Dr. Lorenz Kapsner

d.hip

Radiologisches Institut
Lehrstuhl für Diagnostische Radiologie

Henkestraße 91
91054 Erlangen

Telefon: +49 9131 85-36065
Faxnummer: +49 9131 85-36068
E-Mail: lorenz.kapsner@uk-erlangen.de
Webseite: http://www.radiologie-erlangen.de

Journal Article

2025
- A. Liebert, D. Hadler, C. Ehring, H. Schreiter, L. Brock, L.A. Kapsner, J. Eberle, R. Erber, J. Emons, F.B. Laun, M. Uder, E. Wenkel, S. Ohlmeyer, and S. Bickelhaupt, Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks, Eur Radiol Exp. 9 (2025) 47. doi:10.1186/s41747-025-00580-3.
- J. Ziegler, M.P. Erpenbeck, T. Fuchs, A. Saibold, P.-C. Volkmer, G. Schmidt, J. Eicher, P. Pallaoro, R. De Souza Falguera, F. Aubele, M. Hagedorn, E. Vansovich, J. Raffler, S. Ringshandl, A. Kerscher, J.K. Maurer, B. Kühnel, G. Schenkirsch, M. Kampf, L.A. Kapsner, H. Ghanbarian, H. Spengler, I. Soto-Rey, F. Albashiti, D. Hellwig, M. Ertl, G. Fette, D. Kraska, M. Boeker, H.-U. Prokosch, and C. Gulden, Bridging Data Silos in Oncology with Modular Software for Federated Analysis on Fast Healthcare Interoperability Resources: Multisite Implementation Study, J Med Internet Res. 27 (2025) e65681. doi:10.2196/65681.
- A. Iancu, J. Bauer, M.S. May, H.-U. Prokosch, A. Dörfler, M. Uder, and L.A. Kapsner, Large-Scale Integration of DICOM Metadata into HL7-FHIR for Medical Research, Methods Inf Med. (2025) a-2521-4250. doi:10.1055/a-2521-4250.
2024
- L.A. Kapsner, M. Feißt, A. Purbojo, H.-U. Prokosch, T. Ganslandt, S. Dittrich, J.M. Mang, and W. Wällisch, Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery, Diagnostics. 14 (2024) 2587. doi:10.3390/diagnostics14222587.
- D. Skwierawska, F.B. Laun, E. Wenkel, L.A. Kapsner, R. Janka, M. Uder, S. Ohlmeyer, and S. Bickelhaupt, Diffusion-Weighted Imaging for Skin Pathologies of the Breast—A Feasibility Study, Diagnostics. 14 (2024) 934. doi:10.3390/diagnostics14090934.
- A. Liebert, H. Schreiter, L.A. Kapsner, J. Eberle, C.M. Ehring, D. Hadler, L. Brock, R. Erber, J. Emons, F.B. Laun, M. Uder, E. Wenkel, S. Ohlmeyer, and S. Bickelhaupt, Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network, Eur Radiol. (2024). doi:10.1007/s00330-024-11142-3.
- L.A. Kapsner, L. Folle, D. Hadler, J. Eberle, E.L. Balbach, A. Liebert, T. Ganslandt, E. Wenkel, S. Ohlmeyer, M. Uder, and S. Bickelhaupt, Lesion-conditioning of synthetic MRI-derived subtraction-MIPs of the breast using a latent diffusion model, Sci Rep. 14 (2024) 6391. doi:10.1038/s41598-024-56853-1.
2023
- A. Liebert, B.K. Das, L.A. Kapsner, J. Eberle, D. Skwierawska, L. Folle, H. Schreiter, F.B. Laun, S. Ohlmeyer, M. Uder, E. Wenkel, and S. Bickelhaupt, Smart forecasting of artifacts in contrast-enhanced breast MRI before contrast agent administration, (2023). doi:10.1007/s00330-023-10469-7.
