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• A convolutional neural network (CNN) enables automated MRI sequence classification. • A small amount of training data is sufficient for high classification accuracy. • The model can classify sequence types of two vendors reliably. • CNNs could improve clinical workflows and facilitate data quality control. The ever-increasing volume of medical imaging data and interest in Big Data research brings challenges to data organization, categorization, and retrieval. Although the radiological value chain is almost entirely digital, data structuring has been widely performed pragmatically, but with insufficient naming and metadata standards for the stringent needs of image analysis. To enable automated data management independent of naming and metadata, this study focused on developing a convolutional neural network (CNN) that classifies medical images based solely on voxel data. A 3D CNN (3D-ResNet18) was trained using a dataset of 31,602 prostate MRI volumes with 10 different sequence types of 1243 patients. A five-fold cross-validation approach with patient-based splits was chosen for training and testing. Training was repeated with a gradual reduction in training data assessing classification accuracies to determine the minimum training data required for sufficient performance. The trained model and developed method were tested on three external datasets. The model achieved an overall accuracy of 99.88 % ± 0.13 % in classifying typical prostate MRI sequence types. When being trained with approximately 10 % of the original cohort (112 patients), the CNN still achieved an accuracy of 97.43 % ± 2.10 %. In external testing the model achieved sensitivities of > 90 % for 10/15 tested sequence types. The herein developed CNN enabled automatic and reliable sequence identification in prostate MRI. Ultimately, such CNN models for voxel-based sequence identification could substantially enhance the management of medical imaging data, improve workflow efficiency and data quality, and allow for robust clinical AI workflows. [ABSTRACT FROM AUTHOR]

Introduction: Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and contrast-enhanced ultrasound (CEUS) analyse tissue vascularization. We evaluated if CEUS can provide comparable information as DCE-MRI for the detection of prostate cancer (PCa) and prediction of its aggressiveness.Material and Methods: A post-hoc evaluation of 92 patients was performed. In each patient CEUS and DCE-MRI parameters of the most suspicious lesion identified on MRI were analysed. The predictive values for discrimination between benign lesions, low-/intermediate- and high-grade PCa were evaluated. Results of targeted biopsy served as reference standard (benign lesions, n=51; low- and intermediate-grade PCa [Gleason grade group 1 and 2], n=22; high-grade PCa [≥ Gleason grade group 3], n=19).Results: In peripheral zone lesions of all tested CEUS parameters only time to peak (TTPCEUS) showed significant differences between benign lesions and PCa (AUC 0.65). Of all tested DCE-MRI parameters, rate constant (Kep) was the best discriminator of high-grade PCa in the whole prostate (AUC 0.83) and in peripheral zone lesions (AUC 0.89).Conclusion: DCE-MRI showed a superior performance for detection of PCa and prediction of its aggressiveness. CEUS and DCE-MRI performed better in peripheral zone lesions than in transition zone lesions.Key Points: • DCE-MRI gathers information about vascularization and capillary permeability characteristics of tissues. • DCE-MRI can detect PCa and predict its aggressiveness. • CEUS also gathers information about vascularization of tissues. • For detection of PCa and prediction of aggressiveness DCE-MRI performed superiorly. • Both imaging techniques performed better in peripheral zone lesions. [ABSTRACT FROM AUTHOR]

Objective: To determine if tumor cell density and percentage of Gleason pattern within an outlined volumetric tumor region of interest (TROI) on whole-mount pathology (WMP) correlate with apparent diffusion coefficient (ADC) values on corresponding TROIs outlined on pre-operative MRI. Methods: Men with biopsy-proven prostate adenocarcinoma undergoing multiparametric MRI (mpMRI) prior to prostatectomy were consented to this prospective study. WMP and mpMRI images were viewed using 3D Slicer and each TROI from WMP was contoured on the high b-value ADC maps (b0, 1400). For each TROI outlined on WMP, TCD (tumor cell density) and the percentage of Gleason pattern 3, 4, and 5 were recorded. The ADC, ADC, ADC, and ADC were also calculated in each case from the ADC maps using 3D Slicer. Results: Nineteen patients with 21 tumors were included in this study. ADC values for TROIs were 944.8 ± 327.4 vs. 1329.9 ± 201.6 mm/s for adjacent non-neoplastic prostate tissue ( p < 0.001). ADC, ADC, and ADC values for higher grade tumors were lower than those of lower grade tumors ( mean 809.71 and 1176.34 mm/s, p = 0.014; 10th percentile 613.83 and 1018.14 mm/s, p = 0.009; ratio 0.60 and 0.94, p = 0.005). TCD and ADC ( ρ = −0.61, p = 0.005) and TCD and ADC ( ρ = −0.56, p = 0.01) were negatively correlated. No correlation was observed between percentage of Gleason pattern and ADC values. Conclusion: DWI MRI can characterize focal prostate cancer using ADC, ADC, and ADC, which correlate with pathological tumor cell density. [ABSTRACT FROM AUTHOR]

