Sebastian Foersch

4.5k total citations · 3 hit papers
70 papers, 2.2k citations indexed

About

Sebastian Foersch is a scholar working on Oncology, Radiology, Nuclear Medicine and Imaging and Pulmonary and Respiratory Medicine. According to data from OpenAlex, Sebastian Foersch has authored 70 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 33 papers in Oncology, 27 papers in Radiology, Nuclear Medicine and Imaging and 20 papers in Pulmonary and Respiratory Medicine. Recurrent topics in Sebastian Foersch's work include Radiomics and Machine Learning in Medical Imaging (23 papers), AI in cancer detection (18 papers) and Colorectal Cancer Screening and Detection (11 papers). Sebastian Foersch is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (23 papers), AI in cancer detection (18 papers) and Colorectal Cancer Screening and Detection (11 papers). Sebastian Foersch collaborates with scholars based in Germany, United Kingdom and Switzerland. Sebastian Foersch's co-authors include Markus F. Neurath, Maximilian J. Waldner, Jakob Nikolas Kather, Wilfried Roth, Martin Goetz, Ralf Kießlich, Daniel Truhn, Daniel‐Christoph Wagner, Peter R. Galle and Didem Çifçi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Medicine and Nature Communications.

In The Last Decade

Sebastian Foersch

66 papers receiving 2.2k 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.

Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer

2023 141 citations Sebastian Foersch, Ann-Christin Woerl et al. profile →
Denoising diffusion probabilistic models for 3D medical image generation

2023 121 citations Firas Khader, Gustav Müller‐Franzes et al. Scientific Reports profile →
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology

2025 18 citations Dyke Ferber, Omar S. M. El Nahhas et al. Nature Cancer profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Sebastian Foersch Germany	25	767	568	498	478	392	70	2.2k
Clare Verrill United Kingdom	27	722 0.9×	728 1.3×	761 1.5×	817 1.7×	341 0.9×	102	2.7k
Timo Gaiser Germany	28	1.4k 1.8×	868 1.5×	1.1k 2.2×	837 1.8×	440 1.1×	134	3.7k
Nina Linder Finland	24	450 0.6×	633 1.1×	490 1.0×	743 1.6×	137 0.3×	57	2.1k
Hiroyuki Yoshida Japan	26	1.1k 1.5×	670 1.2×	363 0.7×	383 0.8×	307 0.8×	120	2.6k
Tingbo Liang China	24	696 0.9×	271 0.5×	915 1.8×	150 0.3×	260 0.7×	86	2.2k
Honggang Yu China	30	700 0.9×	561 1.0×	720 1.4×	89 0.2×	395 1.0×	109	2.7k
Feng Li China	27	707 0.9×	394 0.7×	583 1.2×	106 0.2×	466 1.2×	152	2.5k
Muyan Cai China	30	883 1.2×	272 0.5×	1.7k 3.5×	239 0.5×	317 0.8×	121	3.3k
Xin Luo China	27	934 1.2×	228 0.4×	1.5k 3.0×	194 0.4×	165 0.4×	74	2.9k
Jingping Yuan China	26	768 1.0×	232 0.4×	1.2k 2.4×	143 0.3×	255 0.7×	121	3.2k

Countries citing papers authored by Sebastian Foersch

Since Specialization

Citations

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

Fields of papers citing papers by Sebastian Foersch

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sebastian Foersch

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Ernst, Benjamin Philipp, Andrew Blaikie, Tom Kelsey, et al.. (2025). Assessment of decision-making with locally run and web-based large language models versus human board recommendations in otorhinolaryngology, head and neck surgery. European Archives of Oto-Rhino-Laryngology. 282(3). 1593–1607. 4 indexed citations

Herber, Silvani, Lukáš Müller, Daniel Santos, et al.. (2025). Diagnostic performance of artificial intelligence models for pulmonary nodule classification: a multi-model evaluation. European Radiology. 36(1). 537–547.

