Assessment of a fully-automated diagnostic AI software in prostate MRI: Clinical evaluation and histopathological correlation - Bayerl, Nadine; Adams, Lisa C.; Cavallaro, Alexander Josef; Bäuerle, Tobias; Schlicht, Michael; Wullich, Bernd; Hartmann, Arndt; Uder, Michael; Ellmann, Stephan (European Journal of Radiology, 2024)
Objective: This study aims to evaluate the diagnostic performance of a commercial, fully-automated, artificial intelligence (AI) driven software tool in identifying and grading prostate lesions in prostate MRI, using histopathological findings as the reference standard, while contextualizing its performance within the framework of PI-RADS v2.1 criteria. Material and methods: This retrospective study analyzed 123 patients who underwent multiparametric prostate MRI followed by systematic and targeted biopsies. MRI protocols adhered to international guidelines and included T2-weighted, diffusion-weighted, T1-weighted, and dynamic contrast-enhanced imaging. The AI software tool mdprostate was integrated into the Picture Archiving and Communication System to automatically segment the prostate, calculate prostate volume, and classify lesions according to PI-RADS scores using biparametric T2-weighted and diffusion-weighted imaging. Histopathological analysis of biopsy cores served as the reference standard. Diagnostic performance metrics including sensitivity, specificity, positive and negative predictive value (PPV, NPV), and area under the ROC curve (AUC) were calculated. Results: mdprostate demonstrated 100 % sensitivity at a PI-RADS ≥ 2 cutoff, effectively ruling out both clinically significant and non-significant prostate cancers for lesions remaining below this threshold. For detecting clinically significant prostate cancer (csPCa) using a PI-RADS ≥ 4 cutoff, mdprostate achieved a sensitivity of 85.5 % and a specificity of 63.2 %. The AUC for detecting cancers of any grade was 0.803. The performance metrics of mdprostate were comparable to those reported in two meta-analyses of PI-RADS v2.1, with no significant differences in sensitivity and specificity (p > 0.05). Conclusion: The evaluated AI tool demonstrated high diagnostic performance in identifying and grading prostate lesions, with results comparable to those reported in meta-analyses of expert readers using PI-RADS v2.1. Its ability to standardize evaluations and potentially reduce variability underscores its potential as a valuable adjunct in the prostate cancer diagnostic pathway. The high accuracy of mdprostate, particularly in ruling out prostate cancers, highlights its clinical utility by reducing workload and enhancing patient outcomes.
CRIS-Team Scopus Importer:2024-11-15
Keywords: Artificial intelligence; Clinical evaluation; Diagnostic performance; PI-RADS; Prostate cancer; Prostate MRI

Autonomous medical evaluation for guideline adherence of large language models - Fast, Dennis; Adams, Lisa C.; Busch, Felix; Fallon, Conor; Huppertz, Marc; Siepmann, Robert; Prucker, Philipp; Bayerl, Nadine; Truhn, Daniel; Makowski, Marcus; Löser, Alexander; Bressem, Keno K. (npj Digital Medicine, 2024)
Autonomous Medical Evaluation for Guideline Adherence (AMEGA) is a comprehensive benchmark designed to evaluate large language models’ adherence to medical guidelines across 20 diagnostic scenarios spanning 13 specialties. It includes an evaluation framework and methodology to assess models’ capabilities in medical reasoning, differential diagnosis, treatment planning, and guideline adherence, using open-ended questions that mirror real-world clinical interactions. It includes 135 questions and 1337 weighted scoring elements designed to assess comprehensive medical knowledge. In tests of 17 LLMs, GPT-4 scored highest with 41.9/50, followed closely by Llama-3 70B and WizardLM-2-8x22B. For comparison, a recent medical graduate scored 25.8/50. The benchmark introduces novel content to avoid the issue of LLMs memorizing existing medical data. AMEGA’s publicly available code supports further research in AI-assisted clinical decision-making, aiming to enhance patient care by aiding clinicians in diagnosis and treatment under time constraints.
CRIS-Team Scopus Importer:2025-02-04

