Essen University Hospital

Novel therapies like immunotherapy (ICI) and targeted therapy (TT) have significantly improved survival in metastatic melanoma, but treatment failure and/or development of treatment resistance pose major challenges. Radiomics has been shown to be a valuable tool for imaging-based and/or metadata-based (clinomics), non-invasive data analysis to predict therapy response in oncologic patients. This study aims to evaluate the predictive performance of clinical, radiomics, radio-clinomics, and combined radio-clinomics + body composition analysis (BCA) features for treatment response in metastatic melanoma patients undergoing ICI or TT. A retrospective study was conducted on 120 metastatic melanoma patients receiving ICI or TT. Inclusion criteria comprised a minimum of one RECIST 1.1.-identified lesion in pre- and posttherapeutic CT scans as well as metadata including therapy information and clinical features. Radiomics features and BCA biomarkers were extracted. Lesion- and patient-specific models based on clinical, radiomics, radio-clinomics, and BCA parameters were trained twice: with and without therapy information (type and line). All patient-based models showed improved AUCs after inclusion of therapy information. The clinical model achieved modest AUC of 0.657, when compared to imaging included models with AUCs ranging from 0.92 (radiomics) to 0.97 (radio-clinomics + BCA). The lesion-specific radiomics model showed variable performance spanning from poor AUC of 0.76 in peritoneum/soft-tissue lesions to excellent AUC in liver lesions (AUC 0.98). Our results demonstrate that integrating multi-modal data, particularly therapy information and automated BCA features, substantially improves the accuracy of treatment response prediction, highlighting the potential for improved patient stratification and personalized oncologic care.

Radiomics is increasingly explored as a tool for improving diagnosis, prognosis, and treatment planning. However, concerns exist about the reproducibility and methodological rigor of its studies. The integration of high-dimensional radiomic features and machine learning makes the field prone to unintentional errors that may warrant retraction. Despite a rising number of retractions in science overall, no dedicated study has examined retractions specifically within radiomics. Therefore, this study aimed to review retracted radiomics publications and identify the characteristics and reasons for their retraction. We systematically searched sixdatabases (Crossref, Retraction Watch Database, OpenAlex, PubMed, Scopus, Web of Science) and identified 93 retracted radiomics publications, of which 20 were included. These articles were analyzed with respect to publisher, country of origin, dates, citation counts, and reasons for retraction. Retraction rates were then estimated and compared with those in general radiology. Our findings indicate that a disproportionate number of retractions are linked to specific publishers and countries (particularly China and India), with overall low citation counts (median 4.0 citations). Retractions peaked sharply in 2023, followed by a strong decline. Many retraction notes lack a clear explanation for the retraction. Estimated retraction rates in radiomics were lower than in general radiology (6.7 vs 7.4 per 10,000 publications). Notably, no major radiological or oncological journal appears to have retracted a radiomics publication. Given that radiomics demands higher, interdisciplinary expertise, this suggests a gap, implying that flawed research may yet have to be retracted. KEY POINTS: Question Considering the technical complexity of radiomics studies and their susceptibility to unintentional errors, how do their retraction rates compare to those in general radiology? Findings Retractions in radiomics were disproportionately linked to specific publishers and countries; however, no retractions appeared in major journals. Estimated retraction rates were lower than those for general radiology publications. Clinical relevance A potential gap in the number of retracted radiomics studies was identified, implying that flawed research in the field may not yethave been addressed.

Macrovesicular steatosis (MaS) affects liver transplant outcomes. Traditional visual biopsy assessment is subjective and shows inter-observer variability (weighted κ = 0.595 in our cohort), which complicates allocation and prognostication. We developed a semi-automated image analysis method using HALCON Progress (evaluation license, 2019-2023) to quantify MaS in H&E whole-slide images (WSIs). The pipeline processes native SVS files at full resolution (average runtime 1.26 ± 0.53 min/WSI) without tiling, downsampling, or format conversion. Four shape features (area, roundness, circularity, and compactness) guide classification. Results were compared with three specialized pathologists using Pearson and Spearman correlations. Across 129 WSIs (≈52,000 lipid droplets), artificial intelligence-pathologist correlations were statistically significant (Pearson r = 0.526-0.642; Spearman ρ = 0.498-0.615; all p < 0.001; n = 48-95 per pathologist). Correlation with the mean pathologist assessment reached R2 = 0.64 (r = 0.80, ρ = 0.782), within the inter-pathologist range (R2 = 0.34-0.62; weighted κ = 0.595). Using four shape features, the pipeline separates vacuoles from vessels and processing artifacts during interactive review. Processing native SVS at full resolution avoids extra compute while preserving precision. The method provides a rapid, objective readout of MaS and performs on par with inter-pathologist agreement (R2 = 0.64 vs. weighted κ = 0.595). It is best used as decision-support that supplies percentages and overlays for expert review. Main limitations are underestimation in severe, confluent steatosis (>30%), limited coverage of microvesicular fat, and single-center validation. Multi-center evidence across scanners and patient groups is still needed before routine use.

