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Colorectal cancer (CRC) ranks second in leading causes of cancer-related deaths globally, according to the WHO. For the first time, researchers from Helmholtz Munich and the University of Technology Dresden (TU Dresden) show that artificial intelligence (AI)-based predictions can deliver comparable results to clinical tests on biopsies of patients with CRC. AI predictions can speed up the analysis of tissue samples, resulting in faster treatment decisions. This novel model for biomarker detection represents a significant stride towards the realization of precision therapy approaches in the field of oncology. The method is now published in Cancer Cell.
A team of scientists around Dr. Tingying Peng from Helmholtz Munich and Prof. Jakob N. Kather from TU Dresden show that AI can predict specific biomarkers in stained tissue samples of patients with CRC. They used so-called transformer networks, a recent deep learning (DL) approach, to identify patterns and support diagnostic decisions in cancer management. The new method significantly improves previous approaches for biomarker detection.
Large-Scale Evaluation Proves Better Generalization and Data-Efficiency
The team of researchers developed software that uses the new technology of transformer neural networks throughout the analysis process. They show that their approach substantially improves the performance, generalizability, data efficiency, and interpretability by evaluating it on a large multicentric cohort of over 13,000 patients from 16 cohorts from seven countries (Australia, China, Germany, Israel, Netherlands, UK, USA), part of which was contributed by researchers at the German Cancer Research Center (DKFZ) Heidelberg and the network of the National Centers for Tumor Diseases (NCT). The algorithm trained on the large multicentric cohort achieves a very high sensitivity on resection tissue samples obtained during surgery. Strikingly, even though their model has only been trained on tissue samples from resections, the results can reach also a high performance on biopsy tissue obtained during colonoscopy. Sophia J. Wagner, the first author of the study, emphasizes that “the generalization to biopsy tissue increases the algorithm’s benefit for the patient when ultimately implemented in clinical routine”.
AI-Based Pre-screening for Biopsies Accelerate Diagnosis
Because of its high sensitivity on biopsy tissue, the algorithm could serve as a pre-screening tool followed by affirmative testing for cases that received a positive result during AI testing. Applying AI-based biomarker prediction could reduce the testing burden and therefore speed up the step between taking the biopsy and the molecular determination of the genetic risk status, thus enabling an earlier patient treatment with immunotherapy if indicated.
Original publication
Sophia J. Wagner, Daniel Reisenbüchler, Nicholas P. West, Jan Moritz Niehues, Jiefu Zhu, Sebastian Foersch, Gregory Patrick Veldhuizen, Philip Quirke, Heike I. Grabsch, Piet A. van den Brandt, Gordon G. A. Hutchins, Susan D. Richman, Tanwei Yuan, Rupert Langer, Josien C. A. Jenniskens, Kelly Offermans, Wolfram Mueller, Richard Gray, Stephen B. Gruber, Joel K. Greenson, Gad Rennert, Joseph D. Bonner, Daniel Schmolze, Jitendra Jonnagaddala, Nicholas J. Hawkins, Robyn L. Ward, Dion Morton, Matthew Seymour, Laura Magill, Marta Nowak, Jennifer Hay, Viktor H. Koelzer, David N. Church, TransSCOT consortium, Christian Matek, Carol Geppert, Chaolong Peng, Cheng Zhi, Xiaoming Ouyang, Jacqueline A. James, Maurice B. Loughrey, Manuel Salto-Tellez, Hermann Brenner, Michael Hoffmeiste, Daniel Truhn, Julia A. Schnabel, Melanie Boxberg, Tingying Peng, Jakob Nikolas Kather (2023): Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell. https://doi.org/10.1016/j.ccell.2023.08.002
Additional Information
EKFZ for Digital Health: Founded in 2019, the Else Kröner Fresenius Center for Digital Health at TU Dresden is funded by the Else Kröner-Fresenius Foundation with a funding amount of 40 million euros over a period of ten years. The center focuses its research activities on innovative, medical and digital technologies at the direct interface with patients. The aim is to train a new generation of physicians with comprehensive technical knowledge and skills and, conversely, engineers with a comprehensive understanding of the needs of physicians and patients.
Helmholtz Munich is a leading biomedical research center. Its mission is to develop breakthrough solutions for better health in a rapidly changing world. Interdisciplinary research teams focus on environmentally triggered diseases, especially the therapy and prevention of diabetes, obesity, allergies, and chronic lung diseases. With the power of artificial intelligence and bioengineering, researchers accelerate the translation to patients. Helmholtz Munich has more than 2,500 employees and is headquartered in Munich/Neuherberg. It is a member of the Helmholtz Association, with more than 43,000 employees and 18 research centers the largest scientific organization in Germany. More about Helmholtz Munich (Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt GmbH): www.helmholtz-munich.de/en
Sophia J. Wagner, PhD candidate at the Peng Lab at Helmholtz Munich and at Helmholtz AI, Munich, Germany
Dr. Tingying Peng, Principal Investigator for AI for Microscopy at Helmholtz Munich and at Helmholtz AI, Munich, Germany
Contact: tingying.peng@helmholtz-munich.de
Prof. Dr. Jakob Nikolas Kather, Professor at the Else Kröner Fresenius Center for Digital Health (EFFZ), University of Technology Dresden, Dresden, Germany
Contact: ekfz@tu-dresden.de
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Cancer Cell. https://doi.org/10.1016/j.ccell.2023.08.002
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