Oncological imaging
Oncological imaging is becoming increasingly complex in the context of increasingly individualized oncology. Our research group deals with various imaging and treatment techniques as well as the use of other technologies to continuously improve oncological patient care. We are open to cooperation and work closely with other research groups at the Institute and at national and international level. The Data Science Group, with which new methods for the automated evaluation and characterization of oncological findings supported by artificial intelligence and machine learning are jointly developed, and the Experimental Imaging and Image-guided Therapy Research Group, which focuses on research into interventions using MRI-supported high-energy focused ultrasound waves (MR-HIFU) aimed at the treatment of tumors, should be highlighted.
Main research areas
In addition to the areas of MR-HIFU and data science mentioned above, the individualization of oncological imaging is another focus of research. For example, more and more structured tumour classifications and standardized response criteria, such as the Response Evaluation Criteria In Solid Tumors (RECIST), are finding their way into the daily routine of radiological oncological diagnostics.
Computed tomography (CT) and magnetic resonance imaging (MRI) usually allow high-resolution imaging of tumors and a determination of the treatment response of classic chemotherapies based on the change in size. However, new targeted molecular therapeutics often only lead to a change in tumor physiology, such as tumor perfusion, without a measurable change in size.
Our motivation is the development and research of modern quantitative and functional imaging methods that enable the reliable determination of tumor perfusion, tumor vascular permeability and tumor vessel diameter. Thanks to the excellent link between research and clinical practice at our institute, these modern techniques can be transferred directly into clinical examination protocols and used in patients for improved tumor characterization (staging) and early evaluation of the response to therapy.
Current research areas include the development and evaluation of new methods in artificial intelligence (AI) and structured diagnostics funded by the German Research Foundation (DFG), German Cancer Aid (DKH) and the European Union (EU): radCIO Cologne project, Radiomics
Quantification of relative blood volume (rBV) using ΔR2* relaxometry for tumor tissue characterization and early therapy monitoring
MR vessel size imaging using ΔR2/ ΔR2* relaxometry
Pleural mesothelioma evaluation according to RECIST 1.0, RECIST 1.1, mRECIST and WHO
Evaluation of manual versus semi-automatic response classification (RECIST 1.1, Cheson and RANO)
Evaluation of DCE-MRI in lung tumors (MIMEB, BARIS study)
Detection and progression assessment of lung tumors in the transgenic mouse model using CT volumetry
Evaluation of MR sequences for in-vivo monitoring of Autosomal Dominant Polycystic Kidney Disease (ADPKD) in the JCK mouse model
Retrospective evaluation of pulmonary (atypical) infiltrates in patients with aplasia, semi-automated analysis of morphologic image parameters (Prof. Schwartz, Columbia University)
DCE-MRI phantom study to evaluate the impact of parallel imaging and B1 inhomogeneities at different MR field strengths of 1.0T, 1.5T, and 3.0T (Quantitative Imaging Biomarkers Alliance: QIBA)
Reproducibility of the determination of quantitative DCE-MRI and DWI in the prostate in patients with prostate cancer (American College Of Radiology Imaging Network: ACRIN 6701)
Copy header from old website with links to publication lists and add link to Siedek's publication list (see team page)
All publications: Persigehl, Heneweer, Püsken, Siedeck
1. Persigehl T, Lennartz S, Schwartz LH. iRECIST: how to do it. Cancer Imaging. 2020 Jan 3;20(1):2.
2. Zopfs D, Laukamp KR, Pinto Dos Santos D, Sokolowski M, Große Hokamp N, Maintz D, Borggrefe J, Persigehl T, Lennartz S. Low-keV virtual monoenergetic imaging reconstructions of excretory phase spectral dual-energy CT in patients with urothelial carcinoma: A feasibility study. Eur J Radiol. 2019 Jul;116:135-143.
3 Baeßler B, Maintz D, Persigehl T. Imaging Procedures for Colorectal Cancer. Visc Med. 2016 Jun;32(3):166-71.
4. Persigehl T, Ring J, Bremer C, Heindel H, Holtmeier R, Stypmann J, Claesener M, Hermann S, Schäfers M, Zerbst C, Schliemann C, Mesters R, Berdel W, Schwöppe C. Non-invasive monitoring of tumor-vessel infarction by retargeted truncated tissue factor tTF-NGR using multi-modal imaging. Angiogenesis 2014 Jan; 17(1): 235-46.
5 Persigehl T, Ring J, Budny T, Hahnenkamp A, Stoeppeler S, Schwartz LH, Spiegel HU, Heindel W, Remmele S, Bremer C. Vessel Size Imaging (VSI) by Robust Magnetic Resonance (MR) Relaxometry: MR-VSI of Solid Tumors in Correlation with Immunohistology and Intravital Microscopy. Molecular Imaging 2013 Oct;12(7):1-11.
6 Lee HJ, Kim YT, Kang CH, Zhao B, Tan Y, Schwartz LH, Persigehl T, Jeon YK, Chung DH. Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinomas: Relationship with CT Characteristics and Histologic Subtypes. Radiology 2013 Jul; 268(1):254-64. IF 6.3 (IF 2012)
7 Persigehl T, Wall A, Kellert J, Ring J, Remmele S, Heindel W, Dahnke H, Bremer C. Tumor blood volume determination by susceptibility-corrected ΔR2* multi-echo MRI. Radiology; 255(3): 781-9.2010.
8. Bieker R, Kessler T, Schwöppe C, Padró T, Persigehl T, Bremer C, Dreischalück J, Kolkmeyer A, Heindel W, Mesters R, Berdel WE. Infarction of tumor vessels by NGR-peptide directed targeting of tissue factor. Experimental results and first-in-man experience. Blood. 2009 May 14;113(20):5019-27.
Further information
- Information on our areas of focus
- Technical details about the equipment at our institute
- Find out more about oncological imaging in clinical practice at our institute here