Publications

Full literature list

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

Artificial intelligence in oncology

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

  • Wagner SJ, Reisenbüchler D, West NP, Niehues JM, Zhu J, Foersch S, Veldhuizen GP, Quirke P, Grabsch HI, van den Brandt PA, Hutchins GGA, Richman SD, Yuan T, Langer R, Jenniskens JCA, Offermans K, Mueller W, Gray R, Gruber SB, Greenson JK, Rennert G, Bonner JD, Schmolze D, Jonnagaddala J, Hawkins NJ, Ward RL, Morton D, Seymour M, Magill L, Nowak M, Hay J, Koelzer VH, Church DN; TransSCOT consortium; Matek C, Geppert C, Peng C, Zhi C, Ouyang X, James JA, Loughrey MB, Salto-Tellez M, Brenner H, 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.

  • Saldanha, O., Quirke, P., West, N., James, J., Loughrey, M., Grabsch, H., Salto-Tellez, M., Alwers, E., Cifci, D., Laleh, N., Seibel, T., Gray, R., Hutchins, G., Brenner, H., Yuan, T., Brinker, T., Chang-Claude, J., Khader, F., Schuppert, A., Luedde, T., 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, Cheong JH, Kim YW, Kim H, Kook MC, Cunningham D, Allum WH, Langley RE, Nankivell MG, Quirke P, Hayden JD, West NP, Irvine AJ, Yoshikawa T, Oshima T, Huss R, Grosser B, Roviello F, d’Ignazio A, Quaas A, Alakus H, Tan X, Pearson AT, Luedde T, 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

Biases, economic impact and implementation research

AI algorithms need to be carefully probed before we implement them in clinical routine. We need to investigate potential biases and limitations, analyze the economic impact of AI and run clinical trials of computational tools to show that it actually helps patients. Our group is actively involved in clinical AI implementation research.

  • Howard FM, Dolezal J, Kochanny S, Schulte J, Chen H, Heij L, Huo D, Nanda R, Olopade OI, Kather JN, Cipriani N, Grossman RL, Pearson AT. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nature Communications, 2020, doi: 10.1038/s41467-021-24698-1

  • Walle T, Erdal E, Mühlsteffen L, Singh HM, Gnutzmann E, Grün B, Hofmann H, Ivanova A, Köhler BC, Korell F, Mavratzas A, Mock A, Pixberg C, Schult D, Starke H, Steinebrunner N, Woydack L, Schneeweiss A, Dietrich M, Jäger D, Krisam J, Kather JN*, Winkler EC*. Completion rate and impact on physician-patient relationship of video consultations in medical oncology: a randomised controlled open-label trial., ESMO Open, 2020, doi: 10.1136/esmoopen-2020-000912 (* equal contribution)

Artificial intelligence in medicine

In addition to oncology, we use our AI technology to address clinical problems in transplant medicine, endoscopy and other medical domains.

  • Nam D, Chapiro J, Paradis V, Seraphin TP, Kather JN. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction. JHEP Rep, 2022, doi: 10.1016/j.jhepr.2022.100443

  • Buendgens L, Cifci D, Ghaffari Laleh N, van Treeck M, Koenen MT, Zimmermann HW, Herbold T, Lux TJ, Hann A, Trautwein C, Kather JN. Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy. Sci Rep, 2022 doi: 10.1038/s41598-022-08773-1

  • Kers J*, Buelow R*, …, Kather JN*, Boor P*. Deep Learning-based classification of kidney transplant pathology: a retrospective, multicenter, proof of concept study. The Lancet Digital Health, 2021 https://www.thelancet.com/journals/landig/article/PIIS2589-7500(21)00211-9/fulltext

Computational modeling

Tumor cells and immune cells in the tumor microenvironment show complex interactions and it is not easy to predict their future behavior based on a current state. In these papers, we have explored mechanistic models as a way of making clinically relevant predictions in solid tumors cancer:

  • Ghaffari Laleh N, Loeffler CML, Grajek J, Staňková K, Pearson AT, Muti HS, Trautwein C, Enderling H, Poleszczuk J, Kather JN. Classical mathematical models for prediction of response to chemotherapy and immunotherapy. PLoS Comput Biol, 2022 doi: 10.1371/journal.pcbi.1009822

  • Noble R, Burri D, Le Sueur C, Lemant J, Viossat Y, Kather JN, Beerenwinkel N. Spatial structure governs the mode of tumour evolution. Nature Ecol Evol, 2022 doi: 10.1038/s41559-021-01615-9.

  • Kather JN, Charoentong P, Suarez-Carmona M, Herpel E, Klupp F, Ulrich A, Schneider M, Zörnig I, Lüdde T, Jäger D, Poleszczuk J, Halama N. High-throughput screening of combinatorial immunotherapies with patient-specific in silico models of metastatic colorectal cancer. Cancer Research, 2018, doi: 10.1158/0008-5472.CAN-18-1126

Reviews and comments

These are some literature reviews and comment articles that summarize our ideas on clinical artificial intelligence:

  • 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.

  • Calderaro J and Kather JN. Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut, 2020 Nov 19. doi: 10.1136/gutjnl-2020-322880

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