The routine use of dynamic-contrast-enhanced MRI (DCE-MRI) of the liver using hepatocyte-specific contrast agent (HSCA) as the standard of care for the study of focal liver lesions is not widely accepted and opponents invoke the risk of a loss in near 100% specificity of extracellular contrast agents (ECA) and the need for prospective head-to-head comparative studies evaluating the diagnostic performance of both contrast agents. The Purpose of this prospective intraindividual study was to conduct a quantitative and qualitative head-to-head comparison of DCE-MRI using HSCA and ECA in patients with liver cirrhosis and HCC. Twenty-three patients with liver cirrhosis and proven HCC underwent two 3 T-MR examinations, one with ECA (gadoteric acid) and the other with HSCA (gadoxetic acid). Signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), wash-in, wash-out, image quality, artifacts, lesion conspicuity, and major imaging features of LI-RADS v2018 were evaluated. Wash-in and wash-out were significantly stronger with ECA compared to HSCA (P < 0.001 and 0.006, respectively). During the late arterial phase (LAP), CNR was significantly lower with ECA (P = 0.005), while SNR did not differ significantly (P = 0.39). In qualitative analysis, ECA produced a better overall image quality during the portal venous phase (PVP) and delayed phase (DP) compared to HSCA (P = 0.041 and 0.008), showed less artifacts in the LAP and PVP (P = 0.003 and 0.034) and a higher lesion conspicuity in the LAP and PVP (P = 0.004 and 0.037). There was no significant difference in overall image quality during the LAP (P = 1), in artifacts and lesion conspicuity during the DP (P = 0.078 and 0.073) or in the frequency of the three major LI-RADS v2018 imaging features. In conclusion, ECA provides superior contrast of HCC—especially hypervascular HCC lesions—in DCE-MR in terms of better perceptibility of early enhancement and a stronger washout. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "Hepatocellular adenomas: is there additional value in using Gd‑EOB‑enhanced MRI for subtype diferentiation."

Objective: Federated Learning (FL) enables collaborative model training while keeping data locally. Currently, most FL studies in radiology are conducted in simulated environments due to numerous hurdles impeding its translation into practice. The few existing real-world FL initiatives rarely communicate specific measures taken to overcome these hurdles. To bridge this significant knowledge gap, we propose a comprehensive guide for real-world FL in radiology. Minding efforts to implement real-world FL, there is a lack of comprehensive assessments comparing FL to less complex alternatives in challenging real-world settings, which we address through extensive benchmarking., Materials and Methods: We developed our own FL infrastructure within the German Radiological Cooperative Network (RACOON) and demonstrated its functionality by training FL models on lung pathology segmentation tasks across six university hospitals. Insights gained while establishing our FL initiative and running the extensive benchmark experiments were compiled and categorized into the guide., Results: The proposed guide outlines essential steps, identified hurdles, and implemented solutions for establishing successful FL initiatives conducting real-world experiments. Our experimental results prove the practical relevance of our guide and show that FL outperforms less complex alternatives in all evaluation scenarios., Discussion and Conclusion: Our findings justify the efforts required to translate FL into real-world applications by demonstrating advantageous performance over alternative approaches. Additionally, they emphasize the importance of strategic organization, robust management of distributed data and infrastructure in real-world settings. With the proposed guide, we are aiming to aid future FL researchers in circumventing pitfalls and accelerating translation of FL into radiological applications., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Purpose: This observational study aims to provide a detailed clinical and imaging characterization/workup of acute intracerebral hemorrhage (ICH) due to either an underlying metastasis (mICH) or brain tumor (tICH) lesion., Methods: We conducted a retrospective, single-center study, evaluating patients presenting with occult ICH on initial CT imaging, classified as tICH or mICH on follow-up MRI imaging according to the H-Atomic classification. Demographic, clinical and radiological data were reviewed., Results: We included 116 patients (tICH: 20/116, 17.24%; mICH: 96/116, 82.76%). The most common malignancies causing ICH were lung cancer (27.59%), malignant melanoma (18.10%) and glioblastoma (10.34%). The three most common stroke-like symptoms observed were focal deficit (62/116, 53.45%), dizziness (42/116, 36.21%) and cognitive impairment (27/116, 23.28%). Highest mICH prevalence was seen in the occipital lobe (mICH: 28.13%, tICH: 0.00%; p = 0.004) with tICH more in the corpus callosum (tICH: 10.00%, mICH: 0.00%; p = 0.029). Anticoagulation therapy was only frequent in mICH patients (tICH: 0.00%, mICH: 5.21%; p = 0.586). Hemorrhage (tICH: 12682 mm 3 , mICH: 5708 mm 3 , p = 0.020) and edema volumes (tICH: 49389 mm 3 , mICH: 20972 mm 3 , p = 0.035) were significantly larger within tICH patients., Conclusion: More than half of the patients with neoplastic ICH exhibited stroke-like symptoms. Lung cancer was most common in mICH, glioblastoma in tICH. While clinical presentations were similar, significant differences in tumor location and treatments were discernible., (© 2024. The Author(s).)

