Dirk Hellwig

Patients with malignant lymphomas are at increased risk for severe COVID-19. In the general population, outcomes of COVID-19 have improved dramatically over time due to increasing immunity through vaccination or infection and due to improved clinical care. It is unclear whether outcomes of COVID-19 in patients with lymphoma have improved similarly over time. We performed a multi-center retrospective study of lymphoma patients with COVID-19 at five tertiary care centers of the Bavarian Cancer Research Center (BZKF) in Germany, evaluating baseline characteristics as well as COVID-19 management and outcomes across different pandemic waves from March 2020 to March 2022. Clinical management of COVID-19 as well as vaccination rates changed significantly over time. In the most recent pandemic wave (cases after December 27th 2021), 88% of lymphoma patients had been vaccinated. Upon infection, 57% of patients were treated with monoclonal antibodies and 35% were treated with antiviral agents. Concurrent with these interventions, we observed markedly improved outcomes. Severe disease courses were significantly less frequent in the most recent pandemic wave compared to earlier pandemic waves (4/40 vs. 26/73, p = 0.03). Similarly, mortality was significantly lower in the most recent pandemic wave compared to earlier pandemic waves (2/40 vs. 14/73, p = 0.039). Although we cannot clearly distinguish the relative contribution of each intervention on the observed improvement in COVID-19 outcome, our data indicates that severity and mortality of COVID-19 in this vulnerable patient population has recently declined. Thus, our data largely supports a return to pre-pandemic treatment recommendations and protocols.

Ensuring the reliability and impact of data-driven research requires high-quality data. The more complex the source system and data processing pipelines are, the higher the risk of poor data quality (DQ) during analysis, which in turn compromises the research outcomes. Even for well-structured and standardized data, such as the oncological standardized dataset (oBDS), medical research projects must assess DQ to ensure the reliability of their results. This study assesses the quality of the oncological standardized dataset mapped to FHIR resources across six German university hospitals. Automated DQ checks using Pathling and Great Expectations were implemented on the FHIR resources to evaluate key dimensions of DQ. It demonstrates that DQ varies across data sources, indicating that high DQ cannot be assumed to be uniform even if the dataset is standardized. The results of this assessment will be integrated into the Real World Data Platform of the Bavarian Cancer Research Center (BZKF), enhancing its ability to provide researchers with high-quality oncological data for scientific studies.

Die Nuklearmedizin hat eine Persönlichkeit verloren, welche über Jahrzehnte bedeutsame Fortschritte unseres Faches von der Grundlagenentwicklung bis in die praktische Verfügbarkeit für die klinische Anwendung gebracht hat. Prof. Dr. Carl-Martin Kirsch, ehemaliger Lehrstuhlinhaber an der Universität des Saarlandes, verstarb am 25. Januar 2025 nach langer Krankheit im Alter von 75 Jahren. Article published online: 28 May 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany

Background: Real-world data (RWD) from sources like administrative claims, electronic health records, and cancer registries offer insights into patient populations beyond the tightly regulated environment of randomized controlled trials. To leverage this and to advance cancer research, 6 university hospitals in Bavaria have established a joint research IT infrastructure. Objective: This study aimed to outline the design, implementation, and deployment of a modular data transformation pipeline that transforms oncological RWD into a Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) format and then into a tabular format in preparation for a federated analysis (FA) across the 6 Bavarian Cancer Research Center university hospitals. Methods: To harness RWD effectively, we designed a pipeline to convert the oncological basic dataset (oBDS) into HL7 FHIR format and prepare it for FA. The pipeline handles diverse IT infrastructures and systems while maintaining privacy by keeping data decentralized for analysis. To assess the functionality and validity of our implementation, we defined a cohort to address two specific medical research questions. We evaluated our findings by comparing the results of the FA with reports from the Bavarian Cancer Registry and the original data from local tumor documentation systems. Results: We conducted an FA of 17,885 cancer cases from 2021/2022. Breast cancer was the most common diagnosis at 3 sites, prostate cancer ranked in the top 2 at 4 sites, and malignant melanoma was notably prevalent. Gender-specific trends showed larynx and esophagus cancers were more common in males, while breast and thyroid cancers were more frequent in females. Discrepancies between the Bavarian Cancer Registry and our data, such as higher rates of malignant melanoma (3400/63,771, 5.3% vs 1921/17,885, 10.7%) and lower representation of colorectal cancers (8100/63,771, 12.7% vs 1187/17,885, 6.6%) likely result from differences in the time periods analyzed (2019 vs 2021/2022) and the scope of data sources used. The Bavarian Cancer Registry reports approximately 3 times more cancer cases than the 6 university hospitals alone. Conclusions: The modular pipeline successfully transformed oncological RWD across 6 hospitals, and the federated approach preserved privacy while enabling comprehensive analysis. Future work will add support for recent oBDS versions, automate data quality checks, and integrate additional clinical data. Our findings highlight the potential of federated health data networks and lay the groundwork for future research that can leverage high-quality RWD, aiming to contribute valuable knowledge to the field of cancer research.

