Laura J. Esserman

33.2k total citations · 1 hit paper
438 papers, 15.1k citations indexed

About

Laura J. Esserman is a scholar working on Cancer Research, Oncology and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Laura J. Esserman has authored 438 papers receiving a total of 15.1k indexed citations (citations by other indexed papers that have themselves been cited), including 223 papers in Cancer Research, 194 papers in Oncology and 100 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Laura J. Esserman's work include Breast Cancer Treatment Studies (179 papers), Breast Lesions and Carcinomas (70 papers) and Global Cancer Incidence and Screening (64 papers). Laura J. Esserman is often cited by papers focused on Breast Cancer Treatment Studies (179 papers), Breast Lesions and Carcinomas (70 papers) and Global Cancer Incidence and Screening (64 papers). Laura J. Esserman collaborates with scholars based in United States, Sweden and United Kingdom. Laura J. Esserman's co-authors include Nola M. Hylton, Dan H. Moore, Robert D. Foster, Cheryl A. Ewing, Michael Alvarado, Michael J. Campbell, Anne Warren Peled, Hope S. Rugo, Ian M. Thompson and Brian J. Reid and has published in prestigious journals such as Nature, New England Journal of Medicine and Proceedings of the National Academy of Sciences.

In The Last Decade

Laura J. Esserman

407 papers receiving 14.6k citations

Hit Papers

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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.

Addressing overdiagnosis and overtreatment in cancer: a prescription for change

2014 351 citations Laura J. Esserman et al. profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author						Papers	Cites
Laura J. Esserman	6.2k	5.5k	3.5k	2.9k	2.9k	438	15.1k
Benjamin D. Smith	7.0k 1.1×	9.3k 1.7×	2.1k 0.6×	2.9k 1.0×	4.4k 1.5×	375	19.3k
Lisa A. Newman	8.8k 1.4×	7.9k 1.4×	1.7k 0.5×	4.5k 1.5×	4.0k 1.4×	298	18.8k
Harold J. Burstein	7.1k 1.1×	11.9k 2.2×	2.8k 0.8×	2.6k 0.9×	1.7k 0.6×	220	19.1k
Roger A’Hern	5.4k 0.9×	9.4k 1.7×	1.7k 0.5×	2.5k 0.9×	4.0k 1.4×	282	20.5k
Rob A.�E.�M. Tollenaar	2.8k 0.5×	8.1k 1.5×	1.6k 0.5×	2.4k 0.8×	4.0k 1.4×	315	15.6k
Monica Morrow	12.1k 1.9×	7.9k 1.4×	2.2k 0.6×	6.6k 2.2×	6.4k 2.2×	436	19.8k
Philip Poortmans	8.9k 1.4×	7.1k 1.3×	3.3k 0.9×	5.2k 1.8×	3.4k 1.2×	310	19.5k
Sharon H. Giordano	7.4k 1.2×	10.8k 2.0×	2.1k 0.6×	2.6k 0.9×	2.9k 1.0×	408	20.8k
Antonio C. Wolff	10.7k 1.7×	15.9k 2.9×	3.9k 1.1×	3.4k 1.2×	1.8k 0.6×	360	24.8k
Nehmat Houssami	7.1k 1.1×	6.9k 1.3×	5.7k 1.6×	4.8k 1.7×	2.1k 0.7×	318	18.3k

Countries citing papers authored by Laura J. Esserman

Since Specialization

Citations

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

Fields of papers citing papers by Laura J. Esserman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Laura J. Esserman

This figure shows the co-authorship network connecting the top 25 collaborators of Laura J. Esserman. A scholar is included among the top collaborators of Laura J. Esserman 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 Laura J. Esserman. Laura J. Esserman 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

Petricoin, Emanuel F., Denise M. Wolf, Christina Yau, et al.. (2025). Immune and Growth Factor Signaling Pathways Are Associated with Pathologic Complete Response to an Anti–Type I Insulin-like Growth Factor Receptor Regimen in Patients with Breast Cancer. Clinical Cancer Research. 31(20). 4361–4371.

Çil, Onur, A.S. Verkman, Matilda F. Chan, et al.. (2024). Incidence and Mitigation of Corneal Pseudomicrocysts Induced by Antibody–Drug Conjugates (ADCs). Current Ophthalmology Reports. 12(2). 13–22. 7 indexed citations

Alipanah, Narges, Kevin Delucchi, Lamorna Brown Swigart, et al.. (2024). Novel subtypes of severe COVID-19 respiratory failure based on biological heterogeneity: a secondary analysis of a randomized controlled trial. Critical Care. 28(1). 56–56. 3 indexed citations

Esserman, Laura J., et al.. (2024). RE: Supplemental magnetic resonance imaging plus mammography compared with magnetic resonance imaging or mammography by extent of breast density. JNCI Journal of the National Cancer Institute. 116(4). 627–628. 1 indexed citations

