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Junior Research Group Digital Biomarkers for Oncology

PD 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.

The main goal of the group is the development of robust and interpretable digital biomarkers to improve prevention, non-invasive early detection, diagnostic, and therapeutic approaches. A 20-member, almost fully externally funded team from the fields of medicine, molecular biology and informatics/data science focuses on identifying relevant patterns in patient data and increasing the explainability and robustness of deep learning-based classifications. We see software systems as part of clinical teams for more efficient patient care and at the same time as a tool for effective prevention and early detection. In the past (since 2020), we have achieved much-noticed scientific success in these areas; our more than 80 internationally peer-reviewed research papers have been cited more than 5,000 times and numerous project results have been picked up by international media. Software products or apps from our working group have been downloaded more than a million times.

Seven recently approved grants include the MiRisk consortium (1) which develops a free app to individually determine and minimize the risk for breast cancer. Within the BAP-1-consortium (2), we share & extend our expertise in building histology image pipelines to stratify patients for drug development. The Hector grant (3) enables us to integrate spatial transcriptomics for deep-learning-based heterogeneity scores to predict melanoma metastasis. The sKIn project (4) takes the remaining technical and formal steps to build our dermatologist-like skin cancer AI into dermatoscopes together with a company, bringing them into the hands of caregivers. MELCAYA (5) identifies new risk factors for melanoma in CAYAs. A deep learning strategy for high-throughput proteomics (6) allows higher resolution and faster processing of liquid biopsies. A signed collaboration with industry will lead to more individualized sunscreen recommendations based on epigenetic tests read from smartphone photographs via AI. Improved digital analysis of sarcomas (7), the interaction of language models and care, explainable AI algorithms for cancer screening and the optimization of the Sunface & Smokerface App also depict future plans of the group.

On the following pages you can find out more about the people in our team, their main areas of work and the projects our group is currently working on.


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

Selected Publications

  • Chanda, T., Hauser, K., Hobelsberger, S., Bucher, T. C., Garcia, C. N., Wies, C., ... & Brinker, T. J. (2024). Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma. Nature Communications, 15(1), 524.
  • Hetz, M. J., Bucher, T. C., & Brinker, T. J. (2024). Multi-domain stain normalization for digital pathology: A cycle-consistent adversarial network for whole slide images. Medical Image Analysis, 103149.
  • Brinker, T. J., Faria, B. L., de Faria, O. M., Klode, J., Schadendorf, D., Utikal, J. S., ... & Bernardes-Souza, B. (2020). Effect of a face-aging mobile app–based intervention on skin cancer protection behavior in secondary schools in Brazil: a cluster-randomized clinical trial. JAMA dermatology, 156(7), 737-745.
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