CD19-targeting CAR T-cell therapy in patients with diffuse systemic sclerosis: a case series - Auth, Janina; Müller, Fabian; Völkl, Simon; Bayerl, Nadine; Distler, Jörg H.W.; Tur, Carlo; Raimondo, Maria Gabriella; Chenguiti Fakhouri, Sara; Atzinger, Armin; Coppers, Birte; Eckstein, Markus; Liphardt, Anna-Maria; et al. (The Lancet Rheumatology, 2024)
Background: CD19-targeting chimeric antigen receptor (CAR) T-cell therapy has shown remarkable outcomes in patients with systemic lupus erythematosus. The effects of CD19-targeting CAR T cells on organ manifestations in patients with systemic sclerosis have yet to be characterised. B cells have a central role in the pathogenesis of systemic sclerosis. We present a detailed analysis of the effects of CD19-targeting CAR T-cell therapy in patients with systemic sclerosis. Methods: Six patients with severe diffuse systemic sclerosis with an insufficient response to at least two treatments were consecutively recruited at the Department of Internal Medicine 3, University Hospital Erlangen (Erlangen, Germany) to receive CD19-targeting CAR T-cell treatment (1 × 106 CAR T cells per kg bodyweight). Events were predefined by progression of interstitial lung disease, onset of congestive heart failure, onset of renal failure, onset of arterial hypertension, or initiation of new immunosuppressive or antifibrotic therapy. Event-free time or treatment intensification after study entry was the primary outcome. Key secondary outcomes included changes in the modified Rodnan Skin Score (mRSS), imaging (a component of the assessment of lung fibrosis), laboratory assessments, patient-reported outcomes, and a modified version of the American College of Rheumatology Composite Response Index in Systemic Sclerosis (ACR-CRISS), assessed at baseline, 3 months, 6 months, 9 months, and 12 months. Findings: Between April 20, 2022, and Nov 8, 2023, six patients with severe diffuse systemic sclerosis (median age 42 years [IQR 36–53]; four men and two women; all White European) were recruited and received CD19-targeted CAR T-cell therapy. The median follow-up time was 487 days (IQR 342–585). No events occurred within the observational period. Probability of improvement in the ACR-CRISS score increased to a median of 100% (IQR 100–100) at 6 months. Median mRSS decreased by 31% (IQR 29–38), corresponding to a median of 8 points (IQR 7–13) within 100 days. The extent of disease on CT scan decreased by a median of 4% (IQR 3–4) due to reduction of ground-glass opacities while the reticular pattern remained stable. Forced vital capacity improved by a median of 195 mL (IQR 18–275) at the latest observational timepoint. Interpretation: We provide the first evidence that CD19-targeting CAR T-cell therapy might intercept with the progression of fibrotic organ manifestations in patients with systemic sclerosis. Funding: Deutsche Forschungsgemeinschaft, Deutsche Krebshilfe, ELAN-Foundation Erlangen, IZKF Erlangen, and Bundesministerium für Bildung und Forschung.
CRIS-Team Scopus Importer:2024-12-13

Einsatz bildgebender Verfahren bei der Früherkennung von Mesotheliomen - Ludwig, Kathrin; Eisenkolb, Alexander; Hering, Jan Philipp; Rehbock, Beate; Bayerl, Nadine; Hofmann-Prei, Karina; Kraus, Thomas (Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2021)
CRIS-Team Scopus Importer:2021-10-01

Evaluation of a novel content-based image retrieval system for the differentiation of interstitial lung diseases in CT examinations - Pogarell, Tobias; Bayerl, Nadine; Wetzl, Matthias; Roth, Jessica; Speier, Christoph; Uder, Michael; Dankerl, Peter; Cavallaro, Alexander Josef (Diagnostics, 2021)
To evaluate the reader’s diagnostic performance against the ground truth with and without the help of a novel content-based image retrieval system (CBIR) that retrieves images with similar CT patterns from a database of 79 different interstitial lung diseases. We evaluated three novice readers’ and three resident physicians‘ (with at least three years of experience) diagnostic performance evaluating 50 different CTs featuring 10 different patterns (e.g., honeycombing, tree-in bud, ground glass, bronchiectasis, etc.) and 24 different diseases (sarcoidosis, UIP, NSIP, Aspergillosis, COVID-19 pneumonia etc.). The participants read the cases first without assistance (and without feedback regarding correctness), and with a 2-month interval in a random order with the assistance of the novel CBIR. To invoke the CBIR, a ROI is placed into the pathologic pattern by the reader and the system retrieves diseases with similar patterns. To further narrow the differential diagnosis, the readers can consult an integrated textbook and have the possibility of selecting high-level semantic features representing clinical information (chronic, infectious, smoking status, etc.). We analyzed readers’ accuracy without and with CBIR assistance and further tested the hypothesis that the CBIR would help to improve diagnostic performance utilizing Wilcoxon signed rank test. The novice readers demonstrated an unassisted accuracy of 18/28/44%, and an assisted accuracy of 84/82/90%, respectively. The resident physicians demonstrated an unassisted accuracy of 56/56/70%, and an assisted accuracy of 94/90/96%, respectively. For each reader, as well as overall, Sign test demonstrated statistically significant (p < 0.01) difference between the unassisted and the assisted reads. For students and physicians, Chi2-test and Mann-Whitney-U test demonstrated statistically significant (p < 0.01) difference for unassisted reads and statistically insignificant (p > 0.01) difference for assisted reads. The evaluated CBIR relying on pattern analysis and featuring the option to filter the results of the CBIR by predominant characteristics of the diseases via selecting high-level semantic features helped to drastically improve novices’ and resident physicians’ accuracy in diagnosing interstitial lung diseases in CT.
CRIS-Team Scopus Importer:2021-12-03
Keywords: Computed tomography; Computer-aided diagnosis; Content-based image retrieval; Interstitial lung disease

