The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. / Der Arzt im Radiologen ­ künstliche Intelligenz als Möglichkeit, das Spannungsfeld zwischen Patient, Technik und Zuweisern zu lösen ­ ein narratives Review.

BACKGROUND: Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits. METHOD: Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review. RESULTS: The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction. CONCLUSION: Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians. KEY POINTS: · Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline.. CITATION FORMAT: · Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2271-0799.

Endovascular treatment of renal artery stenosis in Germany: a retrospective analysis of the DEGIR registry 2018-2021.

PURPOSE: To provide an overview of endovascular treatment of renal artery stenosis (RAS) using the data of the Deutsche Gesellschaft für interventionelle Radiologie (DeGIR) quality management system. MATERIALS AND METHODS: A retrospective analysis was performed. Pre-, peri- and postprocedural data, technical success rates, complication rates, and clinical success rates at dismissal were examined. RESULTS: Between 2018 and 2021, 2134 angiography examinations of the renal arteries were performed: diagnostic angiography in 70 patients (3 %), balloon angioplasty in 795 (37 %), stent implantation in 1166 (55 %) and miscellaneous procedures in 103 (5 %). The lesion length was less than or equal to 5 mm in 1837 patients (87 %), between 5 and 10 mm in 197 (9 %), and between 10 and 20 mm in 62 (3 %). The degree of stenosis was less than 50 % in 156 patients (7 %), greater than 50 % in 239 (11 %), and greater than 70 % in 1472 (70 %). Occlusion was treated in 235 patients (11 %). Symptoms at discharge resolved in 600 patients (29 %), improved in 1012 (49 %), were unchanged in 77 (4 %), and worsened in 5 (0.2 %). Complications were reported in 51 patients (2.5 %) and the mortality rate was 0.15 %. CONCLUSION: A substantial number of patients with RAS and occlusions were treated by radiologists in Germany, with high technical success rates and low complication rates. The indication should be determined carefully as the current European guidelines for the treatment of RAS suggest that only carefully selected groups of patients will benefit from recanalizing treatment. KEY POINTS: · Carefully selected patient groups may benefit from endovascular treatment of renal artery stenosis.. · Analysis of the DEGIR quality management database shows that treatment of renal artery stenosis was performed by radiologists in Germany with high technical success rates and low complication rates.. · Recanalization even led to symptom improvement in a large proportion of patients with occlusions..

The Current Use of Drug-Eluting Balloons and Stents in Peripheral Arterial Disease: An Online Survey by the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).

PURPOSE: To assess the current use of drug-eluting devices for peripheral arterial disease (PAD) among interventional radiologists following the controversy caused by the 2018 meta-analysis suggesting an increased mortality risk for paclitaxel-eluting devices. METHODS: An anonymous survey was sent to 7035 CIRSE members via email; only complete responses were included and statistically analysed. RESULTS: Three hundred and seven members (4.4%) completed the survey. Among these, 95.8% indicated that they personally perform peripheral vascular procedures. Thirty-eight percentage of respondents did not see any change of practice since 2018, while 47% reported that the use of drug-eluting devices decreased; for 13%, the use stopped altogether, while it increased in 3% of responses. 45.6% of respondents also felt the impact of the controversy in terms of pricing, availability or directives from hospital administration. A large majority of respondents (83.7%) who perform peripheral vascular procedures consider the use of these devices as safe, 12.9% were undecided and 3.4% did not consider them as safe. Among the respondents who do not perform endovascular procedures, 77% considered these devices as safe and 23% were undecided. CONCLUSION: Although the 2018 meta-analysis had a disruptive impact on the use of drug-eluting devices in PAD, with the increasing body of evidence available, a majority of respondents continue to believe in the safety of these devices for use in femoropopliteal disease.

[Correlation between comorbidities and thoracic CT manifestations of COVID-19 pneumonia]. / Korrelation zwischen Komorbiditäten und Veränderungen des Lungenparenchyms im CT des Thorax bei Patienten mit COVID-19-Pneumonie.

