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Abstract Introduction Current treatment guidelines for patients with epidermal growth factor receptor (EGFR)-mutated metastatic non-small cell lung cancer (mNSCLC) recommend EGFR tyrosine kinase inhibitors (TKIs) as the standard of care for first-line treatment, with third-generation osimertinib the preferred choice. However, most patients develop resistance to targeted therapy, and subsequent systemic chemotherapy is recommended. The aim of this study was to characterize the subsequent line of therapy (LOT) following osimertinib in patients with EGFR-mNSCLC. Methods Medical and pharmacy claims of adults who initiated a subsequent LOT (index) after initial osimertinib discontinuation between November 2015 and September 2019 were analyzed retrospectively. Results A total of 135 patients met the inclusion criteria. After metastatic diagnosis, 22.2% and 49.6% of patients were treated with osimertinib in the first and second line, respectively. After osimertinib discontinuation, most patients were treated with a platinum-based chemotherapy regimen (57%), of which 40.3% included immuno-oncology therapy. Reuse or continuation of EGFR TKIs was also common (24%). Overall, the median time to treatment discontinuation for the index LOT was 2.4 months. Proportions of patients with ≥ 1 inpatient or emergency department visit were 31.9% and 35.6%, respectively. Conclusions The duration of the LOT following osimertinib was short and associated with tolerability issues underscoring a high unmet need for new therapies to address EGFR TKI resistance.

Abstract Efficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We study scalable machine learning models for event reconstruction in electron-positron collisions based on a full detector simulation. Particle-flow reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters. We compare a graph neural network and kernel-based transformer and demonstrate that we can avoid quadratic operations while achieving realistic reconstruction. We show that hyperparameter tuning significantly improves the performance of the models. The best graph neural network model shows improvement in the jet transverse momentum resolution by up to 50% compared to the rule-based algorithm. The resulting model is portable across Nvidia, AMD and Habana hardware. Accurate and fast machine-learning based reconstruction can significantly improve future measurements at colliders.

Abstract BackgroundPseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring the direct identification of data to support various research processes while still allowing for advanced processing activities, such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units known as trusted third parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating adequate tool support. Common tasks of TTPs include supporting the secure registration and pseudonymization of patient and sample identities as well as managing consent. ObjectiveDespite the challenges involved, little has been published about successful architectures and functional tools for implementing TTPs in large university hospitals. The aim of this paper is to fill this research gap by describing the software architecture and tool set developed and deployed as part of a TTP established at Charité – Universitätsmedizin Berlin. MethodsThe infrastructure for the TTP was designed to provide a modular structure while keeping maintenance requirements low. Basic functionalities were realized with the free MOSAIC tools. However, supporting common study processes requires implementing workflows that span different basic services, such as patient registration, followed by pseudonym generation and concluded by consent collection. To achieve this, an integration layer was developed to provide a unified Representational state transfer (REST) application programming interface (API) as a basis for more complex workflows. Based on this API, a unified graphical user interface was also implemented, providing an integrated view of information objects and workflows supported by the TTP. The API was implemented using Java and Spring Boot, while the graphical user interface was implemented in PHP and Laravel. Both services use a shared Keycloak instance as a unified management system for roles and rights. ResultsBy the end of 2022, the TTP has already supported more than 10 research projects since its launch in December 2019. Within these projects, more than 3000 identities were stored, more than 30,000 pseudonyms were generated, and more than 1500 consent forms were submitted. In total, more than 150 people regularly work with the software platform. By implementing the integration layer and the unified user interface, together with comprehensive roles and rights management, the effort for operating the TTP could be significantly reduced, as personnel of the supported research projects can use many functionalities independently. ConclusionsWith the architecture and components described, we created a user-friendly and compliant environment for supporting research projects. We believe that the insights into the design and implementation of our TTP can help other institutions to efficiently and effectively set up corresponding structures.