Junior Research Group Digital Biomarkers for Oncology

Dr. Titus Brinker

Artificial intelligence methods are applied to identify digital biomarkers - as shown here in a tissue section of the skin - in order to enable more precise diagnostics and improved therapy selection.
© dkfz.de

The development of digital biomarkers for non-invasive early detection, individualized diagnostic and therapeutic approaches as well as the prediction of tumor behavior is the research focus of our interdisciplinary research group. Scientists from the fields of medicine, molecular biology and data science focus on identifying relevant patterns in image data and their combination with other data such as results of molecular analysis or clinical data.

Our current activities focus on two major projects:

  • The Skin Classification Project (SCP2) focuses on improving the accuracy of skin cancer screenings. With a network of 8 university hospitals throughout Germany, we develop and optimize AI-classifiers for the clinical routine. Our long-term goal is to demonstrate successful on-site implementation in prospective clinical trials, making AI-assisted skin cancer diagnostics the new standard of care.
  • Within the Tumor Behavior Prediction Initiative (TPI), tumor behavior is predicted based on histologic slides. A crucial advantage of this approach is that tissue samples are taken anyway in clinical cancer diagnostics. With the help of artificial intelligence, we want to identify biomarkers on the digitized slides, which could be used in the future with minimal additional time and financial effort as a decision-making aid by the treating physician and represent a further building block for personalized medicine.
A special focus of our research group is on the translation of AI-based methods into the clinical routine. Therefore, we develop methods to improve the explainability of the models and their robustness in everyday use. Our broad network of clinical cooperation partners as well as patient organizations is closely involved in the development of research questions and the subsequent analyses.


Dr. Titus Brinker
Digital Biomarkers for Oncology (C140)
Deutsches Krebsforschungszentrum
Im Neuenheimer Feld 223
69120 Heidelberg
Tel: +49 6221 425301

Selected Publications

  • Hekler A, Utikal JS, Enk AH, Hauschild A, Weichenthal M, Maron RC, Berking C, Haferkamp S, Klode J, Schadendorf D, Schilling B, Holland-Letz T, Izar B, von Kalle C, Fröhling S, Brinker TJ; Collaborators (2019). Superior skin cancer classification by the combination of human and artificial intelligence. Eur J Cancer, 120, 114-121
  • Brinker TJ, Hekler A, Enk AH, et al (2019). Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer, 113, 47-54.
  • Brinker TJ, Hekler A, Enk AH, Berking C, Haferkamp S, Hauschild A, Weichenthal M, Klode J, Schadendorf D, Holland-Letz T, von Kalle C, Fröhling S, Schilling B, Utikal JS (2019). Deep neural networks are superior to dermatologists in melanoma image classification. Eur J Cancer, 119, 11-17
  • Hekler A, Utikal JS, Enk AH, Solass W, Schmitt M, Klode J, Schadendorf D, Sondermann W, Franklin C, Bestvater F, Flaig MJ, Krahl D, von Kalle C, Fröhling S, Brinker TJ (2019). Deep learning outperformed 11 pathologists in the classification of histopathological melanoma images. Eur J Cancer, 119, 91-96
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