Penny J. Barnes

1.5k total citations
45 papers, 874 citations indexed

About

Penny J. Barnes is a scholar working on Oncology, Cancer Research and Pathology and Forensic Medicine. According to data from OpenAlex, Penny J. Barnes has authored 45 papers receiving a total of 874 indexed citations (citations by other indexed papers that have themselves been cited), including 26 papers in Oncology, 14 papers in Cancer Research and 10 papers in Pathology and Forensic Medicine. Recurrent topics in Penny J. Barnes's work include HER2/EGFR in Cancer Research (15 papers), Breast Cancer Treatment Studies (14 papers) and Breast Lesions and Carcinomas (10 papers). Penny J. Barnes is often cited by papers focused on HER2/EGFR in Cancer Research (15 papers), Breast Cancer Treatment Studies (14 papers) and Breast Lesions and Carcinomas (10 papers). Penny J. Barnes collaborates with scholars based in Canada, United States and Norway. Penny J. Barnes's co-authors include Daniel Rayson, Robert G. Boutilier, Tallal Younis, Gillian Bethune, Neil A. Hagen, Judy Caines, Penelope M. A. Brasher, Susan Weaver, Dorcas Fulton and Lisa M. DeAngelis and has published in prestigious journals such as Journal of Clinical Oncology, Cancer and The American Journal of Surgical Pathology.

In The Last Decade

Penny J. Barnes

43 papers receiving 842 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Penny J. Barnes Canada 18 349 288 249 178 166 45 874
Merlın Hamre United States 18 425 1.2× 173 0.6× 212 0.9× 303 1.7× 105 0.6× 29 1.1k
Christopher McNamara United Kingdom 15 221 0.6× 116 0.4× 322 1.3× 136 0.8× 56 0.3× 37 732
David Topolsky United States 15 348 1.0× 127 0.4× 141 0.6× 123 0.7× 49 0.3× 39 884
Sue A. Bartow United States 18 406 1.2× 330 1.1× 298 1.2× 248 1.4× 133 0.8× 25 1.1k
Timothy P. Spiro United States 14 289 0.8× 161 0.6× 122 0.5× 132 0.7× 79 0.5× 32 700
Jeanette Dupont Jensen Denmark 17 418 1.2× 351 1.2× 77 0.3× 219 1.2× 30 0.2× 31 838
Etsuyo Ogo Japan 14 213 0.6× 150 0.5× 93 0.4× 208 1.2× 31 0.2× 69 534
Bimal C. Ghosh United States 17 328 0.9× 63 0.2× 272 1.1× 226 1.3× 142 0.9× 74 1.1k
Caroline Seynaeve Netherlands 16 570 1.6× 378 1.3× 334 1.3× 353 2.0× 30 0.2× 24 1.1k
G. Walsh United Kingdom 20 827 2.4× 783 2.7× 379 1.5× 289 1.6× 78 0.5× 39 1.5k

Countries citing papers authored by Penny J. Barnes

Since Specialization
Citations

This map shows the geographic impact of Penny J. Barnes'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 Penny J. Barnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Penny J. Barnes more than expected).

Fields of papers citing papers by Penny J. Barnes

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Penny J. Barnes. 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 Penny J. Barnes. The network helps show where Penny J. Barnes may publish in the future.

