Johannes Rueckel

Purpose Surface modifications may improve procedural safety of flow diverter (FD) treatment, but larger studies are rare. This multicenter study aims to evaluate the safety and efficacy of unruptured aneurysm treatment with surface-modified and coated FDs. Methods Patients treated with the following FDs at 8 neurovascular centers were retrospectively reviewed: Pipeline Vantage Embolization Device, Pipeline Flex with Shield Technology, FRED X, p48/64 HPC, Derivo Embolization Device (DED), and DED 2heal. Aneurysm characteristics, procedural details, and angiographic results were evaluated in detail. Results A total of 511 patients with 545 aneurysms were included (mean aneurysm size: 8.2 ± 5.5 mm, 12% posterior circulation, 14% non-saccular morphology). Multiple FDs were used in 26/515 (5%) procedures and adjunctive coiling in 76 (15%). Thromboembolic events occurred in 40 (7.8%) cases, including 9 (1.7%) major ischemic events, 20 (3.9%) minor ischemic events and 11 (2.1%) asymptomatic/technical events. Non-saccular aneurysm morphology (p = 0.014) and use of multiple FDs (p = 0.025) favored thromboembolic events. Hemorrhagic events occurred in 5 (1.0%) cases, of which 3 were symptomatic. The combined morbidity and mortality rate was 11/515 (2.1%). Complete and adequate occlusion rates were 66% (196/297) and 80% (238/297) at 6 months, respectively, and 70% (64/92) and 85% (78/92) at 12 months, respectively. Conclusion The present study demonstrates high safety and efficacy for coated or surface-modified FDs. Whether the surface modifications confer a clinical benefit needs to be addressed in comparative studies.

Background: Accurate device sizing is crucial for successful flow diverter (FD) therapy in intracranial aneurysms. This study assesses the accuracy and clinical utility of the Ankyras virtual simulation software (Mentice AB, Gothenburg, Sweden) in predicting FD length across multiple device types and manufacturers. Methods: We retrospectively analyzed 193 FDs (7 device types) deployed in 180 patients with 230 intracranial aneurysms. Simulation-based prediction of effective lengths (simulated length (SL)) and nominal manufacturer specifications (labeled length) were compared with measured in vivo lengths (measured length (ML)). Performance was evaluated using simulation accuracy (SA), absolute error (AE), relative error (RE), length ratio, and correlation analysis. Results: Virtual simulation demonstrated good clinical usability, achieving a mean absolute deviation of only −1.38 mm at the proximal landing zone compared with ML. Simulation-based predictions showed significantly superior accuracy compared with nominal manufacturer specifications (SA 89.6±11.3% vs 81.8±13.7%, P<0.001), with mean AE reduced by 58% (−1.38 mm vs −3.34 mm) and mean RE by 42% (10.5% vs 18.2%). Strong correlations between SL and ML (r=0.900) validated predictive reliability across all tested device types. Centerline correction technology further enhanced parameters such as SA to 94.8±9.5%. Conclusion: Virtual simulation-based prediction of effective FD length using the Ankyras software showed reliable and clinically meaningful results, enabling accurate estimations of the proximal landing zone and overall length. Ankyras, along with comparable simulation platforms, may provide considerable potential to facilitate FD selection and implementation, especially in anatomically complex cases.

Background: Aneurysm-related subarachnoid hemorrhage is a life-threatening form of stroke. While medical image acquisition for aneurysm screening is limited to high-risk patients, advances in artificial intelligence (AI)-based image analysis suggest that AI-driven routine screening of imaging studies acquired for other clinical reasons could be valuable. Methods: A representative cohort of 1761 routine cranial magnetic resonance imaging scans [cMRIs] (with time-of-flight angiographies) from patients without previously known intracranial aneurysms was established by combining 854 general radiology 1.5T and 907 neuroradiology 3.0T cMRIs. TOF-MRAs were analyzed with a commercial AI algorithm for aneurysm detection. Neuroradiology consultants re-assessed cMRIs with AI results, providing Likert-based confidence scores (0–3) and work-up recommendations for suspicious findings. Original cMRI reports from more than 90 radiologists and neuroradiologists were reviewed, and patients with new findings were contacted for consultations including follow-up imaging (cMRI / catheter angiography [DSA]). Statistical analysis was conducted based on descriptive statistics, common diagnostic metrics, and the number needed to screen (NNS), defined as the number of cMRIs that must be analyzed with AI to achieve specific clinical endpoints. Results: Initial cMRI reporting by radiologists/neuroradiologists demonstrated a high risk of incidental aneurysm non-reporting (94.4% / 86.4%). A finding-based analysis revealed high AI algorithm sensitivities (100% [3T] / 94.1% [1.5T] for certain aneurysms of any size, well above 90% for any suspicious findings > 2 mm), associated with AI alerts triggered in 22% of cMRIs with PPVs of 7.5–25.2% (depending on the inclusion of inconclusive findings). The NNS to prompt further imaging work-/follow-up was 22, while the NNS to detect an aneurysm with a possible therapeutic impact was 221. Reference readings and patient consultations suggest that routine AI-driven cMRI screening would lead to additional imaging for 4–5% of patients, with 0.45% to 0.74% found to have previously undetected aneurysms with possibly therapeutic implications. Conclusions: AI-based second-reader screening substantially reduces incidental aneurysm non-reporting but may disproportionally increase follow-/work-up imaging demands also for minor or inconclusive findings with associated patient concern. Future research should focus on (subgroup-specific) AI optimization and cost-effectiveness analyses.