[68Ga]/[18F] labeled Prostate Specific Membrane Antigen (PSMA) is the radiotracer of choice for imaging localized and metastatic prostate cancer with high sensitivity and specificity. On the other hand, 2-[18F]fluoro-D-glucose (FDG) Positron Emission Tomograpy/Computed Tomography (PET/CT) may help to evaluate the tumor heterogeneity in patients with metastatic castration-resistant prostate cancer (mCRPC) and determine treatment eligibility for Prostate Specific Membrane Antigen (PSMA) targeted radioligand therapy (PSMA-RLT) . The aim of the study is to evaluate the biochemical and clinical parameters which can predict the presence of FDG-PSMA discordant disease. A total of 70 advanced mCRPC patients who underwent [68Ga]Ga-PSMA-11 PET and FDG PET/CT between August 2016 and June 2021 were retrospectively analyzed. Inter-tumoral heterogeneity was both visually and semi-quantitatively evaluated. Baseline clinical, laboratory and PSMA PET/CT related semi-quantitative parameters were analyzed to predict FDG discordant disease with logistic regression analysis. 29/70 (41.4%) of the patients had FDG-PSMA discordant disease. Overall 427 mismatch lesions (FDG+PSMA-) were detected: the majority of these lesions were in the bones (n = 236, 55.2%), lymph nodes (n = 95, 22.2%), and visceral organs (n = 88, 20.6%). Most significant parameters to predict FDG-PSMA discordant disease were liver metastases (HR= 26.5, 95%CI 2.3-302.9, P = 0.008) and serum AST (HR= 1.15, 95%CI 1.04-1.26, P = 0.007). The presence of liver metastases and elevated AST may be easily used in clinical practice to predict FDG-PSMA discordant disease.

Transactive response DNA binding protein 43 kDa (TDP-43) pathology, is a central molecular hallmark of amyotrophic lateral sclerosis (ALS). However, the underlying triggers are incompletely understood. Here, we show that infection with herpes simplex virus (HSV) induces molecular hallmarks of ALS in various in vitro and in vivo models and is associated with an increased risk of ALS in human population data. German healthcare provider data (n = 238,440) and herpesvirus serology of an ALS patient and control cohort (n = 1,100) showed that HSV infection elevated the ALS risk by 210% and odds by ~65%, respectively. On a molecular level, HSV infection promoted TDP-43 pathology in neuronal cell models, human iPSC-derived motoneurons and cerebral organoids, mice, and human tissue sections. This effect was triggered by HSV-1 or 2, but not by several other related herpesviruses. Mechanistically, the infected cell protein 0 (ICP0) of HSV-1/2 drives TDP-43 pathology by disturbance of promyelocytic leukemia nuclear bodies (PML-NBs), thereby abrogating TDP-43 SUMO2/3ylation. Taken together, we reveal a previously unrecognized association between HSV infection and ALS and clarify the underlying molecular mechanism that drives TDP-43 pathology. Our data may guide future studies into therapeutic and prophylactic interventions against ALS.

Abstract. Computational models play an increasingly vital role in scientific research by enabling the numerical simulation of complex processes. Such models are also fundamental in geosciences. For instance, they offer critical insights into the impacts of global change on the Earth system today and in the future. Beyond their value as research tools, models are also software products and should therefore adhere to certain established software engineering standards. However, scientists are rarely trained as software developers, which can lead to potential deficiencies in software quality like unreadable, inefficient, or erroneous code. The complexity of models, coupled with their integration into broader workflows, also often makes it challenging to reproduce results, evaluate processes, and build upon them. In this paper, we review the state and current practices of the development processes of the state-of-the-art land surface models used by the Global Carbon Budget. We combine the experience of modelers from the respective research groups with the expertise of software engineers from tech companies to outline key principles and tools for improving software quality in research. We explore four main areas: (1) model testing and validation, (2) scientific, technical, and user documentation, (3) version control, continuous integration, and code review, and (4) the portability and reproducibility of workflows. Our review reveals that while modeling communities are incorporating many best practices, significant room for improvement remains in areas such as automated testing, automated documentation, and reproducibility. Therefore, we here identify and promote essential software engineering practices, including numerous examples of practices from within the community that can serve as guidelines for other models and could help streamline processes across the entire community. We conclude with an open-source example implementation of these principles, demonstrating portable and reproducible data flows, a continuous integration setup, and web-based visualizations. This example may serve as a practical resource for model developers, users, and all scientists engaged in scientific programming.

Hereditary Hemorrhagic Telangiectasia (HHT) is a rare vascular disorder with mucocutaneous telangiectasia. Hormones are hypothesized to play a role in reducing bleeding in these patients; however, clear evidence is currently lacking. The aim of this study was to further investigate this hypothesis by assessing the impact of altered hormonal status on HHT severity. This cross-sectional electronic questionnaire, available in English and German, was distributed with the support of various self-help groups. It included sections on the general medical history of HHT and the influence of hormones on disease symptoms. Data were collected between March 2016 and August 2017. Of the 477 respondents with HHT, 326 were female (68%). With increasing age, female patients experienced more severe epistaxis compared to male patients, with a mean age of 54 years (standard deviation ± 11.5 years, range [28,83]). Of the 228 female patients who reported having taken hormones, 73% used them for contraception and 57% for hyper- or dysmenorrhea. Regarding hormone intake, 52% of the patients taking combined estrogen and progestin therapy experienced less epistaxis. Most female patients stated an increase in epistaxis during puberty (41%) and pregnancy (39%), predominantly in the second trimester. In HHT, variation in levels of hormone concentrations seem to contribute to symptom severity. Off-label use of hormonal products in women with HHT may be considered. Clinicaltrials.gov: NCT02690246, Registration Date: 2016–02-09.