Co-authorship network of co-authors of Penny J. Barnes

This figure shows the co-authorship network connecting the top 25 collaborators of Penny J. Barnes. A scholar is included among the top collaborators of Penny J. Barnes 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 Penny J. Barnes. Penny J. Barnes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Akbarnejad, Amir, Nilanjan Ray, Penny J. Barnes, & Gilbert Bigras. (2025). Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images. Applied immunohistochemistry & molecular morphology. 33(3). 131–141. 2 indexed citations
2.
Rasmussen, Sean A., Valerie Jones Taylor, Alexi P. Surette, Penny J. Barnes, & Gillian Bethune. (2022). Using Deep Learning to Predict Final HER2 Status in Invasive Breast Cancers That are Equivocal (2+) by Immunohistochemistry. Applied immunohistochemistry & molecular morphology. 30(10). 668–673. 7 indexed citations
3.
Bharadwaj, Alamelu G., Rong‐Zong Liu, Lynn N. Thomas, et al.. (2020). S100A10 Has a Critical Regulatory Function in Mammary Tumor Growth and Metastasis: Insights Using MMTV-PyMT Oncomice and Clinical Patient Sample Analysis. Cancers. 12(12). 3673–3673. 11 indexed citations
4.
Bethune, Gillian, et al.. (2020). Cystic neutrophilic granulomatous mastitis – a review of 12 consecutive cases. Histopathology. 77(5). 781–787. 15 indexed citations
5.
Bethune, Gillian, et al.. (2019). Isolated Tumor Cells in Sentinel Lymph Nodes of Primary Invasive Breast Carcinoma: A Cohort Analysis. Clinical Breast Cancer. 19(4). 286–291. 2 indexed citations
6.
Slodkowska, Elzbieta, Bin Xu, Zuzana Kos, et al.. (2019). Predictors of Outcome in Mammary Adenoid Cystic Carcinoma. The American Journal of Surgical Pathology. 44(2). 214–223. 30 indexed citations
7.
Rayson, Daniel, et al.. (2018). Impact of Detection Method and Age on Survival Outcomes in Triple-Negative Breast Cancer: A Population-Based Cohort Analysis. Clinical Breast Cancer. 18(5). e955–e960. 6 indexed citations
8.
Yen, Peggy P.W., et al.. (2015). Benign and Malignant Male Breast Diseases: Radiologic and Pathologic Correlation. Canadian Association of Radiologists Journal. 66(3). 198–207. 22 indexed citations
10.
Hanna, Wedad, Penny J. Barnes, Richard Berendt, et al.. (2012). Testing for HER2 in Breast Cancer: Current Pathology Challenges Faced in Canada. Current Oncology. 19(6). 315–323. 10 indexed citations
11.
Lee, Tsu‐Yee Joseph, et al.. (2010). Flat Epithelial Atypia on Breast Needle Core Biopsy: A Retrospective Study with Clinical-Pathological Correlation. The Breast Journal. 16(4). no–no. 25 indexed citations
12.
Ly, Thai Yen, et al.. (2010). Fine‐needle aspiration cytology of mammary fibroadenoma: A comparison of ThinPrep® and cytospin preparations. Diagnostic Cytopathology. 39(3). 181–187. 4 indexed citations
13.
Younis, Tallal, et al.. (2009). Survivin and COX-2 expression in male breast carcinoma. The Breast. 18(4). 228–232. 11 indexed citations
14.
Barnes, Penny J., et al.. (2007). Acinar Pattern of Mammary Paget?s Disease: A Case Report. The Breast Journal. 13(5). 520–526. 6 indexed citations
15.
Barnes, Penny J., et al.. (2005). Erdheim-Chester Disease of the Breast: A Case Report and Review of the Literature. The Breast Journal. 11(6). 462–467. 21 indexed citations
16.
Potvin, Kylea, et al.. (2005). Patterns of trastuzumab use and cost in a single Canadian cancer institute. Journal of Clinical Oncology. 23(16_suppl). 6070–6070. 7 indexed citations
17.
Barnes, Penny J., et al.. (2005). Ductal carcinoma in situ in core biopsies containing invasive breast cancer: correlation with extensive intraductal component and lumpectomy margins. Journal of Surgical Oncology. 90(2). 71–76. 36 indexed citations
18.
Forsyth, Peter, Susan Weaver, Dorcas Fulton, et al.. (2003). Prophylactic Anticonvulsants in Patients with Brain Tumour. Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques. 30(2). 106–112. 117 indexed citations
19.
Robinson, John W., et al.. (1999). Quality‐of‐life outcomes for men treated with cryosurgery for localized prostate carcinoma. Cancer. 86(9). 1793–1801.
20.
Robinson, John W., et al.. (1999). Quality-of-life outcomes for men treated with cryosurgery for localized prostate carcinoma. Cancer. 86(9). 1793–1801. 29 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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