Objectives: To quantify the clinical value of integrating a commercially available artificial intelligence (AI) algorithm for intracranial aneurysm detection in a screening setting that utilizes cranial magnetic resonance imaging (cMRI) scans acquired primarily for other clinical purposes. Methods: A total of 907 consecutive cMRI datasets, including time-of-flight-angiography (TOF-MRA), were retrospectively identified from patients unaware of intracranial aneurysms. cMRIs were analyzed by a commercial AI algorithm and reassessed by consultant-level neuroradiologists, who provided confidence scores and workup recommendations for suspicious findings. Patients with newly identified findings (relative to initial cMRI reports) were contacted for on-site consultations, including cMRI follow-up or catheter angiography. The number needed to screen (NNS) was defined as the cMRI quantity that must undergo AI screening to achieve various clinical endpoints. Results: The algorithm demonstrates high sensitivities (100% for findings >4 mm in diameter), a 17.8% MRA alert rate and positive predictive values of 11.5–43.8% (depending on whether inconclusive findings are considered or not). Initial cMRI reports missed 50 out of 59 suspicious findings, including 13 certain intradural aneurysms. The NNS for additionally identifying highly suspicious and therapeutically relevant (unruptured intracranial aneurysm treatment scores balanced or in favor of treatment) findings was 152. The NNS for recommending additional follow-/workup imaging (cMRI or catheter angiography) was 26, suggesting an additional up to 4% increase in imaging procedures resulting from a preceding AI screening. Conclusions: AI-powered routine screening of cMRIs clearly lowers the high risk of incidental aneurysm non-reporting but results in a substantial burden of additional imaging follow-up for minor or inconclusive findings.

Objective: In suspected acute ischemic stroke, it is now reasonable to expand the conventional “stroke protocol” (non-contrast computed tomography (NCCT), arterial CT angiography (CTA), and optionally CT perfusion (CTP)) to early and late venous head scans yielding a multiphase CTA (MP-CTA) to increase diagnostic confidence. Diagnostic reference levels (DRLs) have been defined for neither MP-CTA nor CTP. We therefore present dosimetry data, while also considering image quality, for a large, unselected patient cohort. Methods: A retrospective single-center study of 1790 patients undergoing the extended stroke protocol with three scanners (2× dual-source, DSCT; 1× single-source, SSCT) between 07/21 and 12/23 was conducted. For each sequence, we analyzed the radiation dose (volumetric CT dose index (CTDIvol); dose length product; effective dose); objective image quality using manually placed regions of interest (contrast-to-noise ratio (CNR)); and subjective image quality (4-point scale: 1 = non-diagnostic, 4 = excellent). The DRL was defined as the 75% percentile of the CTDIvol distribution. The Kruskal-Wallis test was used initially to test for overall equality of median values in each data group. Single post-test comparisons were performed with Dunn’s test, with an overall statistical significance level of 0.05. Results: Dosimetry values were significantly higher for SSCT (p < 0.001, each). Local DRLs ranged between 37.3 and 49.1 mGy for NCCT, 3.6–5.5 mGy for arterial CTA, 1.2–2.5 mGy each for early/late venous CTA, and 141.1–220.5 mGy for CTP. Protocol adjustment (DSCT-1: CTP) yielded a 28.2% dose reduction. The highest/lowest CNRs (arterial/early venous CTA, respectively) were recorded for SSCT/DSCT-2 (p < 0.001). Subjective image quality was rated excellent except for slightly increased MP-CTA noise at DSCT-2 (median = 3). Conclusions: Our data imply that additive MP-CTA scans only yield a minor increase in radiation exposure, particularly when using DSCT. CTP should be limited to selected patients.