Background: While MRI is the primary diagnostic tool for the diagnosis of spondylodiscitis, the role of [¹⁸F]-fluorodeoxyglucose ([¹⁸F]FDG) PET/CT is gaining prominence. This study aimed to determine the frequency of [¹⁸F]FDG PET/CT usage and its impact on the in-hospital mortality rate in patients with spondylodiscitis, particularly in the geriatric population. Methods: We conducted a Germany-wide cross-sectional study from 2019 to 2021 using an open-access, Germany-wide database, analyzing cases with ICD-10 codes M46.2-, M46.3-, and M46.4- (‘Osteomyelitis of vertebrae’, ‘Infection of intervertebral disc (pyogenic)’, and ‘Discitis unspecified’). Diagnostic modalities were compared for their association with in-hospital mortality, with a focus on [¹⁸F]FDG PET/CT. Results: In total, 29,362 hospital admissions from 2019 to 2021 were analyzed. Of these, 60.1% were male and 39.9% were female, and 71.8% of the patients were aged 65 years and above. The overall in-hospital mortality rate was 6.5% for the entire cohort and 8.2% for the geriatric subgroup (p < 0.001). Contrast-enhanced (ce) MRI (48.1%) and native CT (39.4%) of the spine were the most frequently conducted diagnostic modalities. [¹⁸F]FDG PET/CT was performed in 2.7% of cases. CeCT was associated with increased in-hospital mortality (OR = 2.03, 95% CI: 1.90–2.17, p < 0.001). Cases with documented [¹⁸F]FDG PET/CT showed a lower frequency of in-hospital deaths (OR = 0.58, 95% CI: 0.18–0.50; p = 0.002). This finding was more pronounced in patients aged 65 and above (OR = 0.42, 95% CI: 0.27–0.65, p = 0.001). Conclusions: Despite its infrequent use, [¹⁸F]FDG PET/CT was associated with a lower in-hospital mortality rate in patients with spondylodiscitis, particularly in the geriatric cohort. This study is limited by only considering data on hospitalized patients and relying on the assumption of error-free coding. Further research is needed to optimize diagnostic approaches for spondylodiscitis.

Precision medicine has a substantial basis in nuclear medicine and found its way into diagnosis and treatment of major and rare diseases. Apart from their scientific basis, further aspects are important for new medical procedures to be introduced into broad clinical care. In this matter, we in Germany have special experiences with PET which is still not fully recognized by statutory health insurances as the standard procedure of choice for many important indications despite its recommendation in international guidelines. This chapter describes the long way of PET and PET/CT into German guidelines and reimbursement for the example of lung cancer, to which the jubilarian made crucial contributions.

Positron emission tomography (PET) is vital for diagnosing diseases and monitoring treatments. Conventional image reconstruction (IR) techniques like filtered backprojection and iterative algorithms are powerful but face limitations. PET IR can be seen as an image-to-image translation. Artificial intelligence (AI) and deep learning (DL) using multilayer neural networks enable a new approach to this computer vision task. This review aims to provide mutual understanding for nuclear medicine professionals and AI researchers. We outline fundamentals of PET imaging as well as state-of-the-art in AI-based PET IR with its typical algorithms and DL architectures. Advances improve resolution and contrast recovery, reduce noise, and remove artifacts via inferred attenuation and scatter correction, sinogram inpainting, denoising, and super-resolution refinement. Kernel-priors support list-mode reconstruction, motion correction, and parametric imaging. Hybrid approaches combine AI with conventional IR. Challenges of AI-assisted PET IR include availability of training data, cross-scanner compatibility, and the risk of hallucinated lesions. The need for rigorous evaluations, including quantitative phantom validation and visual comparison of diagnostic accuracy against conventional IR, is highlighted along with regulatory issues. First approved AI-based applications are clinically available, and its impact is foreseeable. Emerging trends, such as the integration of multimodal imaging and the use of data from previous imaging visits, highlight future potentials. Continued collaborative research promises significant improvements in image quality, quantitative accuracy, and diagnostic performance, ultimately leading to the integration of AI-based IR into routine PET imaging protocols.