- L.A. Kapsner, E.L. Balbach, F.B. Laun, L. Baumann, S. Ohlmeyer, M. Uder, S. Bickelhaupt, and E. Wenkel, Prevalence and influencing factors for artifact development in breast MRI-derived maximum intensity projections, Acta Radiologica. (2023). doi:10.1177/02841851231198349.
- L.A. Kapsner, E.L. Balbach, L. Folle, F.B. Laun, A.M. Nagel, A. Liebert, J. Emons, S. Ohlmeyer, M. Uder, E. Wenkel, and S. Bickelhaupt, Image quality assessment using deep learning in high b-value diffusion-weighted breast MRI, Sci Rep. 13 (2023) 10549. doi:10.1038/s41598-023-37342-3.
2022
- J.M. Mang, S.A. Seuchter, C. Gulden, S. Schild, D. Kraska, H.-U. Prokosch, and L.A. Kapsner, DQAgui: a graphical user interface for the MIRACUM data quality assessment tool, BMC Med Inform Decis Mak. 22 (2022) 213. doi:10.1186/s12911-022-01961-z.
- J. Mariño, E. Kasbohm, S. Struckmann, L.A. Kapsner, and C.O. Schmidt, R Packages for Data Quality Assessments and Data Monitoring: A Software Scoping Review with Recommendations for Future Developments, Applied Sciences. (2022) 26. doi:10.3390/app12094238.
- L.A. Kapsner, S. Ohlmeyer, L. Folle, F.B. Laun, A.M. Nagel, A. Liebert, H. Schreiter, M.W. Beckmann, M. Uder, E. Wenkel, and S. Bickelhaupt, Automated artifact detection in abbreviated dynamic contrast-enhanced (DCE) MRI-derived maximum intensity projections (MIPs) of the breast, Eur Radiol. (2022). doi:10.1007/s00330-022-08626-5.
- L. Folle, S. Bayat, A. Kleyer, F. Fagni, L.A. Kapsner, M. Schlereth, T. Meinderink, K. Breininger, K. Tacilar, G. Krönke, M. Uder, M. Sticherling, S. Bickelhaupt, G. Schett, A. Maier, F. Roemer, and D. Simon, Advanced neural networks for classification of MRI in psoriatic arthritis, seronegative, and seropositive rheumatoid arthritis, Rheumatology. (2022). doi:10.1093/rheumatology/keac197.
- D. Medenwald, T. Brunner, H. Christiansen, U. Kisser, S. Mansoorian, D. Vordermark, H.-U. Prokosch, S.A. Seuchter, L.A. Kapsner, on behalf of our MII research group, Shift of radiotherapy use during the first wave of the COVID-19 pandemic? An analysis of German inpatient data, Strahlenther Onkol. (2022). doi:10.1007/s00066-021-01883-1.
2021
- L.A. Kapsner, J.M. Mang, S. Mate, S.A. Seuchter, A. Vengadeswaran, F. Bathelt, N. Deppenwiese, D. Kadioglu, D. Kraska, and H.-U. Prokosch, Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository, Appl Clin Inform. 12 (2021) 826–835. doi:10.1055/s-0041-1733847.
- L.A. Kapsner, M.G. Zavgorodnij, S.P. Majorova, A. Hotz‐Wagenblatt, O.V. Kolychev, I.N. Lebedev, J.D. Hoheisel, A. Hartmann, A. Bauer, S. Mate, H. Prokosch, F. Haller, and E.A. Moskalev, BiasCorrector: fast and accurate correction of all types of experimental biases in quantitative DNA methylation data derived by different technologies, Int. J. Cancer. (2021) ijc.33681. doi:10.1002/ijc.33681.