Carrero, Zunamys I., Katherine Hewitt, Matthew J. Cecchini, et al.. (2025). Application of artificial intelligence and digital tools in cancer pathology. The Lancet Digital Health. 7(10). 100933–100933. 1 indexed citations

Clusmann, Jan, Stefan Schulz, Dyke Ferber, et al.. (2025). Incidental Prompt Injections on Vision–Language Models in Real-Life Histopathology. NEJM AI. 2(6). 3 indexed citations

Papavassiliu, Theano, Stefan O. Schoenberg, Norbert Frey, et al.. (2025). Unconditional latent diffusion models memorize patient imaging data. Nature Biomedical Engineering. 10(3). 458–472. 2 indexed citations

Ferber, Dyke, Omar S. M. El Nahhas, Georg Wölflein, et al.. (2025). Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology. Nature Cancer. 6(8). 1337–1349. 18 indexed citations breakdown →

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

Han, Tianyu, Sven Nebelung, Firas Khader, et al.. (2024). Medical large language models are susceptible to targeted misinformation attacks. npj Digital Medicine. 7(1). 288–288. 17 indexed citations

Khader, Firas, Gustav Müller‐Franzes, Soroosh Tayebi Arasteh, et al.. (2023). Denoising diffusion probabilistic models for 3D medical image generation. Scientific Reports. 13(1). 7303–7303. 121 indexed citations breakdown →

10.

Stenzel, Philipp, Mario Schindeldecker, Larissa Seidmann, et al.. (2023). CD15 Is a Risk Predictor and a Novel Target in Clear Cell Renal Cell Carcinoma. Pathobiology. 91(3). 219–229. 3 indexed citations

11.

Echle, Amelie, et al.. (2022). The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups. Histopathology. 80(7). 1121–1127. 19 indexed citations

12.

Fernandez, Aurélie, Björn Konukiewitz, Mario Schindeldecker, et al.. (2022). Senescence-Associated Molecules and Tumor-Immune-Interactions as Prognostic Biomarkers in Colorectal Cancer. Frontiers in Medicine. 9. 865230–865230. 12 indexed citations

13.

Saldanha, Oliver Lester, Hannah Sophie Muti, Heike I. Grabsch, et al.. (2022). Direct prediction of genetic aberrations from pathology images in gastric cancer with swarm learning. Gastric Cancer. 26(2). 264–274. 24 indexed citations

14.

Silva, Miguel G., Björn Konukiewitz, Katja Steiger, et al.. (2021). Loss of SATB2 Occurs More Frequently Than CDX2 Loss in Colorectal Carcinoma and Identifies Particularly Aggressive Cancers in High-Risk Subgroups. Cancers. 13(24). 6177–6177. 13 indexed citations

15.

Seiwert, Nina, Pablo Steinberg, Stefan Wirtz, et al.. (2021). Chronic intestinal inflammation drives colorectal tumor formation triggered by dietary heme iron in vivo. Archives of Toxicology. 95(7). 2507–2522. 25 indexed citations

16.

Konukiewitz, Björn, Miguel G. Silva, Katja Steiger, et al.. (2021). Loss of CDX2 in colorectal cancer is associated with histopathologic subtypes and microsatellite instability but is prognostically inferior to hematoxylin–eosin-based morphologic parameters from the WHO classification. British Journal of Cancer. 125(12). 1632–1646. 22 indexed citations

17.

Noske, Aurelia, Kristina Schwamborn, Sebastian Foersch, et al.. (2021). 13P Comparison study of different programmed death-ligand 1 (PD-L1) assays, readers and scoring methods in triple-negative breast cancer (TNBC). Annals of Oncology. 32. S26–S26. 3 indexed citations

18.

Woerl, Ann-Christin, Markus Eckstein, Daniel‐Christoph Wagner, et al.. (2020). Deep Learning Predicts Molecular Subtype of Muscle-invasive Bladder Cancer from Conventional Histopathological Slides. European Urology. 78(2). 256–264. 111 indexed citations

19.

Foersch, Sebastian, Maximilian J. Waldner, & Markus F. Neurath. (2013). Innate and Adaptive Immunity in Inflammatory Bowel Diseases. Digestive Diseases. 31(3-4). 317–320. 11 indexed citations

20.

Waldner, Maximilian J., Sebastian Foersch, & Markus F. Neurath. (2012). Interleukin-6 - A Key Regulator of Colorectal Cancer Development. International Journal of Biological Sciences. 8(9). 1248–1253. 319 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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