BACKGROUND AND OBJECTIVES: Pulmonary manifestation of coronavirus disease 2019 (COVID-19) is described using standardized computed tomography (CT) morphologic criteria. In this study, we investigated possible associations between thoracic CT manifestations in COVID-19 pneumonia and typical comorbidities, as well as clinical course. METHODS: We analyzed clinical data and pulmonary imaging of 61 patients with positive PCR test. Pulmonary changes were categorized and reviewed for associations with pre-existing comorbidities and clinical course. RESULTS: Compared to patients with atypical infiltrate patterns (2/19, 10.5%), 25 patients with typical infiltrate patterns (25/42, 59.5%) were significantly more likely to receive intensive care (p<0.001). In addition, patients with typical infiltrate patterns were more likely to receive non-invasive ventilation (12/42, 28.6%, p=0.040) and high-flow therapy (8/42, 19%, p=0.041) compared to patients with atypical infiltrate patterns. Mortality was also higher in patients with typical infiltrate patterns, with 15 patients (15/42, 35.7%) dying during follow-up compared to only 1 patient with atypical infiltrate pattern (1/19, 10.5%, p=0.012). No significant association between specific comorbidities and the resulting infiltrate pattern could be demonstrated. CONCLUSIONS: Patients with a typical COVID-19 infiltrate pattern are more likely to receive intensive care and show higher mortality rates. Further analysis with larger patient collectives is needed to identify specific risk factors for typical COVID-19 pneumonia.

CIRSE Position Paper on Artificial Intelligence in Interventional Radiology.

Artificial intelligence (AI) has made tremendous advances in recent years and will presumably have a major impact in health care. These advancements are expected to affect different aspects of clinical medicine and lead to improvement of delivered care but also optimization of available resources. As a modern specialty that extensively relies on imaging, interventional radiology (IR) is primed to be on the forefront of this development. This is especially relevant since IR is a highly advanced specialty that heavily relies on technology and thus is naturally susceptible to disruption by new technological developments. Disruption always means opportunity and interventionalists must therefore understand AI and be a central part of decision-making when such systems are developed, trained, and implemented. Furthermore, interventional radiologist must not only embrace but lead the change that AI technology will allow. The CIRSE position paper discusses the status quo as well as current developments and challenges.

Machine learning based prediction of length of stay in acute ischaemic stroke of the anterior circulation in patients treated with thrombectomy.

BACKGROUND: Length of stay is an important factor for managing the limited resources of a hospital. The early, accurate prediction of hospital length of stay leads to the optimized disposition of resources particularly in complex stroke treatment. OBJECTIVE: In the present study we evaluated different machine learning techniques in their ability to predict the length of stay of patients with stroke of the anterior circulation who were treated with thrombectomy. MATERIAL AND METHODS: This retrospective study evaluated four algorithms (support vector machine, generalized linear model, K-nearest neighbour and Random Forest) to predict the length of hospitalization of 113 patients with acute stroke who were treated with thrombectomy. Input variables encompassed baseline data at admission, as well as periprocedural and imaging data. Ten-fold cross-validation was used to estimate accuracy. The accuracy of the algorithms was checked with a test dataset. In addition to regression analysis, we performed a binary classification analysis to identify patients that stayed longer than the mean length of stay. RESULTS: Mean length of stay was 10.7 days (median 10, interquartile range 6-15). The sensitivity of the best-performing Random Forest model was 0.8, the specificity was 0.68 and the area under the curve was 0.73 in the classification analysis. The mean absolute error of the best-performing Random Forest Model was 4.6 days in the test dataset in the regression analysis. CONCLUSION: Machine learning has potential use to estimate the length of stay of patients with acute ischaemic stroke that were treated with thrombectomy.

Value of CT in COVID-19-pandemia: A systematic analysis of CT-findings and outcomes in patients with COVID-19 pneumonia.

