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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.
Die Unternehmensberatung OSCAR GmbH und der Autor dieses Beitrags führen im Rahmen einer Kooperationsveranstaltung regelmäßig gemeinsam ein Case Study-Training für ein Assessment Center mit Studierenden der Betriebswirtschaft durch. Die Veranstaltung findet in einem rein digitalen Format live via Zoom statt und wird anschließend um ein Peer Review-Verfahren via Moodle ergänzt, bei dem die Studierenden ihre Gruppenlösungen zur Case Study gegenseitig beurteilen. Nach einem Überblick über das Gesamtkonzept skizziert der vorliegende Beitrag die Vorbereitung in Moodle, die Umsetzung in der Haupt-Session und den Breakout Sessions in Zoom und schließlich die Überführung der Gruppen mit ihren Arbeitsergebnissen von Zoom nach Moodle, um hier die gegenseitige Beurteilung zu ermöglichen. Der Beitrag wendet sich nicht nur an Interessierte im Themenfeld Assessment Center, sondern auch generell an Lehrende, die an einem kombinierten Einsatz von Zoom und Moodle im Kontext von Gruppenarbeit und Feedback interessiert sind.
Digitalisierung – Motor für innovative Geschäftsmodelle im Umfeld des hochautomatisierten Fahrens
(2024)
Digital Trust Management
(2024)
Cooperative intelligent transport systems (C-ITSs) are mass-produced and sold in Europe, promising enhanced safety and comfort. Direct vehicle communication, known as vehicle-to-everything (V2X) communication, is crucial in this context. Drivers receive warnings about potential hazards by exchanging vehicle status and environmental data with other communication-enabled vehicles. However, the impact of these warnings on drivers and their inclusion in accident reconstruction remains uncertain. Unlike sensor-based warnings, V2X warnings may not provide a visible reason for the alert, potentially affecting reaction times and behavior. In this work, a simulator study on V2X warnings was conducted with 32 participants to generate findings on reaction times and behavior for accident reconstruction in connection with these systems. Two scenarios from the Car-2-Car Communication Consortium were implemented: “Stationary Vehicle Warning—Broken-Down Vehicle” and “Dangerous Situation—Electronic Emergency Brake Lights”. Volkswagen’s warning concept was utilized, as they are the sole provider of cooperative vehicles in Europe. Results show that V2X warnings without visible reasons did not negatively impact reaction times or behavior, with average reaction times between 0.58 s (steering) and 0.69 s (braking). No significant distraction or search for warning reasons was observed. However, additional information in the warnings caused confusion and was seldom noticed by subjects. In this study, participants responded correctly and appropriately to the shown false-positive warnings. A wrong reaction triggering an accident is possible but unlikely. Overall, V2X warnings showed no negative impacts compared with sensor-based systems. This means that there are no differences in accident reconstruction regarding the source of the warning (sensors or communication). However, it is important that it is known that there was a warning, which is why the occurrence of V2X warnings should also be saved in the EDR in the future.