Purpose To determine whether the unsupervised domain adaptation (UDA) method with generated images improves the performance of a supervised learning (SL) model for prostate cancer (PCa) detection using multisite biparametric (bp) MRI datasets. Materials and Methods This retrospective study included data from 5150 patients (14 191 samples) collected across nine different imaging centers. A novel UDA method using a unified generative model was developed for PCa detection using multisite bpMRI datasets. This method translates diffusion-weighted imaging (DWI) acquisitions, including apparent diffusion coefficient (ADC) and individual diffusion-weighted (DW) images acquired using various b values, to align with the style of images acquired using b values recommended by Prostate Imaging Reporting and Data System (PI-RADS) guidelines. The generated ADC and DW images replace the original images for PCa detection. An independent set of 1692 test cases (2393 samples) was used for evaluation. The area under the receiver operating characteristic curve (AUC) was used as the primary metric, and statistical analysis was performed via bootstrapping. Results For all test cases, the AUC values for baseline SL and UDA methods were 0.73 and 0.79 ( P < .001), respectively, for PCa lesions with PI-RADS score of 3 or greater and 0.77 and 0.80 ( P < .001) for lesions with PI-RADS scores of 4 or greater. In the 361 test cases under the most unfavorable image acquisition setting, the AUC values for baseline SL and UDA were 0.49 and 0.76 ( P < .001) for lesions with PI-RADS scores of 3 or greater and 0.50 and 0.77 ( P < .001) for lesions with PI-RADS scores of 4 or greater. Conclusion UDA with generated images improved the performance of SL methods in PCa lesion detection across multisite datasets with various b values, especially for images acquired with significant deviations from the PI-RADS-recommended DWI protocol (eg, with an extremely high b value). Keywords: Prostate Cancer Detection, Multisite, Unsupervised Domain Adaptation, Diffusion-weighted Imaging, b Value Supplemental material is available for this article. © RSNA, 2024.

Purpose:  The prevalent coronavirus disease 2019 (COVID-19) pandemic has spread throughout the world and is considered a serious threat to global health. The prognostic role of thoracic lymphadenopathy in COVID-19 is unclear. The aim of the present meta-analysis was to analyze the prognostic role of thoracic lymphadenopathy for the prediction of 30-day mortality in patients with COVID-19., Materials and Methods:  The MEDLINE library, Cochrane, and SCOPUS databases were screened for associations between CT-defined features and mortality in COVID-19 patients up to June 2021. In total, 21 studies were included in the present analysis. The quality of the included studies was assessed by the Newcastle-Ottawa Scale. The meta-analysis was performed using RevMan 5.3. Heterogeneity was calculated by means of the inconsistency index I2. DerSimonian and Laird random-effect models with inverse variance weights were performed without any further correction., Results:  The included studies comprised 4621 patients. The prevalence of thoracic lymphadenopathy varied between 1 % and 73.4 %. The pooled prevalence was 16.7 %, 95 % CI = (15.6 %; 17.8 %). The hospital mortality was higher in patients with thoracic lymphadenopathy (34.7 %) than in patients without (20.0 %). The pooled odds ratio for the influence of thoracic lymphadenopathy on mortality was 2.13 (95 % CI = [1.80-2.52], p < 0.001)., Conclusion:  The prevalence of thoracic lymphadenopathy in COVID-19 is 16.7 %. The presence of thoracic lymphadenopathy is associated with an approximately twofold increase in the risk for hospital mortality in COVID-19., Key Points:   · The prevalence of lymphadenopathy in COVID-19 is 16.7 %.. · Patients with lymphadenopathy in COVID-19 have a higher risk of mortality during hospitalization.. · Lymphadenopathy nearly doubles mortality and plays an important prognostic role.., Citation Format: · Bucher AM, Sieren M, Meinel F et al. Prevalence and prognostic role of thoracic lymphadenopathy in Covid-19. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2293-8132., Competing Interests: R. K.: Consulting fees from Boston Scientific, Bristol Myers Squibb, Guerbet, Roche, and Sirtex; payment for lectures from BTG, Eisai, Guerbet, Ipsen, Roche, Siemens, Sirtex, and MSD Sharp & Dohme; chair of the European Society of Radiology Audit and Standards Subcommittee.A.M.B.: Bayer, Guebert, Siemens Healthineers (Consulting fees and travel Support) T.P.: Fördermittel vom Berlin Institute of Health (Advanced Clinician Scientist Grant, Platform Grant), Bundesministerium für Bildung und Forschung (BMBF, 01KX2021 (RACOON), 01KX2121 (“NUM 2. 0” RACOON), 68GX21001A, 01ZZ2315D), Deutsche Forschungsgemeinschaft (DFG, SFB 1340/2), Europäische Union (H2020, CHAIMELEON: 952172, DIGITAL, EUCAIM:101100633) und berichtet über Forschungsvereinbarungen (keine persönlichen Zuwendungen, außerhalb der eingereichten Arbeit) mit AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Inc, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc, MedImmune, Merck Sharp & Dohme Corp, Millennium Pharmaceuticals, Inc, Morphotec Inc, NovoCure Ltd, PharmaMar S.A. und PharmaMar USA, Inc, Roche, Siemens Healthineers und TESARO Inc, sowie Honorare für eine Buchübersetzung (Elsevier B.V.)., (Thieme. All rights reserved.)