Chest-computer tomography (CT) is a crucial factor in the clinical course and evaluation of patients with COVID-pneumonia. In the initial phase of the COVID-19 pandemic little information was known on the prognostic value of the initially taken thoracic CTs. The purpose of this study was to determine predictive values for clinical outcome based on CT classification of the pulmonary pathologies in patients with COVID-pneumonia. This single center study included 51 non-immunized patients during the first COVID-19 outbreak in Germany. The patients underwent a clinically indicated chest-CT. Using the radiological society of North America (RSNA)-report template, chest-CTs were classified into 4 categories (typical, atypical, indeterminate, and no changes). We analyzed the outcomes based on these imaging classifications and relevant comorbidities. Among the 51 patients of our study population 14 (27.5%) patients had a lethal outcome. Typical radiological COVID-19 pattern was found in 92.9% of the deceased patients and in 59.5% of the surviving patients (P = .022). The lethal group showed a significant higher proportion of diabetes mellitus (50% vs 10.8%; P = .003) and arterial hypertension (aHTN) (85.7% vs 54.1%; P = .037). Male sex, higher age and coronary heart disease (CHD) were also seen more often in the lethal group. In patients with clinically proven COVID-19 pneumonia, typical chest CT findings show a negative outcome. A classification system used in this study is helpful for classifying imaging features and is recommended as a standardized CT reporting tool. It could also help in triaging of the therapy of patients with COVID-19 pneumonia. Especially the comorbidities, diabetes and arterial hypertonia triggered a negative outcome in our study population.

Thrombectomy in ischemic stroke patients with alberta stroke program early computed tomography score 4-5 and 0-3: Factors associated with favorable outcome.

OBJECTIVES: Recently published results of the ANGEL-ASPECT and SELECT2 trials suggest that stroke patients presenting with low Alberta Stroke Program Early Computed Tomography Score (ASPECTS) benefit from mechanical thrombectomy. Purpose of this retrospective study was to identify factors that are associated with a favorable outcome in patients with low ASPECTS of 4-5 and 0-3 undergoing mechanical thrombectomy. MATERIAL AND METHODS: All patients reported in the quality registry of the German Society for Neuroradiology that were treated between 2018 and 2020 were analyzed. Favorable outcome was defined as a National Institute of Health Stroke Scale (NIHSS) score of less than 9 at dismissal. Successful recanalization was defined as Thrombolysis in Cerebral Infarction (mTICI) ≥ 2b. Multivariable logistic regression analyses were performed to assess the association of baseline and treatment variables with favorable outcome. RESULTS: 621 patients were included in the analysis, thereof 495 with ASPECTS 4-5 and 126 with ASPECTS 0-3. In patients with ASPECTS 4-5patients with favorable outcome had less severe neurological symptoms at admission with median NIHSS of 15 vs. 18 (p<0.001), had less often wake-up strokes (44% vs. 81%, p<0.001), received more often iv-lysis (37% vs. 30%, p<0.001), had more often conscious sedation (29% vs. 16%, p<0.001), had a higher rate of successful recanalization (94% vs. 66% and lower times from groin puncture to recanalization. In multivariate regression analysis lower NIHSS at admission (aOR 0.87, CI 0.89-0.91) and successful recanalization (aOR 3.96, CI 2-8.56) were associated with favorable outcome. For ASPECTS 0-3, patients with favorable outcome had lower median NIHSS at admission (16 vs. 18 (p<0.001), lower number of passes (1 vs. 3, p=0.003) and a higher rate of successful recanalization (94% vs. 66%, p<0.001) and lower times from groin puncture to recanalization. In multivariate regression analysis lower NIHSS at admission (aOR 0.87, CI 0.81-0.94) and successful recanalization, (aOR 11.19, CI 3.19-55.53), were associated with favorable outcome. CONCLUSION: Full recanalization with low groin punction to recanalization times and low number of passes were associated with favorable outcome in patients with low ASPECTS.

NIMBUS geometric clot extractor for challenging clots: Real-world clinical experience and clot composition.

PURPOSE: Revascularization rates following mechanical thrombectomy (MT) for acute ischemic stroke (AIS) remain suboptimal for patients with fibrin-rich, recalcitrant clots. The NIMBUS Geometric Clot Extractor has demonstrated promising in vitro revascularization rates using fibrin-rich clot analogs. This study assessed the retrieval rate and composition of clot using NIMBUS in a clinical setting. METHODS: This retrospective study included patients who underwent MT with NIMBUS at two high-volume stroke centers between December 2019 and May 2021. NIMBUS was used for clots deemed challenging to remove at the interventionalist's discretion. At one of the centers, per pass clot was collected for histological analysis by an independent lab. RESULTS: A total of 37 patients (mean age 76.87 ± 11.73 years; 18 female; mean time from stroke onset 11.70 ± 6.41 h) were included. NIMBUS was used as first and second-line device in 5 and 32 patients, respectively. The main reason for using NIMBUS (32/37) was the failure of standard MT techniques after a mean 2.86 ± 1.48 number of passes. Substantial reperfusion (mTICI ≥2b) was achieved in 29/37 patients (78.4%) with a mean of 1.81 ± 1.00 NIMBUS passes (mean 4.68 ± 1.68 passes with all devices), and NIMBUS was the final device used in 79.3% (23/29) of those cases. Clot specimens from 18 cases underwent composition analysis. Fibrin and platelets represented 31.4 ± 13.7% and 28.8 ± 18.8% of clot components; 34.4 ± 19.5% were red blood cells. CONCLUSIONS: In this series, NIMBUS was effective in removing tough clots rich in fibrin and platelets in challenging real-world situations.