Introduction: The Derivo 2 Heal Embolization Device (D2HED) is a novel flow diverter (FD) providing a fibrin-/heparin-based surface coating aiming at lower thrombogenicity. We evaluate periprocedural aspects and preliminary aneurysm occlusion efficacy for intracranial aneurysm treatment. Methods: Thirty-four D2HEDs deployments (34 aneurysms, 32 patients) between 04/2021 and 10/2023 were analyzed. All patients were under dual antiplatelet therapy (dAPT). Periprocedural details, adverse events, and follow-up (FU) imaging were reviewed by consultant-level neuroradiologists. Complication rates and aneurysm occlusion efficacy are compared with performance data of other FDs based on literature research. Results: Each intervention succeeded in the deployment of one D2HED. Significant and/or increased intraaneurysmal contrast stagnation immediately after D2HED deployment was seen in 73.5% of cases according to O’Kelly-Marotta (OKM) grading scale. Clinically relevant early adverse events occurred in three patients: Among them two cases with fusiform aneurysms in the posterior circulation (ischemic events, early in-stent-thrombosis) and one patient (ischemic event) out of the majority of 31 treated internal carotid artery aneurysms (3,2%). Regarding mid-term FU (> 165 days), one aneurysm did not show progressive occlusion presumably caused by a prominent A1 segment arising from the terminal ICA aneurysm itself. Apart from that, mid-term complete / partial occlusion rates of 80% / 20% could be demonstrated. Conclusion: Our case series - although suffering from restricted sample size - suggests a potential effectiveness of D2HED in managing intracranial aneurysms. Further studies with larger samples are warranted to quantify long-term occlusion efficacy and the impact of antithrombogenic surface coating on the necessary (d)APT.

Purpose: To develop and validate an artificial intelligence algorithm for the positioning assessment of tracheal tubes (TTs) and central venous catheters (CVCs) in supine chest radiographs (SCXRs) by using an algorithm approach allowing for adjustable definitions of intended device positioning. Materials and Methods: Positioning quality of CVCs and TTs is evaluated by spatially correlating the respective tip positions with anatomical structures. For CVC analysis, a configurable region of interest is defined to approximate the expected region of well-positioned CVC tips from segmentations of anatomical landmarks. The CVC/TT information is estimated by introducing a new multitask neural network architecture for jointly performing type/existence classification, course segmentation, and tip detection. Validation data consisted of 589 SCXRs that have been radiologically annotated for inserted TTs/CVCs, including an experts' categorical positioning assessment (reading 1). In-image positions of algorithm-detected TT/CVC tips could be corrected using a validation software tool (reading 2) that finally allowed for localization accuracy quantification. Algorithmic detection of images with misplaced devices (reading 1 as reference standard) was quantified by receiver operating characteristics. Results: Supine chest radiographs were correctly classified according to inserted TTs/CVCs in 100%/98% of the cases, thereby with high accuracy in also spatially localizing the medical device tips: corrections less than 3 mm in >86% (TTs) and 77% (CVCs) of the cases. Chest radiographs with malpositioned devices were detected with area under the curves of >0.98 (TTs), >0.96 (CVCs with accidental vessel turnover), and >0.93 (also suboptimal CVC insertion length considered). The receiver operating characteristics limitations regarding CVC assessment were mainly caused by limitations of the applied CXR position definitions (region of interest derived from anatomical landmarks), not by algorithmic spatial detection inaccuracies. Conclusions: The TT and CVC tips were accurately localized in SCXRs by the presented algorithms, but triaging applications for CVC positioning assessment still suffer from the vague definition of optimal CXR positioning. Our algorithm, however, allows for an adjustment of these criteria, theoretically enabling them to meet user-specific or patient subgroups requirements. Besides CVC tip analysis, future work should also include specific course analysis for accidental vessel turnover detection.

(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0–4; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability/correlation and Kruskal–Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists’ and non-radiologists’ reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1–3). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested.

Background: Yttrium-90 radioembolization (RE) plays an important role in the treatment of liver malignancies. Optimal patient selection is crucial for an effective and safe treatment. In this study, we aim to validate the prognostic performance of a previously established random survival forest (RSF) with an external validation cohort from a different national center. Furthermore, we compare outcome prediction models with different established metrics. Methods: A previously established RSF model, trained on a consecutive cohort of 366 patients who had received RE due to primary or secondary liver tumor at a national center (center 1), was used to predict the outcome of an independent consecutive cohort of 202 patients from a different national center (center 2) and vice versa. Prognostic performance was evaluated using the concordance index (C-index) and the integrated Brier score (IBS). The prognostic importance of designated baseline parameters was measured with the minimal depth concept, and the influence on the predicted outcome was analyzed with accumulated local effects plots. RSF values were compared to conventional cox proportional hazards models in terms of C-index and IBS. Results: The established RSF model achieved a C-index of 0.67 for center 2, comparable to the results obtained for center 1, which it was trained on (0.66). The RSF model trained on center 2 achieved a C-index of 0.68 on center 2 data and 0.66 on center 1 data. CPH models showed comparable results on both cohorts, with C-index ranging from 0.68 to 0.72. IBS validation showed more differentiated results depending on which cohort was trained on and which cohort was predicted (range: 0.08 to 0.20). Baseline cholinesterase was the most important variable for survival prediction. Conclusion: The previously developed predictive RSF model was successfully validated with an independent external cohort. C-index and IBS are suitable metrics to compare outcome prediction models, with IBS showing more differentiated results. The findings corroborate that survival after RE is critically determined by functional hepatic reserve and thus baseline liver function should play a key role in patient selection.