Laëtitia Vercellino
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- Medical Imaging Techniques and Applications 23
- Radiomics and Machine Learning in Medical Imaging 14
- Cancer Research top 5%
- Breast Cancer Treatment Studies 11
- Oncology top 5%
- CAR-T cell therapy research 13
- Cancer Immunotherapy and Biomarkers 7
- Cutaneous Melanoma Detection and Management 5
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- Lymphoma Diagnosis and Treatment 15
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- Melanoma and MAPK Pathways 8
- Co-authors
- David GroheuxElif HindiéMarc EspiéSylvie GiacchettiAnne de RoquancourtAnne‐Sophie HamyPascal MerletC. Cuvier
- Partner nations
- FranceUnited StatesItaly
In The Last Decade
Laëtitia Vercellino
75 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 81
- Radiology, Nuclear Medicine and Imaging 1.1k
- Cancer Research 572
- Oncology 788
- Pathology and Forensic Medicine 430
- Pulmonary and Respiratory Medicine 517
Countries citing papers authored by Laëtitia Vercellino
This map shows the geographic impact of Laëtitia Vercellino'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 Laëtitia Vercellino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Laëtitia Vercellino more than expected).
Fields of papers citing papers by Laëtitia Vercellino
This network shows the impact of papers produced by Laëtitia Vercellino. 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 Laëtitia Vercellino. The network helps show where Laëtitia Vercellino may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Laëtitia Vercellino, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2023 | 1 | |
| 4 | 2022 | 43 | |
| 5 | 2022 | 20 | |
| 6 | 2022 | 3 | |
| 7 | 2022 | 31 | |
| 8 | 2021 | 8 | |
| 9 | 2020 | 16 | |
| 10 | 2020 | 42 | |
| 11 | 2020 | 105 | |
| 12 | 2017 | 34 | |
| 13 | 2016 | 96 | |
| 14 | 2013 | 52 | |
| 15 | 2013 | 6 | |
| 16 | 2013 | 29 | |
| 17 | 2012 | 25 | |
| 18 | 2012 | 91 | |
| 19 | 2010 | 312 | |
| 20 | 2009 | 52 |
About Laëtitia Vercellino
Laëtitia Vercellino is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Pathology and Forensic Medicine, having authored 81 papers that have together received 2.2k indexed citations. Recurring topics across this work include Medical Imaging Techniques and Applications (23 papers), Lymphoma Diagnosis and Treatment (15 papers), Radiomics and Machine Learning in Medical Imaging (14 papers), CAR-T cell therapy research (13 papers), Breast Cancer Treatment Studies (11 papers), Melanoma and MAPK Pathways (8 papers), Cancer Immunotherapy and Biomarkers (7 papers) and Cutaneous Melanoma Detection and Management (5 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (1.1k citations), Cancer Research (572 citations) and Oncology (788 citations). Laëtitia Vercellino has collaborated with scholars based in France, United States and Italy. Frequent co-authors include David Groheux, Elif Hindié, Marc Espié, Sylvie Giacchetti, Anne de Roquancourt, Anne‐Sophie Hamy, Pascal Merlet, C. Cuvier, Marie‐Elisabeth Toubert and Catherine Thiéblemont. Their work appears in journals such as Blood, JNCI Journal of the National Cancer Institute and Radiology.
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.