Outcome prediction prior to thrombectomy of the posterior circulation with machine learning.

PURPOSE: Various studies have identified prognostic factors for a favorable outcome of endovascular treatment in posterior circulation. We evaluated various machine learning algorithms in their ability to classify between patients with favorable (defined as 0-2 points on the modified Rankin scale [mRS]), unfavorable (mRS 3-6), poor (mRS 5-6), and nonpoor (mRS 0-4) outcomes at dismissal. METHODS: We retrospectively analyzed data from 415 patients that were treated between 2018 and 2021 from the multicentric DGNR registry. Five models (random forest, support vector machine, k-nearest neighbor, neural network [NN], and generalized linear model [GLM]) were trained with clinical input variables and evaluated with a test dataset of 82 patients. The model with the highest accuracy on the training dataset was defined as the best model. RESULTS: A total of 132 patients showed poor and 162 patients showed favorable outcome. All baseline variables except sex were highly significantly different between patients with favorable and unfavorable outcomes. The variables NIHSS, the presence of wake-up stroke, the administration of IV-thrombolysis and mRS pretreatment were significantly different between patients with poor and nonpoor outcomes. The best-performing NN achieved a sensitivity of 0.56, a specificity of 0.86 and an area under the curve (AUC) of 0.77 on the test dataset in the classification analysis between favorable and unfavorable outcomes. The best-performing GLM achieved a sensitivity of 0.65, a specificity of 0.91 and an AUC of 0.81 in the classification analysis between poor and nonpoor outcomes. CONCLUSION: Short-term favorable and poor outcomes in patients with acute ischemic stroke of the posterior circulation can be predicted prior to thrombectomy with moderate sensitivity and high specificity with machine learning models.

Feasibility of Network-Based, Online Endovascular Simulator Training in Real Time: Results from a Pilot Study.

PURPOSE: To test the feasibility of an online, simulator-based comprehensive interventional radiology (IR) training curriculum in times of COVID-19-induced travel restrictions. MATERIALS AND METHODS: A network of six VIST simulators (Mentice, Gothenburg, Sweden) was installed in six geographically different radiology departments. Two courses with six sessions each took place. 43 participants were recruited on a voluntary basis among local residents. The training sessions were conducted in real time with interconnected simulation devices and were led by experts in the field of IR on a rotational basis. The participants attitude toward various topics was quantified before and after training on a seven-point Likert scale (1 = "not at all", 7 = "to the highest degree"). In addition, post-course surveys were conducted. RESULTS: The courses led to an improvement for all items compared with baseline: interest in IR (pre: 5.5, post: 6.1), knowledge of endovascular procedures (pre: 4.1, post: 4.6), likelihood of choosing IR as a subspecialty (pre: 5.7, post: 5.9). Experience with endovascular procedures (pre: 3.7, post: 4.6) improved significantly (p = 0.016). In the post-course surveys high satisfaction rates with the pedagogical approach (mean 6), the teaching content (mean 6.4), and the duration and frequency of the course (mean 6.1) were observed. CONCLUSION: The implementation of a simultaneous endovascular online training curriculum in different geographic locations is feasible. The curriculum has the potential to meet the demand for training in IR in times of COVID-19-associated travel restrictions and can complement future training in the context of radiologic congresses. KEY POINTS: · The implementation of a simultaneous endovascular online training curriculum in different geographic locations is feasible. For interested residents, the presented online curriculum can offer a low-threshold and comprehensive entry into the world of interventional radiology at the site of their training..

