Charles Maussion

940 total citations · 1 hit paper
12 papers, 338 citations indexed

About

Charles Maussion is a scholar working on Radiology, Nuclear Medicine and Imaging, Pulmonary and Respiratory Medicine and Artificial Intelligence. According to data from OpenAlex, Charles Maussion has authored 12 papers receiving a total of 338 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Radiology, Nuclear Medicine and Imaging, 6 papers in Pulmonary and Respiratory Medicine and 5 papers in Artificial Intelligence. Recurrent topics in Charles Maussion's work include Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers) and Sarcoma Diagnosis and Treatment (2 papers). Charles Maussion is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (6 papers), AI in cancer detection (5 papers) and Sarcoma Diagnosis and Treatment (2 papers). Charles Maussion collaborates with scholars based in France, United Kingdom and United States. Charles Maussion's co-authors include Mikhail Zaslavskiy, Jean‐Yves Blay, Gilles Wainrib, Thomas Clozel, Nolwenn Le Stang, Pierre Courtiol, Matahi Moarii, Olivier Elemento, Andrew G. Nicholson and Elodie Pronier and has published in prestigious journals such as Nature Medicine, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Charles Maussion

9 papers receiving 336 citations

Hit Papers

align trajectories

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.

Deep learning-based classification of mesothelioma improves prediction of patient outcome

2019 314 citations Pierre Courtiol, Charles Maussion et al. Nature Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Charles Maussion France	4	190	178	74	67	57	12	338
Peter Truszkowski United States	2	211 1.1×	187 1.1×	50 0.7×	60 0.9×	41 0.7×	2	341
Vipul Baxi United States	6	206 1.1×	205 1.2×	63 0.9×	112 1.7×	73 1.3×	16	446
Marko van Treeck Germany	11	209 1.1×	178 1.0×	31 0.4×	64 1.0×	60 1.1×	21	345
Emmanuel Agosto‐Arroyo United States	5	207 1.1×	148 0.8×	39 0.5×	81 1.2×	57 1.0×	11	327
Juan Antonio Retámero Spain	8	176 0.9×	116 0.7×	71 1.0×	82 1.2×	22 0.4×	13	299
Kyunghyun Paeng South Korea	9	104 0.5×	136 0.8×	58 0.8×	92 1.4×	48 0.8×	29	237
Wei-Hsiang Yu Taiwan	6	166 0.9×	179 1.0×	68 0.9×	50 0.7×	27 0.5×	10	268
Manuela Vecsler United States	4	196 1.0×	179 1.0×	63 0.9×	45 0.7×	38 0.7×	5	290
Andrew Lagree Canada	11	151 0.8×	192 1.1×	39 0.5×	52 0.8×	61 1.1×	15	295
Patrick Leo United States	13	192 1.0×	325 1.8×	175 2.4×	73 1.1×	54 0.9×	31	464

Countries citing papers authored by Charles Maussion

Since Specialization

Citations

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

Fields of papers citing papers by Charles Maussion

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charles Maussion

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

All Works

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12 of 12 papers shown

Jacob, Paul, Côme Lepage, Claire Gallois, et al.. (2025). Deep Learning on Histologic Slides Accurately Predicts Consensus Molecular Subtypes and Spatial Heterogeneity in Colon Cancer. Modern Pathology. 38(11). 100877–100877. 1 indexed citations

Maussion, Charles, Jean‐Michel Coindre, Jean-Yves Blay, et al.. (2025). Multimodal prediction of metastatic relapse using federated deep learning in soft-tissue sarcoma with a complex genomic profile. Scientific Reports. 15(1). 36588–36588.

Saillard, Charlie, P Mann, Maxime Touzot, et al.. (2024). AI allows pre-screening of FGFR3 mutational status using routine histology slides of muscle-invasive bladder cancer. Nature Communications. 15(1). 10914–10914. 4 indexed citations

Maussion, Charles, Anca Mera, Cheryl Gillett, et al.. (2024). Abstract PO2-07-05: Deep learning model for automated quantification of HER2 expression in invasive breast cancers from immunohistochemical whole slide images. Cancer Research. 84(9_Supplement). PO2–7. 1 indexed citations

Syrykh, Charlotte, Christiane Copie‐Bergman, Fabrice Jardin, et al.. (2024). MYC Rearrangement Prediction From LYSA Whole Slide Images in Large B-Cell Lymphoma: A Multicentric Validation of Self-supervised Deep Learning Models. Modern Pathology. 37(12). 100610–100610. 1 indexed citations

Broeckx, Glenn, Rémy Dubois, Charles Maussion, et al.. (2024). Development of a deep‐learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2‐low cases. Histopathology. 85(3). 478–488. 5 indexed citations

Fidon, Lucas, et al.. (2024). Abstract 7395: Histology-based prognosis prediction using deep learning outperforms and is independent of the MGMT methylation status in patients with glioblastoma. Cancer Research. 84(6_Supplement). 7395–7395.

Fu, Yu, Marie Karanian, Raul Perret, et al.. (2023). Deep learning predicts patients outcome and mutations from digitized histology slides in gastrointestinal stromal tumor. npj Precision Oncology. 7(1). 71–71. 10 indexed citations

Schmauch, Benoît, Sarah McIntyre, Patrick Sin‐Chan, et al.. (2023). Machine learning-based multimodal prediction of prognosis in patients with resected intrahepatic cholangiocarcinoma.. Journal of Clinical Oncology. 41(16_suppl). 4121–4121. 1 indexed citations

10.

Maussion, Charles, Jean‐Michel Coindre, Jean‐Yves Blay, et al.. (2022). Abstract 1939: Multimodal prediction of metastatic relapse using federated deep learning outperforms state-of-the-art methods in soft-tissue sarcoma. Cancer Research. 82(12_Supplement). 1939–1939.

11.

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

12.

Courtiol, Pierre, Charles Maussion, Matahi Moarii, et al.. (2019). Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nature Medicine. 25(10). 1519–1525. 314 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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