Standardized reporting of multiparametric prostate MRI (mpMRI) is widespread and follows international standards (Pi-RADS). However, quantitative measurements from mpMRI are not widely comparable. Although T2 mapping sequences can provide repeatable quantitative image measurements and extract reliable imaging biomarkers from mpMRI, they are often time-consuming. We therefore investigated the value of quantitative measurements on a highly accelerated T2 mapping sequence, in order to establish a threshold to differentiate benign from malignant lesions. For this purpose, we evaluated a novel, highly accelerated T2 mapping research sequence that enables high-resolution image acquisition with short acquisition times in everyday clinical practice. In this retrospective single-center study, we included 54 patients with clinically indicated MRI of the prostate and biopsy-confirmed carcinoma (n = 37) or exclusion of carcinoma (n = 17). All patients had received a standard of care biopsy of the prostate, results of which were used to confirm or exclude presence of malignant lesions. We used the linear mixed-effects model-fit by REML to determine the difference between mean values of cancerous tissue and healthy tissue. We found good differentiation between malignant lesions and normal appearing tissue in the peripheral zone based on the mean T2 value. Specifically, the mean T2 value for tissue without malignant lesions was (151.7 ms [95% CI: 146.9-156.5 ms] compared to 80.9 ms for malignant lesions [95% CI: 67.9-79.1 ms]; p < 0.001). Based on this assessment, a limit of 109.2 ms is suggested. Aditionally, a significant correlation was observed between T2 values of the peripheral zone and PI-RADS scores (p = 0.0194). However, no correlation was found between the Gleason Score and the T2 relaxation time. Using REML, we found a difference of -82.7 ms in mean values between cancerous tissue and healthy tissue. We established a cut-off-value of 109.2 ms to accurately differentiate between malignant and non-malignant prostate regions. The addition of T2 mapping sequences to routine imaging could benefit automated lesion detection and facilitate contrast-free multiparametric MRI of the prostate., (© 2024. The Author(s).)

Rationale and Objectives: The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting., Materials and Methods: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed., Results: Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes., Conclusion: The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Background and Objective: Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. Although approved computer-aided diagnosis (CAD) tools may assist urologists in this task, evidence of improved clinically significant prostate cancer (csPCa) detection is lacking. Therefore, we aimed to document the diagnostic utility of using Prostate Imaging Reporting and Data System (PI-RADS) and CAD for biopsy planning compared with PI-RADS alone., Methods: A total of 262 consecutive men scheduled for TPB at our referral centre were analysed. Reported PI-RADS lesions and an US Food and Drug Administration-cleared CAD tool were used for TPB planning. PI-RADS and CAD lesions were targeted on TPB, while four (interquartile range: 2-5) systematic biopsies were taken. The outcomes were the (1) proportion of csPCa (grade group ≥2) and (2) number of targeted lesions and false-positive rate. Performance was tested using free-response receiver operating characteristic curves and the exact Fisher-Yates test., Key Findings and Limitations: Overall, csPCa was detected in 56% (146/262) of men, with sensitivity of 92% and 97% (p = 0.007) for PI-RADS- and CAD-directed TPB, respectively. In 4% (10/262), csPCa was detected solely by CAD-directed biopsies; in 8% (22/262), additional csPCa lesions were detected. However, the number of targeted lesions increased by 54% (518 vs 336) and the false-positive rate doubled (0.66 vs 1.39; p = 0.009). Limitations include biopsies only for men at clinical/radiological suspicion and no multidisciplinary review of MRI before biopsy., Conclusions and Clinical Implications: The tested CAD tool for TPB planning improves csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences., Patient Summary: The computer-aided diagnosis tool tested for transperineal prostate biopsy planning improves the detection of clinically significant prostate cancer at the cost of an increased number of lesions sampled and false positives. This may enable more personalised biopsy planning depending on urological and patient preferences., (Copyright © 2024 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Background: Magnetic resonance imaging (MRI) is used to detect the prostate index lesion before targeted biopsy. However, the number of biopsy cores that should be obtained from the index lesion is unclear. The aim of this study is to analyze how many MRI-targeted biopsy cores are needed to establish the most relevant histopathologic diagnosis of the index lesion and to build a prediction model., Methods: We retrospectively included 451 patients who underwent 10-core systematic prostate biopsy and MRI-targeted biopsy with sampling of at least three cores from the index lesion. A total of 1587 biopsy cores were analyzed. The core sampling sequence was recorded, and the first biopsy core detecting the most relevant histopathologic diagnosis was identified. In a subgroup of 261 patients in whom exactly three MRI-targeted biopsy cores were obtained from the index lesion, we generated a prediction model. A nonparametric Bayes classifier was trained using the PI-RADS score, prostate-specific antigen (PSA) density, lesion size, zone, and location as covariates., Results: The most relevant histopathologic diagnosis of the index lesion was detected by the first biopsy core in 331 cases (73%), by the second in 66 cases (15%), and by the third in 39 cases (9%), by the fourth in 13 cases (3%), and by the fifth in two cases (<1%). The Bayes classifier correctly predicted which biopsy core yielded the most relevant histopathologic diagnosis in 79% of the subjects. PI-RADS score, PSA density, lesion size, zone, and location did not independently influence the prediction model., Conclusion: The most relevant histopathologic diagnosis of the index lesion was made on the basis of three MRI-targeted biopsy cores in 97% of patients. Our classifier can help in predicting the first MRI-targeted biopsy core revealing the most relevant histopathologic diagnosis; however, at least three MRI-targeted biopsy cores should be obtained regardless of the preinterventionally assessed covariates., (© 2022. The Author(s).)