Thrombectomy of Mild Stroke : An Analysis of the Nationwide Registry of the German Society for Neuroradiology.

PURPOSE: Whether patients presenting with mild stroke (NIHSS at admission < 6) should be treated with mechanical thrombectomy (MT) is the subject of an ongoing debate. This retrospective study based on large-scale clinical data aims to identify factors associated with favorable outcome (FO) in patients with mild stroke. METHODS: A total of 761 patients with mild stroke enrolled between 1 January 2020 and 31 December 2020 in the Quality Registry of the German Society for Neuroradiology were analyzed. The FO was defined as stable or improved NIHSS at discharge vs. admission. Descriptive statistics and multivariable logistic regression analyses were performed to identify factors associated with FO. Furthermore, a subgroup analysis of mild stroke based on distal vessel occlusion was conducted. RESULTS: In this study 610 patients had FO with a median NIHSS at discharge of 1 (interquartile range, IQR, 0-2) and 151 had an unfavorable outcome (UO) with median NIHSS at discharge of 10 (IQR 13). Patients with FO had a slightly higher NIHSS at admission (4 vs. 3, p < 0.001), lower mTICI 0 (2.7% vs. 14.2%, p < 0.001), higher mTICI 3 (61.3% vs. 34.5%, p < 0.001) and a lower number of passes (1 vs. 2, p < 0.001). No statistically significant difference was observed for MT-related adverse events. Multivariable logistic regression suggested that NIHSS at admission (adjusted odds ratio (aOR) = 1.28, 95% confidence interval (CI) = 1.10-1.48), mTICI 2b (aOR = 5.44, CI = 2.06-15.03), mTICI 2c (aOR = 10.81, CI = 3.65-34.07) and mTICI 3 (aOR = 11.56, CI = 4.49-31.10) as well as number of passes (aOR 0.76, CI = 0.66-0.88) were significantly associated with FO. No MT-related adverse events were observed for distal vessel occlusions. CONCLUSION: The FO in patients with mild stroke undergoing MT was associated with successful recanalization. No significant differences between patients with FO and UO were found for MT-related adverse events, suggesting that MT complications have no significant effects on the outcome of these patients. MT might improve the prognosis also in patients with mild stroke based on distal vessel occlusions without significantly increasing the risk of adverse events.

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke.

PURPOSE: Outcome prediction of large vessel occlusion of the anterior circulation in patients with wake-up stroke is important to identify patients that will benefit from thrombectomy. Currently, mismatch concepts that require MRI or CT-Perfusion (CTP) are recommended to identify these patients. We evaluated machine learning algorithms in their ability to discriminate between patients with favorable (defined as a modified Rankin Scale (mRS) score of 0-2) and unfavorable (mRS 3-6) outcome and between patients with poor (mRS5-6) and non-poor (mRS 0-4) outcome. METHODS: Data of 8395 patients that were treated between 2018 and 2020 from the nationwide registry of the German Society for Neuroradiology was retrospectively analyzed. Five models were trained with clinical variables and Alberta Stroke Program Early CT Score (ASPECTS). The model with the highest accuracy was validated with a test dataset with known stroke onset and with a test dataset that consisted only of wake-up strokes. RESULTS: 2419 patients showed poor and 3310 patients showed favorable outcome. The best performing Random Forest model achieved a sensitivity of 0.65, a specificity of 0.81 and an AUC of 0.79 on the test dataset of patients with wake-up stroke in the classification analysis between favorable and unfavorable outcome and a sensitivity of 0.42, a specificity of 0.83 and an AUC of 0.72 in the classification analysis between poor and non-poor outcome. CONCLUSION: Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.

Safety and Efficacy of the Novel Low-Profile APERIO Hybrid17 for a Treatment of Proximal and Distal Vessel Occlusion in Acute Ischemic Stroke: A Multi-Center Experience.

