helps show where Mathew J. Ellis may publish in the future.
Co-authorship network of co-authors of Mathew J. Ellis
This figure shows the co-authorship network connecting the top 25 collaborators of Mathew J. Ellis.
A scholar is included among the top collaborators of Mathew J. Ellis 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 Mathew J. Ellis. Mathew J. Ellis is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Nielsen, Torsten O., Roger A’Hern, R. Charles Coombes, et al.. (2011). Assessment of Ki67 in Breast Cancer: Recommendations from the International Ki67 in Breast Cancer Working Group. JNCI Journal of the National Cancer Institute. 103(22). 1656–1664.1488 indexed citations breakdown →
Budd, G. Thomas, Massimo Cristofanilli, Mathew J. Ellis, et al.. (2006). Circulating Tumor Cells versus Imaging—Predicting Overall Survival in Metastatic Breast Cancer. Clinical Cancer Research. 12(21). 6403–6409.594 indexed citations breakdown →
6.
Cristofanilli, Massimo, Daniel F. Hayes, G. Thomas Budd, et al.. (2005). Circulating Tumor Cells: A Novel Prognostic Factor for Newly Diagnosed Metastatic Breast Cancer. Journal of Clinical Oncology. 23(7). 1420–1430.857 indexed citations breakdown →
7.
Hayes, Daniel F., G. Thomas Budd, Mathew J. Ellis, et al.. (2005). Cristofanilli M, Hayes DF, Budd GT et al.Circulating tumor cells: a novel prognostic factor for newly diagnosed metastatic breast cancer. J Clin Oncol 23:1420-30.2 indexed citations
Stearns, Vered, Baljit Singh, Theodore N. Tsangaris, et al.. (2003). A prospective randomized pilot study to evaluate predictors of response in serial core biopsies to single agent neoadjuvant doxorubicin or paclitaxel for patients with locally advanced breast cancer.. PubMed. 9(1). 124–33.84 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.