Data-driven machine learning in medical research and diagnostics needs large-scale datasets curated by clinical experts. The generation of large datasets can be challenging in terms of resource consumption and time effort, while generalizability and validation of the developed models significantly benefit from variety in data sources. Training algorithms on smaller decentralized datasets through federated learning can reduce effort, but require the implementation of a specific and ambitious infrastructure to share data, algorithms and computing time. Additionally, it offers the opportunity of maintaining and keeping the data locally. Thus, data safety issues can be avoided because patient data must not be shared. Machine learning models are trained on local data by sharing the model and through an established network. In addition to commercial applications, there are also numerous academic and customized implementations of network infrastructures available. The configuration of these networks primarily differs, yet adheres to a standard framework composed of fundamental components. In this technical note, we propose basic infrastructure requirements for data governance, data science workflows, and local node set-up, and report on the advantages and experienced pitfalls in implementing the local infrastructure with the German Radiological Cooperative Network initiative as the use case example. We show how the infrastructure can be built upon some base components to reflect the needs of a federated learning network and how they can be implemented considering both local and global network requirements. After analyzing the deployment process in different settings and scenarios, we recommend integrating the local node into an existing clinical IT infrastructure. This approach offers benefits in terms of maintenance and deployment effort compared to external integration in a separate environment (e.g., the radiology department). This proposed groundwork can be taken as an exemplary development guideline for future applications of federated learning networks in clinical and scientific environments.

Background: In patients with non-small cell lung cancer (NSCLC), accuracy of [ 18 F]FDG-PET/CT for pretherapeutic lymph node (LN) staging is limited by false positive findings. Our aim was to evaluate machine learning with routinely obtainable variables to improve accuracy over standard visual image assessment., Methods: Monocentric retrospective analysis of pretherapeutic [ 18 F]FDG-PET/CT in 491 consecutive patients with NSCLC using an analog PET/CT scanner (training + test cohort, n = 385) or digital scanner (validation, n = 106). Forty clinical variables, tumor characteristics, and image variables (e.g., primary tumor and LN SUVmax and size) were collected. Different combinations of machine learning methods for feature selection and classification of N0/1 vs. N2/3 disease were compared. Ten-fold nested cross-validation was used to derive the mean area under the ROC curve of the ten test folds ("test AUC") and AUC in the validation cohort. Reference standard was the final N stage from interdisciplinary consensus (histological results for N2/3 LNs in 96%)., Results: N2/3 disease was present in 190 patients (39%; training + test, 37%; validation, 46%; p = 0.09). A gradient boosting classifier (GBM) with 10 features was selected as the final model based on test AUC of 0.91 (95% confidence interval, 0.87-0.94). Validation AUC was 0.94 (0.89-0.98). At a target sensitivity of approx. 90%, test/validation accuracy of the GBM was 0.78/0.87. This was significantly higher than the accuracy based on "mediastinal LN uptake > mediastinum" (0.7/0.75; each p < 0.05) or combined PET/CT criteria (PET positive and/or LN short axis diameter > 10 mm; 0.68/0.75; each p < 0.001). Harmonization of PET images between the two scanners affected SUVmax and visual assessment of the LNs but did not diminish the AUC of the GBM., Conclusions: A machine learning model based on routinely available variables from [ 18 F]FDG-PET/CT improved accuracy in mediastinal LN staging compared to established visual assessment criteria. A web application implementing this model was made available., (© 2023. The Author(s).)