Improved visualization of peripherally inserted central catheters on chest radiographs of neonates using fractional multiscale image processing - Hammon, Rebecca A.; Seuß, Hannes; Hammon, Matthias; Grillhoesl, Christian; Heiß, Rafael; Zeilinger, Martin; Bayerl, Nadine; Vuylsteke, Pieter; Wanninger, Friedrich; Schroth, Michael; Uder, Michael; Rompel, Oliver (BMC Medical Imaging, 2019)
BACKGROUND: Peripherally inserted central catheters (PICCs) provide secure intravenous access for the delivery of life-sustaining medications and nutrition. They are commonly used in pediatrics. Confirmation of correct central catheter tip position is crucial. Verification is usually done by a radiograph. The aim of this study is to evaluate the ability of Fractional Multiscale image Processing (FMP) to detect PICC tips on the digital chest radiographs of neonates. METHODS: A total of 94 radiographs of 47 patients were included in the study. 29 patients were male, 18 were female. The mean age of all examined children was 9.2 days (range 0-99 days). In total, six readers (two radiologists, two residents in radiology, one last year medical student, one neonatologist) evaluated 94 unprocessed and catheter-enhanced radiographs using a 5-point Likert scale (1 = poor catheter tip visualization, 5 = excellent catheter tip visualization). Additionally, the two radiologists evaluated the diagnostic confidence for chest pathologies using a 5-point Likert scale (1 = poor diagnostic confidence, 5 = excellent diagnostic confidence). Radiographs were evaluated on a dedicated workstation. RESULTS: In all cases, the catheter-enhanced radiograph rated higher than (n = 471), or equal (n = 93) to, the unprocessed radiograph when visualizing catheter tips. 87% of the catheter-enhanced radiographs obtained a rating of 4 or higher, while only 42% of unprocessed radiographs received 4 or more points. Regarding diagnostic confidence for chest pathologies one radiologist rated two catheter-enhanced radiographs higher than the unprocessed radiographs, while all other 186 evaluations rated the catheter-enhanced radiographs equal to (n = 78) or lower than (n = 108) the unprocessed radiographs. Only 60% of the catheter-enhanced radiographs yielded a diagnostic confidence of 4 or higher, while 90% of the unprocessed images received 4 or more points. CONCLUSION: Catheter-enhanced digital chest radiographs demonstrate improved visualization of low contrast PICC tips in neonates compared to unprocessed radiographs. Furthermore, they enable detection of accompanying chest pathologies. However, definitive diagnosis of chest pathologies should be made on unprocessed radiographs.
EVALuna2:36095

Iterative Metal Artifact Reduction in Head and Neck CT Facilitates Tumor Visualization of Oral and Oropharyngeal Cancer Obscured by Artifacts From Dental Hardware - Bayerl, Nadine; May, Matthias; Wuest, Wolfgang; Roth, Jessica; Kramer, Manuel; Hofmann, Christian; Schmidt, Bernhard; Uder, Michael; Ellmann, Stephan (Academic Radiology, 2023)
Rationale and Objectives: The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose. Materials and Methods: The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64 ± 12.7 years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined. Results: iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P < .001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P < .001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P < .05), reaching a maximum with iMAR 5. Conclusion: iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths.
CRIS-Team Scopus Importer:2023-05-26
Keywords: Artifact; Cancer imaging; Computed tomography; Head and neck; Metal artifact reduction

