MLPI Research
Clinical research
In this Christian Doppler Laboratory, radiologists are conducting research together with AI specialists to further improve the informative value of CT images for the assessment of lung carcinomas. Currently, radiologists assess tumors and metastases primarily in terms of their size, density and location and their relationship to surrounding structures. By assisting with AI, tumors can be analyzed in a much more automated manner, thereby significantly improving the estimation of prognosis. With this project, we also expect to be able to support therapy decisions and ultimately improve the prognosis.
AI Research
We are developing machine learning methods for the assessment and prediction of treatment response in lung cancer. We are specifically focussing on methodology that bridges imaging phenotype data, and molecular characteristics of the tumor, and the biological processes of disease progression. To enable wide and sustainable applicability of the methods, we will investigate approaches to cope with heterogeneity of data, and the continual change of acquisition technology and treatment options.
Legal Research
In addition to ensuring respect for individual patients' privacy rights the general legal compliance of project activities, we are interested in broader legal questions related to data sharing multi-party medical research projects. Specifically, we are interested in the impact of existing and upcoming legislation on fostering and/or hindering data-rich, multi-party medical research projects such as CD-Laboratory for Machine Learning Driven Precision Imaging. We also hope to develop best practices on how to navigate the legal landscape for partners involved in such projects.
Publications
- Mura R, Kifjak D, Beer L, Prosch H. “Lung cancer screening: current knowledge, opportunities, and challenges ”. Memo - Magazine of European Medical Oncology, Volume 19, pages 13–17, (2026)
- Beer L, Hochmair M, Widder J, Hoda MA, Helmberger T. “ESR Bridges: lung cancer: new developments in imaging and treatment-a multidisciplinary view”. Eur Radiolog 2025 Nov,35(11):7150 7152
- Kifjak D, Mura R, Khenkina N, Pochepnia S, Heidinger BH, Milos RI, Beer L, Prosch H. “Something old something new-introduction to the ninth edition TNM classification of lung cancer”. Br J Radiol 2025 Oct 1,98(1174):1543 1555
- Kifjak D, Mura R, Pochepnia S, Bogveradze N, Korajac A, Heidinger BH, Milos RI, Beer L, Prosch H. “Beyond the usual - Atypical imaging presentation in lung cancer and implications for TNM-staging”. Curr Opin Oncol. 2025 Oct 30,38(1):31-38.
- Herold A, Sobotka D, Beer L, Bastati N, Poetter-Lang S, Weber M, Reiberger T, Mandorfer M, Semmler G, Simmbrunner B, Wichtmann B, Ba-Ssalamah S, Trauner M, Ba-Ssalamah A, Langs G “MRI-derived quantification of hepatic vessel-to-volume ratios in chronic liver disease using a deep learning approach”. Eur Radiol Exp. 2025 Aug 12,9(1):75
- Strassl A, Lauriero F, Rueda MA, Wassipaul C, Weber M, Loewe C, Beitzke D, Beer L “High-pitch photon-counting detector computed tomography angiography of the coronary arteries: Qualitative and quantitative evaluation of monoenergetic image reconstructions”. Eur J Radiol Open 2025 Jun 13,15:100666
- Mura R, Pochepnia S, Kifjak D, Khenkina N, Prosch H. “A diagnostic approach to mediastinal masses in clinical practice”. BJR Open, 2025 May 8,7(1)
- Straub J, Estrada Lobato E, Paez D, Langs G, Prosch H. “Artificial intelligence in respiratory pandemics-ready for disease X?” A scoping review. Eur Radiol, 2025 Mar, 35(3)
- Schmidbauer M, Kaltenbrunner S „Die Verarbeitung medizinischer Forschungsdaten ohne datenschutzrechtliche Einwilligung: Der Korridor zwischen Anonymisierung und der Forschungsausnahme in Österreich.“ arXiv (2025), https://doi.org/10.48550/arXiv.2509.08841
- Schmidbauer M, Kaltenbrunner S „Transparente KI – Herausforderungen am Beispiel klinischer Entscheidungsunterstützungssysteme.“ ZIIR (2025), 276.
- Schmidbauer M „Revolution im Datenzugang – Eine rollenbezogene Darstellung der Sekundärnutzung elektronischer Gesundheitsdaten im Europäischen Gesundheitsdatenraum (EHDS).“ Master-Thesis (LLM) (2025), 10.25365/thesis.78325.
- Schmidbauer M, Kaltenbrunner S „Datenschutz-Folgenabschätzung und Grundrechte-Folgenabschätzung in der KI-Forschung.“ jusIT (2024), 192.
- Melhorn P, Raderer M, Mazal P, Berchtold L, Beer L Kiesewetter B. Liver metastases in high-grade neuroendocrine neoplasms: A comparative study of hepatic tumor volume and biochemical findings in NET G3 versus NEC . J Neuroendocrinology, 2024 Dec;36(12):e13454.
- Kifjak, D., Hochmair, M., Sobotka, D., Haug, A., Ambros, R., Prayer, F., Heidinger, D., Roehrich, S., Milos, R.-I., Wadsak, W., Fuereder, T., Krenbek, D., Fazekas, A., Meilinger, M., Mayerhoefer, M., Langs, G., Herold, C., Prosch, H., Beer, L. Metabolic tumor volume and sites of organ involvement predict outcome in NSCLC immune-checkpoint inhibitor therapy. European Journal of Radiology, 170 (2024), 111198
- Watzenboeck, Martin L., et al. "Contrast Agent Dynamics Determine Radiomics Profiles in Oncologic Imaging." Cancers 16.8 (2024): 1519.
- Langs, G. "Artificial intelligence in medical imaging is a tool for clinical routine and scientific discovery." Seminars in Arthritis and Rheumatism. Vol. 64. WB Saunders, 2024.
- Langs, G. 2023 Innovations in Deep Learning to Predict Individual Risk and Treatment Outcome. Radiology, 307:5
- Röhrich, S., Hofmanninger, J., Prayer, F., Müller, H., Prosch, H. and Langs, G., 2020. Prospects and Challenges of Radiomics by Using Nononcologic Routine Chest CT. Radiology: Cardiothoracic Imaging, 2(4), p.e190190.