Maud Milder

2.4k total citations · 1 hit paper
14 papers, 1.4k citations indexed

About

Maud Milder is a scholar working on Oncology, Cancer Research and Ophthalmology. According to data from OpenAlex, Maud Milder has authored 14 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Oncology, 6 papers in Cancer Research and 4 papers in Ophthalmology. Recurrent topics in Maud Milder's work include Cancer Genomics and Diagnostics (4 papers), Ocular Oncology and Treatments (4 papers) and Immunotherapy and Immune Responses (3 papers). Maud Milder is often cited by papers focused on Cancer Genomics and Diagnostics (4 papers), Ocular Oncology and Treatments (4 papers) and Immunotherapy and Immune Responses (3 papers). Maud Milder collaborates with scholars based in France, Italy and United States. Maud Milder's co-authors include Olivier Lantz, Delphine Louis, Lionel Le Bourhis, Isabelle Péguillet, Virginie Prémel, Claire Soudais, Mathilde Dusséaux, Emmanuel Treiner, Emmanuel Martin and Marc‐Henri Stern and has published in prestigious journals such as Journal of Clinical Oncology, Blood and JNCI Journal of the National Cancer Institute.

In The Last Decade

Maud Milder

14 papers receiving 1.4k citations

Hit Papers

Human MAIT cells are xenobiotic-resistant, tissue-targete... 2010 2026 2015 2020 2010 200 400 600
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.

  1. Human MAIT cells are xenobiotic-resistant, tissue-targeted, CD161hi IL-17–secreting T cells
    2010 741 citations Mathilde Dusséaux, Emmanuel Martin et al. Blood profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Maud Milder France 9 865 319 307 234 233 14 1.4k
Alain Gey France 19 1.1k 1.3× 983 3.1× 135 0.4× 196 0.8× 281 1.2× 37 1.7k
Richard Childs United States 13 629 0.7× 465 1.5× 68 0.2× 245 1.0× 196 0.8× 18 1.4k
Toshinao Kawai Japan 16 483 0.6× 268 0.8× 48 0.2× 106 0.5× 164 0.7× 50 930
Roberto Crocchiolo Italy 27 661 0.8× 587 1.8× 44 0.1× 553 2.4× 133 0.6× 111 2.1k
Ismael Buño Spain 22 344 0.4× 432 1.4× 181 0.6× 155 0.7× 378 1.6× 105 1.8k
Ashish V. Chintakuntlawar United States 21 300 0.3× 1.0k 3.2× 137 0.4× 284 1.2× 302 1.3× 75 1.9k
Laura Caggiari Italy 20 358 0.4× 432 1.4× 85 0.3× 265 1.1× 194 0.8× 62 1.3k
A H Rook United States 11 623 0.7× 212 0.7× 44 0.1× 348 1.5× 241 1.0× 11 1.2k
Jean-Pierre Vendrell France 20 439 0.5× 677 2.1× 443 1.4× 295 1.3× 263 1.1× 35 1.6k
Kathrin Zitzmann Germany 15 855 1.0× 613 1.9× 95 0.3× 518 2.2× 347 1.5× 24 1.6k

Countries citing papers authored by Maud Milder

Since Specialization
Citations

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

Fields of papers citing papers by Maud Milder

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Maud Milder

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

All Works

14 of 14 papers shown
1.
Basse, Clémence, Matthieu Carton, Maud Milder, et al.. (2024). Real-World Survival Impact of New Treatment Strategies for Lung Cancer: A 2000–2020 French Cohort. Cancers. 16(15). 2768–2768. 4 indexed citations
2.
Basse, Clémence, Julien Ancel, Marie-Ange Massiani, et al.. (2023). Accelerated subsequent lung cancer after post-operative radiotherapy for breast cancer. Lung Cancer. 182. 107295–107295. 2 indexed citations
3.
Delrieu, Lidia, Anne‐Sophie Hamy, Florence Coussy, et al.. (2022). Digital phenotyping in young breast cancer patients treated with neoadjuvant chemotherapy (the NeoFit Trial): protocol for a national, multicenter single-arm trial. BMC Cancer. 22(1). 493–493. 3 indexed citations
4.
Terrail, Jean Ogier du, Mathieu Andreux, Charles Maussion, et al.. (2022). Collaborative federated learning behind hospitals’ firewalls for predicting histological complete response to neoadjuvant chemotherapy in triple-negative breast cancer.. Journal of Clinical Oncology. 40(16_suppl). 590–590. 1 indexed citations
5.
Mobuchon, Lenha, Alexandre Houy, Gaëlle Pierron, et al.. (2021). Different Pigmentation Risk Loci for High-Risk Monosomy 3 and Low-Risk Disomy 3 Uveal Melanomas. JNCI Journal of the National Cancer Institute. 114(2). 302–309. 14 indexed citations
6.
7.
Mobuchon, Lenha, Aude Battistella, Claire Bardel, et al.. (2017). A GWAS in uveal melanoma identifies risk polymorphisms in the CLPTM1L locus. npj Genomic Medicine. 2(1). 15 indexed citations
8.
Decraene, Charles, Amanda Bortolini Silveira, François‐Clément Bidard, et al.. (2017). Multiple Hotspot Mutations Scanning by Single Droplet Digital PCR. Clinical Chemistry. 64(2). 317–328. 43 indexed citations
9.
Riva, Francesca, François‐Clément Bidard, Alexandre Houy, et al.. (2017). Patient-Specific Circulating Tumor DNA Detection during Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer. Clinical Chemistry. 63(3). 691–699. 158 indexed citations
10.
Péguillet, Isabelle, Maud Milder, Delphine Louis, et al.. (2014). High Numbers of Differentiated Effector CD4 T Cells Are Found in Patients with Cancer and Correlate with Clinical Response after Neoadjuvant Therapy of Breast Cancer. Cancer Research. 74(8). 2204–2216. 29 indexed citations
11.
Grimaldi, David, Lionel Le Bourhis, Bertrand Sauneuf, et al.. (2013). Specific MAIT cell behaviour among innate-like T lymphocytes in critically ill patients with severe infections. Intensive Care Medicine. 40(2). 192–201. 137 indexed citations
12.
Bidard, François‐Clément, Jordan Madic, Pascale Mariani, et al.. (2013). Detection rate and prognostic value of circulating tumor cells and circulating tumor DNA in metastatic uveal melanoma. International Journal of Cancer. 134(5). 1207–1213. 146 indexed citations
13.
Madic, Jordan, Sophie Piperno‐Neumann, Vincent Servois, et al.. (2012). Pyrophosphorolysis-Activated Polymerization Detects Circulating Tumor DNA in Metastatic Uveal Melanoma. Clinical Cancer Research. 18(14). 3934–3941. 65 indexed citations
14.
Dusséaux, Mathilde, Emmanuel Martin, Isabelle Péguillet, et al.. (2010). Human MAIT cells are xenobiotic-resistant, tissue-targeted, CD161hi IL-17–secreting T cells. Blood. 117(4). 1250–1259. 741 indexed citations breakdown →

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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