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging Reporting and Data System (PI-RADS) features for classification justification. Materials and Methods This retrospective study included consecutive patients with histopathologic analysis-proven prostatic lesions who underwent biparametric MRI and biopsy between January 2012 and December 2017. After image annotation by two radiologists, a deep learning model was trained to detect the index lesion; classify PCa, clinically significant PCa (Gleason score ≥ 7), and benign lesions (eg, prostatitis); and justify classifications using PI-RADS features. Lesion- and patient-based performance were assessed using fivefold cross validation and areas under the receiver operating characteristic curve. Clinical feasibility was tested in a multireader study and by using the external PROSTATEx data set. Statistical evaluation of the multireader study included Mann-Whitney U and exact Fisher-Yates test. Results Overall, 1224 men (median age, 67 years; IQR, 62-73 years) had 3260 prostatic lesions (372 lesions with Gleason score of 6; 743 lesions with Gleason score of ≥ 7; 2145 benign lesions). XAI reliably detected clinically significant PCa in internal (area under the receiver operating characteristic curve, 0.89) and external test sets (area under the receiver operating characteristic curve, 0.87) with a sensitivity of 93% (95% CI: 87, 98) and an average of one false-positive finding per patient. Accuracy of the visual and textual explanations of XAI classifications was 80% (1080 of 1352), confirmed by experts. XAI-assisted readings improved the confidence (4.1 vs 3.4 on a five-point Likert scale; P = .007) of nonexperts in assessing PI-RADS 3 lesions, reducing reading time by 58 seconds ( P = .009). Conclusion The explainable AI model reliably detected and classified clinically significant prostate cancer and improved the confidence and reading time of nonexperts while providing visual and textual explanations using well-established imaging features. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Chapiro in this issue.

Background: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency., Methods: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant., Results: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUC patient : 0.89 vs. 0.86; AUC lesion : 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (OR rectal susceptibility artifact  = 5.46; OR diameter, = 1.12; OR ADC  = 0.998; all P < .001) and false negatives (OR rectal susceptibility artifact  = 3.31; OR diameter  = 0.82; OR ADC  = 1.007; all P ≤ .03) of DL-CAD., Conclusions: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD., Trial Registration: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021., (© 2023. The Author(s).)

Background: Noncontrast Computed Tomography (NCCT) features are promising markers for acute hematoma expansion (HE) in patients with intracerebral hemorrhage (ICH). It remains unclear whether accurate identification of these markers is also reliable in raters with different levels of experience., Methods: Patients with acute spontaneous ICH admitted at four tertiary centers in Germany and Italy were retrospectively included from January 2017 to June 2020. In total, nine NCCT markers were rated by one radiology resident, one radiology fellow, and one neuroradiology fellow with different levels experience in ICH imaging. Interrater reliabilities of the resident and radiology fellow were evaluated by calculated Cohen's kappa (κ) statistics in reference to the neuroradiology fellow who was referred as the gold standard. Gold-standard ratings were evaluated by calculated interrater κ statistics. Global interrater reliabilities were evaluated by calculated Fleiss kappa statistics across all three readers. A comparison of receiver operating characteristics (ROCs) was used to evaluate differences in the diagnostic accuracy for predicting acute hematoma expansion (HE) among the raters., Results: Substantial-to-almost-perfect interrater concordance was found for the resident with interrater Cohen's kappa from 0.70 (95% CI 0.65-0.81) to 0.96 (95% CI 0.94-0.98). The interrater Cohen's kappa for the radiology fellow was moderate to almost perfect and ranged from 0.58 (95% CI 0.52-0.65) to 94 (95% CI 92-0.97). The intrarater gold-standard Cohen's kappa was almost perfect and ranged from 0.79 (95% CI 0.78-0.90) to 0.98 (95% CI 0.78-0.90). The global interrater Fleiss kappa ranged from 0.62 (95%CI 0.57-0.66) to 0.93 (95%CI 0.89-0.97). The diagnostic accuracy for the prediction of acute hematoma expansion (HE) was different for the island sign and fluid sign, with p -values < 0.05., Conclusion: The NCCT markers had a substantial-to-almost-perfect interrater agreement among raters with different levels of experience. Differences in the diagnostic accuracy for the prediction of acute HE were found in two out of nine NCCT markers. The study highlights the promising utility of NCCT markers for acute HE prediction.