Thomas, Alexandra, Denise M. Wolf, Ronald Balassanian, et al.. (2024). Therapeutic response and outcomes with uncommon breast cancer subtypes in the I-SPY trial 2010-2022.. Journal of Clinical Oncology. 42(23_suppl). 1–1. 1 indexed citations

Meisel, Jane, Katia Khoury, A. Jo Chien, et al.. (2024). Rates of pathologic complete response (pCR)after neoadjuvant datopotamab deruxtecan (Dato): Results from the I-SPY2.2 trial.. Journal of Clinical Oncology. 42(17_suppl). LBA509–LBA509. 4 indexed citations

Shatsky, Rebecca, Meghna S. Trivedi, Coral Omene, et al.. (2024). Rates of pathologic complete response (pCR) after datopotamab deruxtecan (Dato) plus durvalumab (Durva) in the neoadjuvant setting: Results from the I-SPY2.2 trial.. Journal of Clinical Oncology. 42(17_suppl). LBA501–LBA501. 10 indexed citations

Diwanji, Devan, Natsuko Onishi, Courtney Lawhn-Heath, et al.. (2024). ¹⁸F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy. Radiology Imaging Cancer. 6(2). e230082–e230082.

Rothschild, Harriet, Jasmine Wong, Laura J. Esserman, et al.. (2023). Predictors of Early Versus Late Recurrence in Invasive Lobular Carcinoma of the Breast: Impact of Local and Systemic Therapy. Annals of Surgical Oncology. 30(10). 5999–6006. 2 indexed citations

10.

Basu, Amrita, Thomas Jones, Timothy D. Brown, et al.. (2023). 374P Evaluation of symptom severity, tolerability, and physical function in the I-SPY2 trial. Annals of Oncology. 34. S332–S333. 1 indexed citations

11.

Rothschild, Harriet, Mary Kathryn Abel, A. Jo Chien, et al.. (2023). The impact of histologic subtype on primary site surgery in the management of metastatic lobular versus ductal breast cancer: a population based study from the National Cancer Database (NCDB). Breast Cancer Research and Treatment. 203(2). 245–256. 2 indexed citations

12.

Chitalia, Rhea, Nariman Jahani, Spyros Tastsoglou, et al.. (2023). Radiomic tumor phenotypes augment molecular profiling in predicting recurrence free survival after breast neoadjuvant chemotherapy. SHILAP Revista de lepidopterología. 3(1). 46–46. 5 indexed citations

13.

Glencer, Alexa, Heather Greenwood, Rita I. Freimanis, et al.. (2022). Identifying Good Candidates for Active Surveillance of Ductal Carcinoma In Situ : Insights from a Large Neoadjuvant Endocrine Therapy Cohort. Cancer Research Communications. 2(12). 1579–1589. 5 indexed citations

14.

Li, Wen, Natsuko Onishi, David C. Newitt, et al.. (2022). Effect of Inter-Reader Variability on Diffusion-Weighted MRI Apparent Diffusion Coefficient Measurements and Prediction of Pathologic Complete Response for Breast Cancer. Tomography. 8(3). 1208–1220. 5 indexed citations

15.

Magbanua, Mark Jesus M., Wen Li, Denise M. Wolf, et al.. (2021). Circulating tumor DNA and magnetic resonance imaging to predict neoadjuvant chemotherapy response and recurrence risk. npj Breast Cancer. 7(1). 32–32. 33 indexed citations

16.

Magbanua, Mark Jesus M., Christina Yau, Denise M. Wolf, et al.. (2019). Synchronous Detection of Circulating Tumor Cells in Blood and Disseminated Tumor Cells in Bone Marrow Predicts Adverse Outcome in Early Breast Cancer. Clinical Cancer Research. 25(17). 5388–5397. 21 indexed citations

17.

Li, Wen, Lisa J. Wilmes, Ella F. Jones, et al.. (2019). Additive value of diffusion‐weighted MRI in the I‐SPY 2 TRIAL. Journal of Magnetic Resonance Imaging. 50(6). 1742–1753. 27 indexed citations

18.

Marks, Jeffrey R., Karen S. Anderson, Paul F. Engstrom, et al.. (2014). Construction and Analysis of the NCI-EDRN Breast Cancer Reference Set for Circulating Markers of Disease. Cancer Epidemiology Biomarkers & Prevention. 24(2). 435–441. 13 indexed citations

19.

Wulfkuhle, Julia, Daniela Berg, Claudia Wolff, et al.. (2012). Molecular Analysis of HER2 Signaling in Human Breast Cancer by Functional Protein Pathway Activation Mapping. Clinical Cancer Research. 18(23). 6426–6435. 85 indexed citations

20.

Wang, Yuker, Martin Moorhead, George Karlin‐Neumann, et al.. (2008). Performance of Molecular Inversion Probes (MIP) in Allele Copy Number Determination. eScholarship (California Digital Library). 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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