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Radiological imaging contains enourmous amounts of information, much of which cannot be seen with the naked eye. This information is currently not routinely analyzed during the radiological interpretation process but contains valuable anatomical and pathological information. These data can be used to better detect and classify variants, diseases and can lead to quicker or improved treatment of diseases. Our group focusses on quantitative methods such as image analysis using Radiomics and Deep Learning (AI) to develop quantitative imaging biomarkers.

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Publikationen (Auswahl):

Diagnostic performance of PI-RADS version 2.1 compared to version 2.0 for detection of peripheral and transition zone prostate cancer

Rudolph MM, Baur ADJ, Cash H, Haas M, Mahjoub S, Hartenstein A, Hamm CA, Beetz NL, Konietschke F, Hamm B, Penzkofer T.

Sci Rep 2020, doi: 10.1038/s41598-020-72544-z


Optimizing size thresholds for detection of clinically significant prostate cancer on MRI: Peripheral zone cancers are smaller and more predictable than transition zone tumors.

Mahjoub S, Baur ADJ, Lenk J, Lee CH, Hartenstein A, Rudolph MM, Cash H, Hamm B, Asbach P, Haas M, Penzkofer T.

Eur J Radiol. 2020 Aug;129:109071. doi: 10.1016/j.ejrad.2020.109071. Epub 2020 May 17. PMID: 32531720.


Validation of the PI-RADS language: predictive values of PI-RADS lexicon descriptors for detection of prostate cancer

Rudolph MM, Baur ADJ, Haas M, Cash H, Miller K, Mahjoub S, Hartenstein A, Kaufmann D, Rotzinger R, Lee CH, Asbach P, Hamm B, Penzkofer T.

Eur Radiol. 2020 Mar 26. doi: 10.1007/s00330-020-06773-1. PMID: 32219507


Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone.

Hartenstein A, Lübbe F, Baur ADJ, Rudolph MM, Furth C, Brenner W, Amthauer H, Hamm B, Makowski M, Penzkofer T.

Sci Rep. 2020 Feb 25;10(1):3398. doi: 10.1038/s41598-020-60311-z.


Safety Analysis of Iobitridol as a Nonionic Contrast Medium: A Postmarketing Multicenter Surveillance Study With 94,960 Patients Almost 20 Years After Introduction.

Gorodetski B, Heine O, Wolf M, Collettini F, Hamm B, Darmon-Kern E, Penzkofer T.

Invest Radiol. 2020 Mar;55(3):144-152. doi: 10.1097/RLI.0000000000000620.


[Artificial intelligence and radiomics in MRI-based prostate diagnostics].

Hamm CA, Beetz NL, Savic LJ, Penzkofer T.

Radiologe. 2020 Jan;60(1):48-55. doi: 10.1007/s00117-019-00613-0. Review. German. PMID: 31802148


Evaluation of prostate MRI: can machine learning provide support where radiologists need it?

Baur ADJ, Penzkofer T.

Eur Radiol. 2019 Sep;29(9):4751-4753. doi: 10.1007/s00330-019-06241-5. Epub 2019 May 9. PMID: 31073858


Validation of Prostate Imaging Reporting and Data System Version 2 for the Detection of Prostate Cancer.

Hofbauer SL, Maxeiner A, Kittner B, Heckmann R, Reimann M, Wiemer L, Asbach P, Haas M, Penzkofer T, Stephan C, Friedersdorff F, Fuller F, Miller K, Cash H.

J Urol. 2018 Oct;200(4):767-773. doi: 10.1016/j.juro.2018.05.003. Epub 2018 May 5. PMID: 29733838


L1Base 2: more retrotransposition-active LINE-1s, more mammalian genomes.

Penzkofer T, Jäger M, Figlerowicz M, Badge R, Mundlos S, Robinson PN, Zemojtel T.

Nucleic Acids Res. 2017 Jan 4;45(D1):D68-D73. doi: 10.1093/nar/gkw925. Epub 2016 Oct 18. PMID: 27924012 Free PMC Article