The goal of our research is to improve individual patient diagnostics and therapy response monitoring in oncology. We employ laboratory model systems and advanced computational methods to develop and validate imaging-derived biomarkers.
Our preclinical work comprises the characterization of advanced tumor models including genetically engineered mouse models and PDX, the development of novel multi-modal imaging techniques (e.g. Magnetic Resonance Spectroscopic Imaging, Phase Contrast Computed Tomography) and the integrated anaylsis of imaging and morphomolecular data.
Our clinical work includes the curation of large, multi-institutional image and clinical data repositiores, the integrated modelling of image data and metadata for the improved non-invasive characterization of tumor heterogeneity and the prediction of individual patient therapy response and survival

Selected publications:

Multiparametric Modelling of Survival in Pancreatic Ductal Adenocarcinoma Using Clinical, Histomorphological, Genetic and Image-Derived Parameters.
Kaissis GA, Jungmann F, Ziegelmayer S, Lohöfer FK, Harder FN, Schlitter AM, Muckenhuber A, Steiger K, Schirren R, Friess H, Schmid R, Weichert W, Makowski MR, Braren RF.
J Clin Med. 2020 Apr 25;9(5):1250. doi: 10.3390/jcm9051250.

Combined DCE-MRI- and FDG-PET enable histopathological grading prediction in a rat model of hepatocellular carcinoma.
Kaissis GA, Lohöfer FK, Hörl M, Heid I, Steiger K, Munoz-Alvarez KA, Schwaiger M, Rummeny EJ, Weichert W, Paprottka P, Braren R.
Eur J Radiol. 2020 Mar;124:108848. doi: 10.1016/j.ejrad.2020.108848. Epub 2020 Jan 23. PMID: 32006931

Co-clinical Assessment of Tumor Cellularity in Pancreatic Cancer.
Heid I, Steiger K, Trajkovic-Arsic M, Settles M, Eßwein MR, Erkan M, Kleeff J, Jäger C, Friess H, Haller B, Steingötter A, Schmid RM, Schwaiger M, Rummeny EJ, Esposito I, Siveke JT, Braren RF.
Clin Cancer Res. 2017 Mar 15;23(6):1461-1470. doi: 10.1158/1078-0432.CCR-15-2432. Epub 2016 Sep 23. PMID: 27663591 

https://www.rad.mri.tum.de/ag/braren