- J. Schüttler, J.M. Mang, L.A. Kapsner, S.A. Seuchter, H. Binder, D. Zöller, O. Kohlbacher, M. Boeker, G. Zacharowski, G. Rohde, J. Balig, M.O. Kampf, R. Röhrig, and H.-U. Prokosch, Letalität von Patienten mit COVID-19: Untersuchungen zu Ursachen und Dynamik an deutschen Universitätsklinika, Anästh Intensivmed. 61 (2021). doi: 10.19224/ai2021.244
- C. Maier, L.A. Kapsner, S. Mate, H.-U. Prokosch, and S. Kraus, Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model, Appl Clin Inform. 12 (2021) 057–064. doi:10.1055/s-0040-1721481.
- L.A. Kapsner, M.O. Kampf, S.A. Seuchter, J. Gruendner, C. Gulden, S. Mate, J.M. Mang, C. Schüttler, N. Deppenwiese, L. Krause, D. Zöller, J. Balig, T. Fuchs, P. Fischer, C. Haverkamp, M. Holderried, G. Mayer, H. Stenzhorn, A. Stolnicu, M. Storck, H. Storf, J. Zohner, O. Kohlbacher, A. Strzelczyk, J. Schüttler, T. Acker, M. Boeker, U.X. Kaisers, H.A. Kestler, and H.-U. Prokosch, Reduced Rate of Inpatient Hospital Admissions in 18 German University Hospitals During the COVID-19 Lockdown, Front. Public Health. 8 (2021) 594117. doi:10.3389/fpubh.2020.594117.
2020
- M. Fuchs, F.P. Kreutzer, L.A. Kapsner, S. Mitzka, A. Just, F. Perbellini, C.M. Terracciano, K. Xiao, R. Geffers, C. Bogdan, H.-U. Prokosch, J. Fiedler, T. Thum, and M. Kunz, Integrative Bioinformatic Analyses of Global Transcriptome Data Decipher Novel Molecular Insights into Cardiac Anti-Fibrotic Therapies, IJMS. 21 (2020) 4727. doi:10.3390/ijms21134727.
- J. Zierk, F. Arzideh, L.A. Kapsner, H.-U. Prokosch, M. Metzler, and M. Rauh, Reference Interval Estimation from Mixed Distributions using Truncation Points and the Kolmogorov-Smirnov Distance (kosmic), Sci Rep. 10 (2020) 1704. doi:10.1038/s41598-020-58749-2.
2019
- J. Vey, L.A. Kapsner, M. Fuchs, P. Unberath, G. Veronesi, and M. Kunz, A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning, Cancers. 11 (2019) 14. doi:10.3390/cancers11101606
- S. Mate, T. Bürkle, L.A. Kapsner, D. Toddenroth, M. Kampf, M. Sedlmayr, I. Castellanos, H.-U. Prokosch, and S. Kraus, A Method for the Graphical Modeling of Relative Temporal Constraints, J. Biomed. Inform. (2019) 103314. doi:10.1016/j.jbi.2019.103314.
- J. Gruendner, T. Schwachhofer, P. Sippl, N. Wolf, M. Erpenbeck, C. Gulden, L.A. Kapsner, J. Zierk, S. Mate, M. Stürzl, R. Croner, H.-U. Prokosch, and D. Toddenroth, KETOS: Clinical decision support and machine learning as a service – A training and deployment platform based on Docker, OMOP-CDM, and FHIR Web Services, PLOS ONE. 14 (2019) e0223010. doi:10/gf93pm.
- O.Y. Tektas, L. Kapsner, M. Lemmer, P. Bouna-Pyrrou, P. Lewczuk, B. Lenz, and J. Kornhuber, Digit ratio (2D:4D) and academic success as measured by achievement in the academic degree “Habilitation,” PLOS ONE. 14 (2019) e0212167. doi:10/gf93pd.