Large Language Models for Simplified Interventional Radiology Reports: A Comparative Analysis - Can, Elif; Uller, Wibke; Vogt, Katharina; Doppler, Michael C.; Busch, Felix; Bayerl, Nadine; Ellmann, Stephan; Kader, Avan; Elkilany, Aboelyazid; Makowski, Marcus R.; Bressem, Keno K.; Adams, Lisa C. (Academic Radiology, 2024)
Purpose: To quantitatively and qualitatively evaluate and compare the performance of leading large language models (LLMs), including proprietary models (GPT-4, GPT-3.5 Turbo, Claude-3-Opus, and Gemini Ultra) and open-source models (Mistral-7b and Mistral-8×7b), in simplifying 109 interventional radiology reports. Methods: Qualitative performance was assessed using a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates, including trust-breaking and post-therapy misconduct errors. Quantitative readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), SMOG Index, and Dale-Chall Readability Score (DCRS). Paired t-tests and Bonferroni-corrected p-values were used for statistical analysis. Results: Qualitative evaluation showed no significant differences between GPT-4 and Claude-3-Opus for any metrics evaluated (all Bonferroni-corrected p-values: p = 1), while they outperformed other assessed models across five qualitative metrics (p < 0.001). GPT-4 had the fewest content and trust-breaking errors, with Claude-3-Opus second. However, all models exhibited some level of trust-breaking and post-therapy misconduct errors, with GPT-4-Turbo and GPT-3.5-Turbo with few-shot prompting showing the lowest error rates, and Mistral-7B and Mistral-8×7B showing the highest. Quantitatively, GPT-4 surpassed Claude-3-Opus in all readability metrics (all p < 0.001), with a median FRE score of 69.01 (IQR: 64.88–73.14) versus 59.74 (IQR: 55.47–64.01) for Claude-3-Opus. GPT-4 also outperformed GPT-3.5-Turbo and Gemini Ultra (both p < 0.001). Inter-rater reliability was strong (κ = 0.77–0.84). Conclusions: GPT-4 and Claude-3-Opus demonstrated superior performance in generating simplified IR reports, but the presence of errors across all models, including trust-breaking errors, highlights the need for further refinement and validation before clinical implementation. Clinical relevance/applications: With the increasing complexity of interventional radiology (IR) procedures and the growing availability of electronic health records, simplifying IR reports is critical to improving patient understanding and clinical decision-making. This study provides insights into the performance of various LLMs in rewriting IR reports, which can help in selecting the most suitable model for clinical patient-centered applications.
CRIS-Team Scopus Importer:2024-10-11
Keywords: Artificial Intelligence; Interventional Radiology; Large Language Model; Patient Friendliness; Structured Reporting

Position Paper on Magnetic Resonance Imaging as a non-specialist Service - Ludwig, Kathrin; Eisenkolb, Alexander; Hering, Jan Philipp; Rehbock, Beate; Bayerl, Nadine; Hofmann-Preiss, Karina; Kraus, Thomas (Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2021)
CRIS-Team WoS Importer:2021-10-15