Background: We evaluated the role of radiomics applied to contrast-enhanced computed tomography (CT) in the detection of lymph node (LN) metastases in patients with known lung cancer compared to 18 F-fluorodeoxyglucose positron emission tomography (PET)/CT as a reference., Methods: This retrospective analysis included 381 patients with 1,799 lymph nodes (450 malignant, 1,349 negative). The data set was divided into a training and validation set. A radiomics analysis with 4 filters and 6 algorithms resulting in 24 different radiomics signatures and a bootstrap algorithm (Bagging) with 30 bootstrap iterations was performed. A decision curve analysis was applied to generate a net benefit to compare the radiomics signature to two expert radiologists as one-by-one and as a prescreening tool in combination with the respective radiologist and only the radiologists., Results: All 24 modeling methods showed good and reliable discrimination for malignant/benign LNs (area under the curve 0.75-0.87). The decision curve analysis showed a net benefit for the least absolute shrinkage and selection operator (LASSO) classifier for the entire probability range and outperformed the expert radiologists except for the high probability range. Using the radiomics signature as a prescreening tool for the radiologists did not improve net benefit., Conclusions: Radiomics showed good discrimination power irrespective of the modeling technique in detecting LN metastases in patients with known lung cancer. The LASSO classifier was a suitable diagnostic tool and even outperformed the expert radiologists, except for high probabilities. Radiomics failed to improve clinical benefit as a prescreening tool., (© 2022. The Author(s) under exclusive licence to European Society of Radiology.)

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Purpose: To analyze possible differences in the inter-reader variability between PI-RADS version 2 (v2) and version 2.1 (v2.1) for the classification of prostate lesions using multiparametric MRI (mpMRI) of the prostate., Methods: In this retrospective and randomized study, 239 annotated and histopathologically correlated prostate lesions (104 positive and 135 negative for prostate cancer) were rated twice by three experienced uroradiologists using PI-RADS v2 and v2.1 with an interval of at least two months between readings. Results were tabulated across readers and reading timepoints and inter-reader variability was determined using Fleiss' kappa (κ). Thereafter, an additional analysis of the data was performed in which PI-RADS scores 1 and 2 were combined, as they have the same clinical consequences., Results: PI-PI-RADS v2.1 showed better inter-reader agreement in the peripheral zone (PZ), but poorer inter-reader agreement in the transition zone (TZ) (PZ: κ = 0.63 vs. κ = 0.58; TZ: κ = 0.47 vs. κ = 0.57). When PI-RADS scores 1 and 2 were combined, the use of PI-RADS v2.1 resulted in almost perfect inter-reader agreement in the PZ and substantial agreement in the TZ (PZ: κ = 0.81; TZ: κ = 0.80)., Conclusion: PI-RADS v2.1 improves inter-reader agreement in the PZ. New differences in inter-reader agreement were mainly the result of the assignment of PI-RADS v2.1 scores 1 and 2 to lesions in the TZ. Combining scores 1 and 2 improved inter-reader agreement both in the TZ and in the PZ, indicating that refined definitions may be warranted for these PI-RADS scores., Key Points: · PI-RADSv2.1 improves inter-reader agreement in the PZ but not in the TZ.. · New differences derived from PI-RADSv2.1 scores 1 and 2 in the TZ.. · Combined PI-RADSv2.1 scores of 1 and 2 yielded better inter-reader agreement.. · PI-RADSv2.1 appears to provide more precise description of lesions in the PZ.. · Improved inter-reader agreement in the PZ stresses the importance of appropriate lexicon description.., Citation Format: · Beetz N, Haas M, Baur A et al. Inter-Reader Variability Using PI-RADS v2 Versus PI-RADS v2.1: Most New Disagreement Stems from Scores 1 and 2. Fortschr Röntgenstr 2022; 194: 852 - 861., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Introduction: Prospective and longitudinal data on pulmonary injury over one year after acute coronavirus disease 2019 (COVID-19) are sparse. We aim to determine reductions in pulmonary function and respiratory related quality of life up to 12 months after acute COVID-19., Methods: Patients with acute COVID-19 were enrolled into an ongoing single-centre, prospective observational study and prospectively examined 6 weeks, 3, 6 and 12 months after onset of COVID-19 symptoms. Chest CT-scans, pulmonary function and symptoms assessed by St. Georges Respiratory Questionnaire were used to evaluate respiratory limitations. Patients were stratified according to severity of acute COVID-19., Results: Median age of all patients was 57 years, 37.8% were female. Higher age, male sex and higher BMI were associated with acute-COVID-19 severity (p < 0.0001, 0.001 and 0.004 respectively). Also, pulmonary restriction and reduced carbon monoxide diffusion capacity was associated with disease severity. In patients with restriction and impaired diffusion capacity, FVC improved over 12 months from 61.32 to 71.82, TLC from 68.92 to 76.95, D LCO from 60.18 to 68.98 and K CO from 81.28 to 87.80 (percent predicted values; p = 0.002, 0.045, 0.0002 and 0.0005). The CT-score of lung involvement in the acute phase was associated with restriction and reduction in diffusion capacity in follow-up. Respiratory symptoms improved for patients in higher severity groups during follow-up, but not for patients with initially mild disease., Conclusion: Severity of respiratory failure during COVID-19 correlates with the degree of pulmonary function impairment and respiratory quality of life in the year after acute infection., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Objectives: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement., Materials and Methods: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used., Results: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]., Conclusion: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement., Key Points: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.., Citation Format: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82., Competing Interests: The authors declare that they have no conflict of interest., (Thieme. All rights reserved.)