Conference Contributions

2025
- H. Schreiter, J. Eberle, L.A. Kapsner, D. Hadler, S. Ohlmeyer, R. Erber, J. Emons, F.B. Laun, M. Uder, E. Wenkel, S. Bickelhaupt, and A. Liebert, Virtual dynamic contrast enhanced breast MRI using 2D u-net architectures, in: R.M. Mann, T. Zhang, T. Tan, L. Han, D. Truhn, S. Li, Y. Gao, S. Doyle, R. Martí Marly, J.N. Kather, K. Pinker-Domenig, S. Wu, and G. Litjens (Eds.), Artificial Intelligence and Imaging for Diagnostic and Treatment Challenges in Breast Care, Springer Nature Switzerland, Cham, 2025: pp. 85–95. doi: 10.1007/978-3-031-77789-9_9
- A. George, H. Schreiter, J. Hossbach, T.-T. Nguyen, I. Horishnyi, S. Heidarikahkesh, L.A. Kapsner, F.B. Laun, M. Uder, S. Ohlmeyer, S. Bickelhaupt, and A. Liebert, U-Net and GAN for Virtual Contrast in Breast MRI – How Do They Compare to Real Contrast Images?, Springer Fachmedien Wiesbaden, Wiesbaden, 2025. doi:10.1007/978-3-658-47422-5_63.
2024
- L. Brock, A. Liebert, H. Schreiter, D. Skwierawska, C. Ehring, J. Eberle, F.B. Laun, M. Uder, L. Kapsner, S. Ohlmeyer, D. Hadler, F. Knoll, and S. Bickelhaupt, Comparative Study on Co-registration Techniques for Diffusion-Weighted Breast MRI and Improved ADC Mapping, in: M. Modat, I. Simpson, Z. Spiclin, W. Bastiaansen, A. Hering, and T.C. Mok (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Science and Business Media Deutschland GmbH, 2024: pp. 127–136. doi:10.1007/978-3-031-73480-9_10.
2023
- J.M. Mang, H.-U. Prokosch, and L.A. Kapsner, Reproducibility in 2023 – An End-to-End Template for Analysis and Manuscript Writing, in: M. Hägglund, M. Blusi, S. Bonacina, L. Nilsson, I. Cort Madsen, S. Pelayo, A. Moen, A. Benis, L. Lindsköld, and P. Gallos (Eds.), Studies in Health Technology and Informatics, IOS Press, 2023. doi:10.3233/SHTI230064.
2022
- H.-U. Prokosch, T. Bahls, M. Bialke, J. Eils, C. Fegeler, J. Gruendner, B. Haarbrandt, C. Hampf, W. Hoffmann, H. Hund, M. Kampf, L.A. Kapsner, P. Kasprzak, O. Kohlbacher, D. Krefting, J.M. Mang, M. Marschollek, S. Mate, A. Müller, F. Prasser, J. Sass, S. Semler, H. Stenzhorn, S. Thun, S. Zenker, and R. Eils, The COVID-19 Data Exchange Platform of the German University Medicine, in: B. Séroussi, P. Weber, F. Dhombres, C. Grouin, J.-D. Liebe, S. Pelayo, A. Pinna, B. Rance, L. Sacchi, A. Ugon, A. Benis, and P. Gallos (Eds.), Studies in Health Technology and Informatics, IOS Press, 2022. doi:10.3233/SHTI220554.
- L. Folle, K. Tkotz, F. Gadjimuradov, L.A. Kapsner, M. Fabian, S. Bickelhaupt, D. Simon, A. Kleyer, G. Krönke, M. Zaiß, A. Nagel, and A. Maier, Towards super-resolution CEST MRI for visualization of small structures, in: K. Maier-Hein, T.M. Deserno, H. Handels, A. Maier, C. Palm, and T. Tolxdorff (Eds.), Bildverarbeitung Für Die Medizin 2022, Springer Fachmedien Wiesbaden, Wiesbaden, 2022: pp. 210–215. doi: 10.1007/978-3-658-36932-3_45
2021
- S. Neher, L.A. Kapsner, H.-U. Prokosch, and D. Toddenroth, Design of an Interactive Web Application for Teaching Uncertainty Interpretations of Clinical Tests, Studies in Health Technology and Informatics. (2021). doi:10.3233/SHTI210554.
- D. Caliskan, J. Zierk, D. Kraska, S. Schulz, P. Daumke, H.-U. Prokosch, and L.A. Kapsner, First Steps to Evaluate an NLP Tool’s Medication Extraction Accuracy from Discharge Letters, Studies in Health Technology and Informatics. (2021). doi:10.3233/SHTI210073.
- S. Mate, S.A. Seuchter, K. Ehrenberg, N. Deppenwiese, J. Zierk, H.-U. Prokosch, D. Kraska, and L.A. Kapsner, A Multi-User Terminology Mapping Toolbox, Studies in Health Technology and Informatics. (2021). doi:10.3233/SHTI210072.
2020
- L.A. Kapsner, M.O. Kampf, S.A. Seuchter, G. Kamdje-Wabo, T. Gradinger, T. Ganslandt, S. Mate, J. Gruendner, D. Kraska, and H.-U. Prokosch, Moving Towards an EHR Data Quality Framework: The MIRACUM Approach, Stud. Health Technol. Inform. (2019) 247–253. doi:10.3233/SHTI190834

