Hoffmeister, Michael

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,458

Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study

Kather, J. N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.-A., Gaiser, T., Marx, A., Valous, N. A., Ferber, D., et al. (2019) · PLOS Medicine

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 1,082

Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Echle, A., Grabsch, H. I., Quirke, P., van den Brandt, P. A., West, N. P., Hutchins, G. G. A., Heij, L. R., Tan, X., Richman, S. D., Krause, J., et al. (2020) · Gastroenterology

Genetic factors in colorectal cancerAI in cancer detection

Cited by 376

Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study

Wagner, S. J., Reisenbüchler, D., West, N. P., Niehues, J. M., Zhu, J., Foersch, S., Veldhuizen, G. P., Quirke, P., Grabsch, H. I., van den Brandt, P. A., et al. (2023) · Cancer Cell

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 275

Swarm learning for decentralized artificial intelligence in cancer histopathology

Saldanha, O. L., Quirke, P., West, N. P., James, J. A., Loughrey, M. B., Grabsch, H. I., Salto-Tellez, M., Alwers, E., Cifci, D., Ghaffari Laleh, N., et al. (2022) · Nature Medicine

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 227

Benefit finding and post-traumatic growth in long-term colorectal cancer survivors: prevalence, determinants, and associations with quality of life

Jansen, L., Hoffmeister, M., Chang-Claude, J., Brenner, H., & Arndt, V (2011) · British Journal of Cancer

Cancer survivorship and careChildhood Cancer Survivors' Quality of Life

Cited by 152

Artificial intelligence for detection of microsatellite instability in colorectal cancer—a multicentric analysis of a pre-screening tool for clinical application

Echle, A., Ghaffari Laleh, N., Quirke, P., Grabsch, H. I., Muti, H. S., Saldanha, O. L., Brockmoeller, S. F., van den Brandt, P. A., Hutchins, G. G. A., Richman, S. D., et al. (2022) · ESMO Open

AI in cancer detectionGenetic factors in colorectal cancer

Cited by 111

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

El Nahhas, O. S. M., Loeffler, C. M. L., Carrero, Z. I., van Treeck, M., Kolbinger, F. R., Hewitt, K. J., Muti, H. S., Graziani, M., Zeng, Q., Calderaro, J., et al. (2024) · Nature Communications

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 108

End-to-end prognostication in colorectal cancer by deep learning: a retrospective, multicentre study

Jiang, X., Hoffmeister, M., Brenner, H., Muti, H. S., Yuan, T., Foersch, S., West, N. P., Brobeil, A., Jonnagaddala, J., Hawkins, N., et al. (2024) · The Lancet Digital Health

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 108

Utilisation of colorectal cancer screening tests in European countries by type of screening offer: results from the European Health Interview Survey

Cardoso, R., Guo, F., Heisser, T., Hoffmeister, M., & Brenner, H. (2020) · Cancers

Colorectal Cancer Screening and DetectionGlobal Cancer Incidence and Screening

Cited by 102

Benchmarking foundation models as feature extractors for weakly supervised computational pathology

Neidlinger, P., El Nahhas, O. S. M., Muti, H. S., Lenz, T., Hoffmeister, M., Brenner, H., van Treeck, M., Langer, R., Dislich, B., Behrens, H. M., et al. (2025) · Nature Biomedical Engineering

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 80

Genetic variants in DNA repair genes as potential predictive markers for oxaliplatin chemotherapy in colorectal cancer

Kap, E. J., Seibold, P., Richter, S., Scherer, D., Habermann, N., Balavarca, Y., Jansen, L., Becker, N., Pfütze, K., Popanda, O., et al. (2015) · The Pharmacogenomics Journal

Genetic factors in colorectal cancerColorectal Cancer Treatments and Studies

Cited by 20

Dose-Response Relationship between Serum Retinol Levels and Survival in Patients with Colorectal Cancer: Results from the DACHS Study

Maalmi, H., Walter, V., Jansen, L., Owen, R. W., Ulrich, A., Schöttker, B., Chang-Claude, J., Hoffmeister, M., & Brenner, H. (2018) · Nutrients

Vitamin D Research StudiesColorectal Cancer Treatments and Studies

Cited by 16

Benchmarking artificial intelligence methods for end-to-end computational pathology

Laleh, N. G., Muti, H. S., Lavinia Loeffler, C. M., Echle, A., Saldanha, O. L., Mahmood, F., Lu, M. Y., Trautwein, C., Langer, R., Dislich, B., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory)

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 13