William Audeh

1.6k total citations
42 papers, 435 citations indexed

About

William Audeh is a scholar working on Cancer Research, Oncology and Genetics. According to data from OpenAlex, William Audeh has authored 42 papers receiving a total of 435 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Cancer Research, 19 papers in Oncology and 16 papers in Genetics. Recurrent topics in William Audeh's work include Breast Cancer Treatment Studies (24 papers), BRCA gene mutations in cancer (13 papers) and Cancer Genomics and Diagnostics (13 papers). William Audeh is often cited by papers focused on Breast Cancer Treatment Studies (24 papers), BRCA gene mutations in cancer (13 papers) and Cancer Genomics and Diagnostics (13 papers). William Audeh collaborates with scholars based in United States, Netherlands and Canada. William Audeh's co-authors include Xiaojiang Cui, Bradley M. Turner, Huina Zhang, David G. Hicks, Armando E. Giuliano, Alice Chung, Heather M. Kling, Shikha Bose, Gordan Srkalović and Hatem Soliman and has published in prestigious journals such as Journal of Clinical Oncology, Cancer Research and Annals of Oncology.

In The Last Decade

William Audeh

30 papers receiving 429 citations

Peers

William Audeh
Lingzhu Wen China
Claudia Capdevila United States
Michelle K. DeMeo United States
Yaning He China
Jayeta Chakrabarti United Kingdom
Michele Bottosso Italy
Epifanio Ruiz United States
Jorma J. de Ronde Netherlands
Denise Treue Germany
Carolina Reduzzi Italy
Lingzhu Wen China
William Audeh
Citations per year, relative to William Audeh William Audeh (= 1×) peers Lingzhu Wen

Countries citing papers authored by William Audeh

Since Specialization
Citations

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

Fields of papers citing papers by William Audeh

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Audeh. 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 William Audeh. The network helps show where William Audeh may publish in the future.

Co-authorship network of co-authors of William Audeh

This figure shows the co-authorship network connecting the top 25 collaborators of William Audeh. A scholar is included among the top collaborators of William Audeh 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 William Audeh. William Audeh 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.
Veer, Laura van ‘t, Elma Meershoek‐Klein Kranenbarg, Marjolijn Duijm‐de Carpentier, et al.. (2024). Selection of Patients With Early-Stage Breast Cancer for Extended Endocrine Therapy. JAMA Network Open. 7(11). e2447530–e2447530. 1 indexed citations
2.
O’Shaughnessy, Joyce, Lajos Pusztai, Cathy Graham, et al.. (2024). Abstract PO5-15-04: MammaPrint index predicts neoadjuvant chemosensitivity in patients with HR+HER2- early-stage breast cancer in the real-world evidence FLEX study. Cancer Research. 84(9_Supplement). PO5–15.
3.
Borges, Virginia F., Elena Shagisultanova, Joyce O’Shaughnessy, et al.. (2024). 32P Genomic risk classification and whole transcriptome analysis of HR+/HER2- post-partum breast cancers: A FLEX sub study. ESMO Open. 9. 103040–103040. 1 indexed citations
4.
Rastogi, Priya, Hanna Bandos, Peter C. Lucas, et al.. (2024). Utility of the 70-Gene MammaPrint Assay for Prediction of Benefit From Extended Letrozole Therapy in the NRG Oncology/NSABP B-42 Trial. Journal of Clinical Oncology. 42(30). 3561–3569. 3 indexed citations
5.
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Liefers, Gerrit‐Jan, Elma Meershoek‐Klein Kranenbarg, Marjolijn Duijm‐de Carpentier, et al.. (2023). Abstract GS5-10: Utility of the 70-gene MammaPrint test for prediction of extended endocrine therapy benefit in patients with early-stage breast cancer in the IDEAL Trial. Cancer Research. 83(5_Supplement). GS5–10. 6 indexed citations
8.
Zhang, Huina, et al.. (2022). HER2-low breast cancers: incidence, HER2 staining patterns, clinicopathologic features, MammaPrint and BluePrint genomic profiles. Modern Pathology. 35(8). 1075–1082. 79 indexed citations
9.
Crozier, Jennifer A., Pat W. Whitworth, Patricia Dauer, et al.. (2021). High concordance of 70‐gene recurrence risk signature and 80‐gene molecular subtyping signature between core needle biopsy and surgical resection specimens in early‐stage breast cancer. Journal of Surgical Oncology. 125(4). 596–602. 7 indexed citations
10.
Robinson, Patricia A., Michaela L. Tsai, Shelly S. Lo, et al.. (2021). Genomic risk classification by the 70-gene signature and 21-gene assay in African American early-stage breast cancer patients.. Journal of Clinical Oncology. 39(15_suppl). e12568–e12568. 2 indexed citations
11.
Kleijn, Miranda, Jianfeng Wei, Andrea Menicucci, et al.. (2021). Comparing MammaPrint and BluePrint results between core needle biopsy and surgical resection breast cancer specimens. The Breast. 56. S61–S62. 1 indexed citations
12.
Haan, Josien, Rajith Bhaskaran, Lorenza Mittempergher, et al.. (2020). 220P Annotating MammaPrint and BluePrint gene profile to the Hallmarks of cancer and understanding the biology of MammaPrint extreme risk groups. Annals of Oncology. 31. S329–S329. 1 indexed citations
13.
Groenendijk, Floris H., Paul L. Baron, Peter D. Beitsch, et al.. (2019). Estrogen receptor variants in ER-positive basal-type breast cancers responding to therapy like ER-negative breast cancers. npj Breast Cancer. 5(1). 15–15. 16 indexed citations
14.
Soliman, H. S., et al.. (2018). Abstract P1-02-05: The effect of obesity and metabolic factors on genomic assays for risk of recurrence. Cancer Research. 78(4_Supplement). P1–2. 1 indexed citations
15.
Telli, Melinda L., Jessica A. Hellyer, William Audeh, et al.. (2017). Homologous recombination deficiency (HRD) status predicts response to standard neoadjuvant chemotherapy in patients with triple-negative or BRCA1/2 mutation-associated breast cancer. Breast Cancer Research and Treatment. 168(3). 625–630. 78 indexed citations
16.
Tsai, Michaela L., Shelly S. Lo, William Audeh, et al.. (2017). Association of 70-Gene Signature Assay Findings With Physicians’ Treatment Guidance for Patients With Early Breast Cancer Classified as Intermediate Risk by the 21-Gene Assay. JAMA Oncology. 4(1). e173470–e173470. 32 indexed citations
17.
Yue, Yong, Xiaojiang Cui, Shikha Bose, et al.. (2015). Stratifying triple-negative breast cancer prognosis using 18F-FDG-PET/CT imaging. Breast Cancer Research and Treatment. 153(3). 607–616. 20 indexed citations
18.
Audeh, William. (2014). Novel treatment strategies in triple-negative breast cancer: specific role of poly(adenosine diphosphate-ribose) polymerase inhibition. Pharmacogenomics and Personalized Medicine. 7. 307–307. 36 indexed citations
19.
Chung, Alice, Xiaojiang Cui, William Audeh, & Armando E. Giuliano. (2013). Current Status of Anti–Human Epidermal Growth Factor Receptor 2 Therapies: Predicting and Overcoming Herceptin Resistance. Clinical Breast Cancer. 13(4). 223–232. 57 indexed citations
20.
Audeh, William. (2001). MDS and Secondary AML : An Evolutionary Perspective. Cancer treatment and research. 108. 267–278. 1 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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