OBJECTIVE: To report our initial experience with the novel low-profile APERIO Hybrid17 Thrombectomy Device (AP17) for proximal and distal vessel occlusions in acute ischemic stroke. METHODS: A multicentric retrospective analysis of patients treated with the AP17 was performed. The primary effectiveness endpoint was first-pass TICI ≥2b (Thrombolysis in cerebral infarction scale). The primary safety endpoint was the occurrence of hemorrhagic complications. Further outcome measures were number of passes, device-related complications, and 3-month functional outcome. RESULTS: The AP17 was used in 71 patients (mean age: 73 years) with a median baseline National Institutes of Health Stroke Scale score of 9. Treated vessels were the carotid-T in 8 cases (11%), the M1-segment in 16 (23%), the M2-segment in 29 (41%), the anterior cerebral artery in 3 (4%), and basilar/posterior cerebral arteries in 15 (21%). The rates of first-pass and final TICI ≥2b were 75.6% and 92.7%, retrospectively, with a mean number of passes of 3 ± 2. Final TICI ≥2b rates were comparable between large and medium vessel occlusions. Symptomatic intracranial hemorrhages were recorded in 2 cases (2.8%). At 3-month clinical follow-up, a modified Rankin scale score ≤2 was achieved in 69.0% (29/42). The all-cause mortality at discharge was 17.4%. CONCLUSIONS: The AP17 was associated with a reasonable safety and efficacy profile for both proximal and distal vessel occlusions. These results may contribute to establish mechanical thrombectomy for distal occlusions.

Thin Slice Unenhanced Brain CT Can Detect Aneurysms Larger than 7 mm.

Purpose: To evaluate the detection rate of intracranial aneurysms on reconstructed thin slice non enhanced CT (NECT) scans. Methods: NECT scans from 34 patients with 35 aneurysms and 35 individuals without aneurysms were collected. Thin slice maximum intensity projections of the NECT scans were reconstructed. One observer evaluated the native images twice with a time interval of six month between both passes with respect to the prevalence and location of an aneurysm. The size and location of the aneurysms were evaluated in corresponding CT-Angiography and Time of flight datasets. A logit regression analysis was performed with size and location as dependent variables. 2 × 2 tables were constructed. The sensitivity and false negative rate were calculated for aneurysms with 0-6.9 mm, 7-9.9 mm and 10-20 mm and the under the curve (AUC) was calculated. Results: The overall detection rate of the aneurysms was 63% for the first pass and 66% for the second pass in the reconstructed NECT scans. The detection rate of aneurysms is size dependent. The sensitivity to detect aneurysms with a size of 0-6.9 mm was 0.09 and 0.03, for aneurysms with a size of 7-9.9. mm was 0.8 and 0.7 and for aneurysms with a size of 10-20 mm was 0.92 for both passes.The AUC was 0.77 for the first pass and 0.78 for the second pass. Conclusions: NECT scans can be used to detect a significant proportion of intracranial aneurysms larger than 7 mm if properly displayed and reconstructed. These patients should receive further vascular imaging to prevent future aneurysm related subarachnoid hemorrhage.

Imaging of clinically silent rectoprostatic hematoma in MRI guided in-bore prostate biopsy.

MR imaging provides awareness for rectoprostatic hematomas as a complication in prostate biopsy. We evaluated the frequency and size of clinically silent bleeding after in-bore MRI-guided prostate biopsy according to documentation in MRI. From 2007 until 2020 in-bore MRI-guided prostate biopsy was performed in 283 consecutive patients with suspected prostate cancer. Interventional image documentation was reviewed for rectoprostatic hematomas and rectal blood collections in this retrospective observational single-center study. Correlation to patient characteristics was analyzed using a multivariable logistic regression model. 283 consecutive patients with a mean age of 66 ± 8 years were included. We diagnosed bleeding complications in 41 (14.5%) of the patients. Significant rectoprostatic hematomas were found in 24 patients. Intra-rectal blood collections were observed in 16 patients and one patient showed bleeding in the urinary bladder. The volume of rectoprostatic hematomas was determined with a median of 7.5 ml (range 2-40 ml, IQR 11.25). We found no correlation between the presence of a rectoprostatic hematoma and malignant findings, patient position in biopsy, number of cores, age, prostate volume nor PSA density (p > 0.05). Rectoprostatic hematomas and rectal blood collections are rare complications after in-bore MR-guided prostate biopsy. MR imaging provides benefits not only for lesion detection in prostate biopsy but also for the control of bleeding complications, which can be overlooked in standard TRUS biopsy. Their significance in pain, erectile dysfunction, and urinary retention remains to be investigated.