The Effect of Cooling Fluid Composition on Ablation Size in Hepatic Laser Ablation: A Comparative Study in an Ex Vivo Bovine Setting - Mankertz, Fiona; Bayerl, Nadine; Gemeinhardt, Ole; Hosten, Norbert; Kromrey, Marie Luise (Tomography, 2023)
PURPOSE: Hyperthermic ablation is a minimally invasive mode of tumour therapy which serves as a viable alternative to surgical intervention. However, one of the major drawbacks, besides the heat sink effect and the risk of damaging adjacent organs, is limited ablation size. The use of a cooling fluid during ablation has been shown to increase the ablation volume and decrease the carbonisation rate. The aim of this study was to investigate whether the composition of the cooling fluid has an effect on ablation size and carbonisation rate during hepatic laser ablation in an ex vivo bovine setting. METHOD: In this study bovine hepatic tissue was ablated in an ex vivo setting using an internally cooled laser applicator. A total of 45 tissue samples were assigned to three groups: 0.9% saline infusion (n = 15), distilled water infusion (n = 15) and a 50%/50% mixture of 0.9% saline and distilled water (n = 15). Ablation was conducted using a 1064 nm Nd:YAG laser at a wattage of 25 W and time interval of 10 min. The ablation volume and carbonisation rate were then measured and recorded through postprocedural MRI. One-way ANOVA and post-hoc testing were performed to assess the effect of the cooling fluid composition on the ablation volumes. RESULTS: We found that using a mixture of saline and distilled water as a cooling fluid during hyperthermic ablation resulted in a larger ablation volume (mean ± SD: 22.64 ± 0.99 cm3) when compared to saline infusion (21.08 ± 1.11 cm3) or distilled water infusion (20.92 ± 0.92 cm3). This difference was highly significant (p < 0.001). There was no significant difference in ablation size between the saline group and the distilled water group. The highest carbonisation rate occurred in the saline group (12/15), followed by the mixed infusion group (3/15) and the distilled water group (1/15). CONCLUSIONS: The results of this study suggest that cooling fluid composition during hepatic laser ablation affects ablation volume in an ex vivo bovine setting. There was no statistically significant difference when comparing ablation volumes during saline infusion and distilled water infusion, but the carbonisation rate was significantly higher when using saline. The combination of saline and distilled water in a 50%/50% mixture as cooling fluid appears to be an auspicious alternative, as ablation volumes created with it are larger when compared to saline and distilled water alone, while carbonisation rate remains low. This might improve patient outcome as well as patient eligibility for hyperthermic ablation.
CRIS-Team Scopus Importer:2023-09-29
Keywords: experimental radiology; hyperthermic ablation; interventional radiology; laser ablation; oncoradiology

Treatment of a patient with severe systemic sclerosis (SSc) using CD19-targeted CAR T cells. - Bergmann, Christina; Müller, Fabian; Distler, Jörg; Györfi, Andrea Hermina; Völkl, Simon; Aigner, Michael; Kretschmann, Sascha; Reimann, Hannah; Harrer, Thomas; Bayerl, Nadine; Böltz, Sebastian; Wirsching, Andreas; et al. (Annals of the Rheumatic Diseases, 2023)
EVALuna2:537682

Tumor grade-titude: XGBoost radiomics paves the way for RCC classification - Ellmann, Stephan; von Rohr, Felicitas; Komina, Selim; Bayerl, Nadine; Amann, Kerstin Ute; Polifka, Iris; Hartmann, Arndt; Sikic, Danijel; Wullich, Bernd; Uder, Michael; Bäuerle, Tobias (European Journal of Radiology, 2025)
This study aimed to develop and evaluate a non-invasive XGBoost-based machine learning model using radiomic features extracted from pre-treatment CT images to differentiate grade 4 renal cell carcinoma (RCC) from lower-grade tumours. A total of 102 RCC patients who underwent contrast-enhanced CT scans were included in the analysis. Radiomic features were extracted, and a two-step feature selection methodology was applied to identify the most relevant features for classification. The XGBoost model demonstrated high performance in both training (AUC = 0.87) and testing (AUC = 0.92) sets, with no significant difference between the two (p = 0.521). The model also exhibited high sensitivity, specificity, positive predictive value, and negative predictive value. The selected radiomic features captured both the distribution of intensity values and spatial relationships, which may provide valuable insights for personalized treatment decision-making. Our findings suggest that the XGBoost model has the potential to be integrated into clinical workflows to facilitate personalized adjuvant immunotherapy decision-making, ultimately improving patient outcomes. Further research is needed to validate the model in larger, multicentre cohorts and explore the potential of combining radiomic features with other clinical and molecular data.
CRIS-Team Scopus Importer:2025-05-16
Keywords: Computed tomography (CT); Diagnostic performance; Feature extraction; Machine learning; Personalized immunotherapy; Radiomics; Renal cell carcinoma (RCC); Treatment decision-making; Tumor grading

Vision, Development, and Structure of the First German Specialist Training Curriculum for Radiology - Molwitz, Isabel; Frisch, Anne; Adam, Gerhard; Afat, Saif; Ammon, Josefin; Antoch, Gerald; Baeßler, Bettina; Balks, Friederike; Barkhausen, Jörg; Bayerl, Nadine; Brendlin, Andreas; Bucher, Andreas Michael; et al. (Röfo: Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren, 2022)
CRIS-Team Scopus Importer:2022-08-12