Radiomics Research Group (Bonekamp)

Research Focus

Radiologic images uniquely represent the spatial fingerprints of disease progress and treatment response over time. “Radiomics” was coined to give a name to the emerging endeavor to systematically extract, mine and leverage this rich information in a personalized medicine approach. The Department of Radiology, the Department of Medical Physics and the Junior Group Medical Image Computing have established and studied comprehensive MRI imaging phenotypes for prostate cancer, breast cancer and brain tumors in close collaboration with the University Hospital. This has already enabled patient stratification and direct links between quantitative imaging information and clinical, biological, genomic or proteomic parameters. Research topics include automated anatomical structure detection and lesion segmentation and exploration of quantitative imaging biomarkers. Our special interest lies in investigating the use of data-driven paradigms such as deep and weak learning strategies for building robust models and tapping the full potential of the information encoded in the images. To achieve successful validation and translation of the developed computational techniques, we have established a strong research software engineering group with matched technology performance that covers interactive exploration as well as high-throughput automated analysis of imaging data.

 

NCT 3.0 – Precision Oncology Funding Program

Contact: Bonekamp/Radbruch/Maier-Hein

The department of radiology at the DKFZ participates in the above program with a project titled “From “dark” data to radiomics – oncologic radiology integrated with informatics to enable radiomics in personalized tumor therapy”. The strategic priority of this project is to create the “Extension Radiology Platform” is to establish the field of radiomics in the complex research and clinical environment of the NCT. Aim is to develop the future tools, workflow concepts and imaging protocols of oncologic radiology for integration into cancer research and treatment by making use of imaging data in novel ways that rely heavily on informatics and information technology. Large amounts of data generated by radiologic modalities and tumor molecular (genomic, proteomic, …) analyses are to be integrated with histology, tumor grading/staging, proliferation markers, and treatment outcome for as many patients as possible by dedicated infrastructure, workflow, storage and retrieval (e.g. by tight integration into the NCT DataTherehouse architecture) and analysis methodology. The developed methods will be applied in clinical studies with the ultimate aim of less invasive, more precise and personalized tumor therapy. The systematic high throughput extraction of advanced quantitative imaging features from massively available but so far „dark“ image data will yield novel patient-individual models with diagnostic, prognostic or predictive power. Within the program, NCT Oncologists and DKFZ radiologists collaborate closely (1). Tools have been developed enabling advanced image analysis which have been applied in brain tumor research (2-4) and breast cancer MRI research (5).

Selected Publications

1.        Dieter SM, Heining C, Agaimy A, Huebschmann D, Bonekamp D, Hutter B, Ehrenberg KR, Frohlich M, Schlesner M, Scholl C, Schlemmer HP, Wolf S, Mavratzas A, Jung CS, Groschel S, von Kalle C, Eils R, Brors B, Penzel R, Kriegsmann M, Reuss DE, Schirmacher P, Stenzinger A, Federspil PA, Weichert W, Glimm H, Frohling S. Mutant KIT as imatinib-sensitive target in metastatic sinonasal carcinoma. Annals of oncology : official journal of the European Society for Medical Oncology. 2016.

2.        Kickingereder P, Gotz M, Muschelli J, Wick A, Neuberger U, Shinohara RT, Sill M, Nowosielski M, Schlemmer HP, Radbruch A, Wick W, Bendszus M, Maier-Hein KH, Bonekamp D. Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response. Clinical cancer research : an official journal of the American Association for Cancer Research. 2016;22(23):5765-71.

3.        Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, Maier-Hein KH, Wick W, Bendszus M, Radbruch A, Bonekamp D. Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology. 2016;280(3):880-9.

4.        Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, Wick A, Eidel O, Schlemmer HP, Radbruch A, Debus J, Herold-Mende C, Unterberg A, Jones D, Pfister S, Wick W, von Deimling A, Bendszus M, Capper D. Radiogenomics of Glioblastoma: Machine Learning-based Classification of Molecular Characteristics by Using Multiparametric and Multiregional MR Imaging Features. Radiology. 2016;281(3):907-18.

5.        Bickelhaupt S, Paech D, Kickingereder P, Steudle F, Lederer W, Daniel H, Gotz M, Gahlert N, Tichy D, Wiesenfarth M, Laun FB, Maier-Hein KH, Schlemmer HP, Bonekamp D. Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography. Journal of magnetic resonance imaging : JMRI. 2017.

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