R-Packages (CRAN-listed)

- L.A. Kapsner, mlexperiments: Machine Learning Experiments, (2023).
  https://CRAN.R-project.org/package=mlexperiments.
- L.A. Kapsner, mllrnrs: R6-Based ML Learners for ‚mlexperiments‘, (2023).
  https://CRAN.R-project.org/package=mllrnrs.
- L.A. Kapsner, mlsurvlrnrs: R6-Based ML Survival Learners for ‚mlexperiments‘, (2023).
  https://CRAN.R-project.org/package=mlexperiments.
- L.A. Kapsner, kdry: K’s „Don’t Repeat Yourself“-Collection, (2023).
  https://CRAN.R-project.org/package=kdry.
- L.A. Kapsner, autonewsmd: Auto-generate changelog using conventional commits, (2022).
  https://CRAN.R-project.org/package=autonewsmd.
- L.A. Kapsner, sjtable2df: Convert “sjPlot” HTML-Tables to R “data.frame,” (2022).
  https://CRAN.R-project.org/package=sjtable2df.
- L.A. Kapsner, and E.A. Moskalev, BiasCorrector: A GUI to correct measurement bias in DNA methylation analyses, (2021).
  https://CRAN.R-project.org/package=BiasCorrector.
- L.A. Kapsner, and E.A. Moskalev, rBiasCorrection: Correct bias in DNA methylation analyses, (2021).
  https://CRAN.R-project.org/package=rBiasCorrection.
- J.M. Mang, and L.A. Kapsner, DIZtools: Lightweight Utilities for “DIZ” R package development, (2022).
  https://CRAN.R-project.org/package=DIZtools.
- J.M. Mang, and L.A. Kapsner, DIZutils: Utilities for “DIZ” R package development, (2021).
  https://CRAN.R-project.org/package=DIZutils.
- L.A. Kapsner, and J.M. Mang DQAstats: Core Functions for Data Quality Assessment, (2022).
  https://CRAN.R-project.org/package=DQAstats.
- L.A. Kapsner, and J.M. Mang DQAgui: Graphical User Interface for Data Quality Assessment, (2022).
  https://CRAN.R-project.org/package=DQAgui.

Further R-packages

- L.A. Kapsner, and J.M. Mang, miRacumDQA: MIRACUM DQA tool, (2021).
  https://gitlab.miracum.org/miracum/dqa/miRacumDQA.
- M. Fuchs, and L.A. Kapsner, tRomics: GUI for transcriptome profiling analysis, (2020).
  https://gitlab.miracum.org/clearly/tromics.
- L.A. Kapsner, and J. Vey, sigident: Signature analyses in genomic expression sets, (2020).
  https://github.com/miracum/clearly-sigident.
- L.A. Kapsner, J. Vey, M. Kunz, and A. Pittroff, sigident.preproc: Sigident preprocessing, (2020).
  https://github.com/miracum/clearly-sigident.preproc.
- L.A. Kapsner, J. Vey, M. Kunz, and A. Pittroff, sigident.func: Sigident functional analysis, (2020).
  https://github.com/miracum/clearly-sigident.func.
- L.A. Kapsner, and J. Zierk, kosmicGUI: GUI for reference interval estimation, (2021).
  https://gitlab.miracum.org/kosmic/kosmicr.
- L.A. Kapsner, labVisualizeR: labVisualizeR – an interactive web application to visualize measurements from SQL databases, (2020).
  
  https://github.com/miracum/labor-labvisualizer.
- L.A. Kapsner, mlr3learners.lightgbm: mlr3: LightGBM Learner, (2021).
  https://github.com/mlr3learners/mlr3learners.lightgbm.