Publications

Full literature list

Check out Google Scholar or PubMed for a full list of our publications. Below, we highlight some recent publications.

Literature reviews and concept articles

Our mission is to make Deep Learning understandable and usable for biological researchers and clinicians in cancer research and oncology. In addition to our technical work (see below), we publish high-level overview articles describing the developments in our research fields.

  • Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome Medicine, 2024 Mar 27;16(1):44. doi: 10.1186/s13073-024-01315-6.

  • Truhn D, Eckardt JN, Ferber D, Kather JN. Large language models and multimodal foundation models for precision oncology. NPJ Precision Oncology, 2024, doi: 10.1038/s41698-024-00573-2.

  • Shmatko A, Ghaffari Laleh N, Gerstung M, Kather JN. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nature Cancer, 2022, doi: 10.1038/s43018-022-00436-4.

  • Kather JN, Calderaro J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer. Nature Reviews Gastroenterology Hepatology, 2020, doi: 10.1038/s41575-020-0343-3.

Computational Pathology

For more than five years, we have been using artificial intelligence (AI) methods, in particular deep learning, to extract hidden information from routine pathology images of human cancer. Our aim is to use AI to make clinically actionable predictions which can improve clinical outcomes. These are some selected publications.

  • Jiang X, Hoffmeister M, Brenner H, Muti HS, Yuan T, Foersch S, West NP, Brobeil A, Jonnagaddala J, Hawkins N, Ward RL, Brinker TJ, Saldanha OL, Ke J, Müller W, Grabsch HI, Quirke P, Truhn D, Kather JN. End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study. The Lancet Digital Health, 2024, doi: 10.1016/S2589-7500(23)00208-X.

  • El Nahhas OSM, Loeffler CML, Carrero ZI, van Treeck M, Kolbinger FR, Hewitt KJ, Muti HS, Graziani M, Zeng Q, Calderaro J, Ortiz-Brüchle N, Yuan T, Hoffmeister M, Brenner H, Brobeil A, Reis-Filho JS, Kather JN. Regression-based Deep-Learning predicts molecular biomarkers from pathology slides. Nature Communications, 2024, doi: 10.1038/s41467-024-45589-1

  • Wagner SJ, Reisenbüchler D, West NP, Niehues JM, Zhu J, Foersch S, … Hoffmeister M, Truhn D, Schnabel JA, Boxberg M, Peng T, Kather JN. Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell, 2023, doi: 10.1016/j.ccell.2023.08.002.

  • Calderaro J, Ghaffari Laleh N, Zeng Q, Maille P, Favre L, Pujals A, … Tantipisit J, Kaewdech A, Shen J, Paradis V, Caruso S, Kather JN. Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Nature Communications, 2023, doi: 10.1038/s41467-023-43749-3.

  • Saldanha, O., Quirke, P., West, N., James, J., Loughrey, M., Grabsch, H., … Foersch, S., Muti, H., Trautwein, C., Hoffmeister, M., Truhn, D. and Kather, JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nature Medicine, 2022, doi: 10.1038/s41591-022-01768-5

  • Muti HS, Heij LR, Keller G, Kohlruss M, Langer R, Dislich B, … Ebert MP, Jäger D, Trautwein C, Gaisa NT, Grabsch HI, Kather JN. Development and validation of deep learning classifiers to detect Epstein-Barr virus and microsatellite instability status in gastric cancer: a retrospective multicentre cohort study. The Lancet Digital Health, 2021, doi: 10.1016/S2589-7500(21)00133-3.

  • Kather JN, Heij LR , Grabsch HI , Loeffler C , Echle A , Muti HS, Krause J, Niehues JM, Sommer KA, Bankhead P, Kooreman LFS, Schulte J, Cipriani NA , Bülow RD, Boor P, Ortiz Bruechle N, Hanby AM, Speirs V, Kochanny, Patnaik A, Srisuwananukorn A, Brenner H, Hoffmeister M, van den Brandt PA, Jaeger D, Trautwein C, Pearson AT , Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations. Nature Cancer, 2020, doi: 10.1038/s43018-020-0087-6

  • Echle A, Grabsch HI, Quirke P, van den Brandt PA, West NP, Hutchins GGA, Heij LR, Tan X, Richman SD, Krause J, Alwers E, Jenniskens J, Offermans K, Gray R, Brenner H, Chang-Claude J, Trautwein C, Pearson AT, Boor P, Luedde T, Gaisa NT, Hoffmeister M, Kather JN. Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning. Gastroenterology, 2020, doi: 10.1053/j.gastro.2020.06.021

  • Kather JN, Pearson AT, Halama N, Jaeger D, Krause J, Loosen SH, Marx A, Boor P, Tacke F, Neumann UP, Grabsch HI, Yoshikawa T, Brenner H, Chang-Claude J, Hoffmeister M, Trautwein C, Luedde T. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nature Medicine, 2019, doi: 10.1038/s41591-019-0462-y

Deep Learning in Radiology

Deep Learning can extract actionable information from radiology scans of cancer patients. We use end-to-end weakly supervised Deep Learning, building and validating hands-off methods with minimal human bias, requiring minimal human intervention.

  • Truhn D, Tayebi Arasteh S, Saldanha OL, Müller-Franzes G, Khader F, Quirke P, … Isfort P, Bruners P, Kaissis G, Kuhl C, Nebelung S, Kather JN. Encrypted federated learning for secure decentralized collaboration in cancer image analysis. Medical Image Analysis, 2024, doi: 10.1016/j.media.2023.103059

  • Jiang X, Zhao H, Saldanha OL, Nebelung S, Kuhl C, Amygdalos I, Lang SA, Wu X, Meng X, Truhn D, Kather JN, Ke J. An MRI Deep Learning Model Predicts Outcome in Rectal Cancer. Radiology, 2023, doi: 10.1148/radiol.222223

  • Khader F, Müller-Franzes G, Wang T, Han T, Tayebi Arasteh S, Haarburger C, Stegmaier J, Bressem K, Kuhl C, Nebelung S, Kather JN, Truhn D. Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers. Radiology, 2023, doi: 10.1148/radiol.230806

Natural Language Processing

Large language models (LLM) have revolutionized natural language processing (NLP) and can process text at a human level in many domains. We are applying and developing LLM-based pipelines for cancer research and oncology.

  • Truhn D, Reis-Filho JS, Kather JN. Large language models should be used as scientific reasoning engines, not knowledge databases. Nature Medicine, 2023, doi: 10.1038/s41591-023-02594-z

  • Truhn D, Loeffler CM, Müller-Franzes G, Nebelung S, Hewitt KJ, Brandner S, Bressem KK, Foersch S, Kather JN. Extracting structured information from unstructured histopathology reports using generative pre-trained transformer 4 (GPT-4). Journal of Pathology, 2023, doi: 10.1002/path.6232