TY - JOUR A1 - Werle, Silke D. A1 - Ikonomi, Nensi A1 - Lausser, Ludwig A1 - Kestler, Annika M. T. U. A1 - Weidner, Felix M. A1 - Schwab, Julian D. A1 - Maier, Julia A1 - Buchholz, Malte A1 - Gress, Thomas M. A1 - Kestler, Angelika M. R. A1 - Kestler, Hans A. T1 - A systems biology approach to define mechanisms, phenotypes, and drivers in PanNETs with a personalized perspective JF - npj Systems Biology and Applications N2 - AbstractPancreatic neuroendocrine tumors (PanNETs) are a rare tumor entity with largely unpredictable progression and increasing incidence in developed countries. Molecular pathways involved in PanNETs development are still not elucidated, and specific biomarkers are missing. Moreover, the heterogeneity of PanNETs makes their treatment challenging and most approved targeted therapeutic options for PanNETs lack objective responses. Here, we applied a systems biology approach integrating dynamic modeling strategies, foreign classifier tailored approaches, and patient expression profiles to predict PanNETs progression as well as resistance mechanisms to clinically approved treatments such as the mammalian target of rapamycin complex 1 (mTORC1) inhibitors. We set up a model able to represent frequently reported PanNETs drivers in patient cohorts, such as Menin-1 (MEN1), Death domain associated protein (DAXX), Tuberous Sclerosis (TSC), as well as wild-type tumors. Model-based simulations suggested drivers of cancer progression as both first and second hits after MEN1 loss. In addition, we could predict the benefit of mTORC1 inhibitors on differentially mutated cohorts and hypothesize resistance mechanisms. Our approach sheds light on a more personalized prediction and treatment of PanNET mutant phenotypes. UR - https://doi.org/10.1038/s41540-023-00283-8 Y1 - 2023 UR - https://doi.org/10.1038/s41540-023-00283-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-38071 SN - 2056-7189 VL - 9 IS - 1 PB - Springer CY - Heidelberg ER - TY - CHAP A1 - Krüger, Max A1 - Lausser, Ludwig T1 - Identifying Key Features in Aggregated Real-Life AIS-Messages for Classification of Maritime Vessels by Random Forests T2 - 2023 26th International Conference on Information Fusion (FUSION) UR - https://doi.org/10.23919/FUSION52260.2023.10224109 KW - Classification KW - key features KW - AIS KW - aggregated messages KW - performance evaluation KW - experimental comparison KW - maritime surveillance systems Y1 - 2023 UR - https://doi.org/10.23919/FUSION52260.2023.10224109 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Thiam, Patrick A1 - Lausser, Ludwig A1 - Kloth, Christopher A1 - Blaich, Daniel A1 - Liebold, Andreas A1 - Beer, Meinrad A1 - Kestler, Hans A. T1 - Unsupervised domain adaptation for the detection of cardiomegaly in cross-domain chest X-ray images JF - Frontiers in Artificial Intelligence N2 - In recent years, several deep learning approaches have been successfully applied in the field of medical image analysis. More specifically, different deep neural network architectures have been proposed and assessed for the detection of various pathologies based on chest X-ray images. While the performed assessments have shown very promising results, most of them consist in training and evaluating the performance of the proposed approaches on a single data set. However, the generalization of such models is quite limited in a cross-domain setting, since a significant performance degradation can be observed when these models are evaluated on data sets stemming from different medical centers or recorded under different protocols. The performance degradation is mostly caused by the domain shift between the training set and the evaluation set. To alleviate this problem, different unsupervised domain adaptation approaches are proposed and evaluated in the current work, for the detection of cardiomegaly based on chest X-ray images, in a cross-domain setting. The proposed approaches generate domain invariant feature representations by adapting the parameters of a model optimized on a large set of labeled samples, to a set of unlabeled images stemming from a different data set. The performed evaluation points to the effectiveness of the proposed approaches, since the adapted models outperform optimized models which are directly applied to the evaluation sets without any form of domain adaptation. UR - https://doi.org/10.3389/frai.2023.1056422 KW - chest X-ray KW - cardiomegaly KW - deep learning KW - transfer learning KW - unsupervised domain adaptation Y1 - 2023 UR - https://doi.org/10.3389/frai.2023.1056422 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-32108 SN - 2624-8212 VL - 6 PB - Frontiers Media CY - Lausanne ER - TY - CHAP A1 - Lausser, Ludwig A1 - Bertram, Christof A1 - Klopfleisch, Robert A1 - Aubreville, Marc ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Limits of Human Expert Ensembles in Mitosis Multi-expert Ground Truth Generation T2 - Bildverarbeitung für die Medizin 2023: Proceedings, German Workshop on Medical Image Computing, Braunschweig, July 2-4, 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 Y1 - 2023 UR - https://doi.org/10.1007/978-3-658-41657-7_27 SN - 978-3-658-41657-7 SN - 978-3-658-41656-0 SP - 116 EP - 121 PB - Springer Vieweg CY - Wiesbaden ER -