@article{SeufferleinLausserSteinetal.2024, author = {Seufferlein, Thomas and Lausser, Ludwig and Stein, Alexander and Arnold, Dirk and Prager, Gerald and Kasper-Virchow, Stefan and Niedermeier, Michael and M{\"u}ller, Lothar and Kubicka, Stefan and K{\"o}nig, Alexander and B{\"u}chner-Steudel, Petra and Wille, Kai and Berger, Andreas W. and Kestler, Angelika M. R. and Kraus, Johann M. and Werle, Silke D. and Perkhofer, Lukas and Ettrich, Thomas J. and Kestler, Hans A.}, title = {Prediction of resistance to bevacizumab plus FOLFOX in metastatic colorectal cancer—Results of the prospective multicenter PERMAD trial}, volume = {19}, pages = {e0304324}, journal = {PLOS ONE}, number = {6}, publisher = {PLOS}, address = {San Francisco}, issn = {1932-6203}, doi = {https://doi.org/10.1371/journal.pone.0304324}, year = {2024}, abstract = {Background Anti-vascular endothelial growth factor (VEGF) monoclonal antibodies (mAbs) are widely used for tumor treatment, including metastatic colorectal cancer (mCRC). So far, there are no biomarkers that reliably predict resistance to anti-VEGF mAbs like bevacizumab. A biomarker-guided strategy for early and accurate assessment of resistance could avoid the use of non-effective treatment and improve patient outcomes. We hypothesized that repeated analysis of multiple cytokines and angiogenic growth factors (CAFs) before and during treatment using machine learning could provide an accurate and earlier, i.e., 100 days before conventional radiologic staging, prediction of resistance to first-line mCRC treatment with FOLFOX plus bevacizumab. Patients and methods 15 German and Austrian centers prospectively recruited 50 mCRC patients receiving FOLFOX plus bevacizumab as first-line treatment. Plasma samples were collected every two weeks until radiologic progression (RECIST 1.1) as determined by CT scans performed every 2 months. 102 pre-selected CAFs were centrally analyzed using a cytokine multiplex assay (Luminex, Myriad RBM). Results Using random forests, we developed a predictive machine learning model that discriminated between the situations of "no progress within 100 days before radiological progress" and "progress within 100 days before radiological progress". We could further identify a combination of ten out of the 102 CAF markers, which fulfilled this task with 78.2\% accuracy, 71.8\% sensitivity, and 82.5\% specificity. Conclusions We identified a CAF marker combination that indicates treatment resistance to FOLFOX plus bevacizumab in patients with mCRC within 100 days prior to radiologic progress.}, language = {en} } @inproceedings{PaulaBauderKoenigetal.2022, author = {Paula, Daniel and Bauder, Maximilian and K{\"o}nig, Thomas and Dengler, Yannick and B{\"o}hm, Klaus and Kubjatko, Tibor and Schweiger, Hans-Georg}, title = {Impact of vehicle electrification on fundamental accident reconstruction parameters}, booktitle = {Proceedings of the 30th Annual Congress of the EVU}, publisher = {EVU}, address = {Berlin}, pages = {34 -- 41}, year = {2022}, language = {en} } @inproceedings{PaulaBauderKoenigetal.2023, author = {Paula, Daniel and Bauder, Maximilian and K{\"o}nig, Thomas and Kubjatko, Tibor and Schweiger, Hans-Georg}, title = {Systematic investigation of influences on advanced driver assistance systems for accident reconstruction}, booktitle = {Proceedings of the 31st Annual Congress of the EVU}, publisher = {EVU}, address = {Berlin}, pages = {253 -- 261}, year = {2023}, language = {en} } @article{KoenigPaulaQuaschneretal.2024, author = {K{\"o}nig, Thomas and Paula, Daniel and Quaschner, Stefan and Schweiger, Hans-Georg}, title = {Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction}, volume = {24}, pages = {1517}, journal = {Sensors}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {1424-8220}, doi = {https://doi.org/10.3390/s24051517}, year = {2024}, abstract = {Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRUs) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU, as well as obstacles between them, along with the speed difference can have great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal, the accident can be avoided or not by the driver when reacting to this signal.}, language = {en} } @unpublished{KoenigPaulaQuaschneretal.2023, author = {K{\"o}nig, Thomas and Paula, Daniel and Quaschner, Stefan and Schweiger, Hans-Georg}, title = {Influence of Blind Spot Assistance Systems in Heavy Commercial Vehicles on Accident Reconstruction}, publisher = {Preprints}, address = {Basel}, doi = {https://doi.org/10.20944/preprints202311.1308.v1}, year = {2023}, abstract = {Accidents between right-turning commercial vehicles and crossing vulnerable road users (VRU) in urban environments often lead to serious or fatal injuries and therefore play a significant role in forensic accident analysis. To reduce the risk of accidents, blind spot assistance systems have been installed in commercial vehicles for several years, among other things, to detect VRUs and warn the driver in time. However, since such systems cannot reliably prevent all turning accidents, an investigation by experts must clarify how the accident occurred and to what extent the blind spot assistance system influenced the course of the accident. The occurrence of the acoustic warning message can be defined as an objective reaction prompt for the driver, so that the blind spot assistance system can significantly influence the avoidability assessment. In order to be able to integrate the system into forensic accident analysis, a precise knowledge of how the system works and its limitations is required. For this purpose, tests with different systems and accident constellations were conducted and evaluated. It was found that the type of sensor used for the assistance systems has a great influence on the system's performance. The lateral distance between the right side of the commercial vehicle and the VRU as well as obstacles between them and the speed difference can take great influence on the reliability of the assistance system. Depending on the concrete time of the system's warning signal the accident can be avoided or not by the driver when reacting on this signal.}, language = {en} } @inproceedings{PaulaKoenigBauderetal.2022, author = {Paula, Daniel and K{\"o}nig, Thomas and Bauder, Maximilian and Petermeier, Franziska and Kubjatko, Tibor and Schweiger, Hans-Georg}, title = {Performance Tests of the Tesla Autopilot and VW Travel Assist on a Rural Road}, booktitle = {Transport Means 2022: Proceedings of the 26th International Scientific Conference - Part II}, publisher = {Kaunas University of Technology}, address = {Kaunas}, issn = {2351-7034}, doi = {https://doi.org/10.5755/e01.2351-7034.2022.P2}, pages = {498 -- 508}, year = {2022}, language = {en} } @article{PaulaBauderKoenigetal.2022, author = {Paula, Daniel and Bauder, Maximilian and K{\"o}nig, Thomas and B{\"o}hm, Klaus and Kubjatko, Tibor and Schweiger, Hans-Georg}, title = {Fahrerassistenzsysteme - Herausforderungen \& Chancen f{\"u}r die forensische Unfallanalyse}, volume = {2022}, journal = {Zeitschrift f{\"u}r Verkehrssicherheit}, number = {4}, publisher = {Kirschbaum Verlag GmbH}, address = {Bonn}, issn = {0044-3654}, url = {https://www.kirschbaum.de/fachzeitschriften/zeitschrift-fuer-verkehrssicherheit/zvs/aktuelles-heft-6.html\#c11330}, year = {2022}, language = {de} }