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. KEY POINTS: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined., (© 2021. The Author(s).)

Competing Interests: Dr. Penzkofer reports research agreements (no personal payments, outside of submitted work) with AGO, Aprea AB, ARCAGY-GINECO, Astellas Pharma Global Inc. (APGD), Astra Zeneca, Clovis Oncology, Inc., Dohme Corp, Holaira, Incyte Corporation, Karyopharm, Lion Biotechnologies, Inc., MedImmune, Merck Sharp, Millennium Pharmaceuticals, Inc., Morphotec Inc., NovoCure Ltd., PharmaMar S.A. and PharmaMar USA, Inc., Roche, Siemens Healthineers, and TESARO Inc. Dr. Rogasch declared that he has no conflict of interest.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint Lesion TM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

Purpose:  To evaluate the diagnostic performance and reliability of non-enhanced imaging characterization quotients (NICQs) derived from magnetic resonance imaging (MRI) in the differential diagnosis of pulmonary nodules in hematological patients., Materials and Methods:  A total of 83 lesions in 45 consecutive hematological patients were analyzed (10 bacterial pneumonias, 16 fungal pneumonias, 19 pulmonary lymphoma manifestations). The MRI protocol included T2-weighted single-shot fast spin echo (FSE) and T1-weighted gradient echo (GRE) sequences. T2-based T2-NICQ mean and T2-NICQ 90th were calculated from signal intensities measured in the lesion, muscle, and fat ((SI Lesion - SI Muscle )/(SI Fat - SI Muscle ) * 100), and simple T1-based T1-Q mean from signal intensities of the lesion and muscle (SI Lesion /SI Muscle ). Images were read by one radiologist with > 7 years and one with 1 year of experience. For statistical evaluation the Kruskal-Wallis or Mann-Whitney U-test, receiver operating characteristic (ROC) analysis with calculation of areas under the curve (AUC), and intraclass correlation coefficients (ICCs) were used., Results:  Medians of T2-NICQs differed significantly when comparing infectious lesions and lymphoma manifestations in general (T2-NICQ mean 20.33 vs. 10.14; T2-NICQ 90th 34.96 vs. 25.52) or fungal lesions and lymphoma manifestations in particular (T2-NICQ mean 19.00 vs. 10.14; T2-NICQ 90th 34.49 vs. 25.25). The AUCs for T2-NICQs on the per-patient level ranged from 0.73 to 0.79. ICCs were at least > 0.85, except for intrarater testing of T2-NICQ 90th (0.79)., Conclusion:  The overall diagnostic performance of T2-NICQs is adequate for differentiating infectious and fungal lesions from lymphoma manifestations. The results show good to excellent intra- and interrater agreement. We therefore consider NICQs helpful in the diagnostic workup of pulmonary nodules in hematological patients., Key Points:   · Non-enhanced Imaging Characterization Quotients provide a fast and pragmatic approach for assessing pulmonary lesions in hematological patients.. · The diagnostic performance of Non-enhanced Imaging Characterization Quotients is adequate for differentiating infectious and fungal infiltrates from lymphoma manifestations.. · Non-enhanced Imaging Characterization Quotients show good to excellent intra- and interrater agreement.., Citation Format: · Nagel SN, Kim D, Wylutzki T et al. Diagnostic Performance and Reliability of Non-Enhanced Imaging Characterization Quotients for the Differentiation of Infectious and Malignant Pulmonary Nodules in Hematological Patients Using 3T MRI. Fortschr Röntgenstr 2020; 192: 327 - 334., Competing Interests: The authors declare that they have no conflict of interest., (© Georg Thieme Verlag KG Stuttgart · New York.)

Objectives: Our study sought to reevaluate the safety and diagnostic efficacy of iobitridol as a nonionic contrast medium after almost 20 years of use., Materials and Methods: This noninterventional postmarketing surveillance noncontrolled, multicenter (168 centers in Germany) study enrolled 94,960 patients receiving intravenous or intra-arterial iobitridol. The majority of the adjusted population (n = 92,550, 98.2%) underwent either computed tomography examination (n = 46,502, 49.3%) or intravenous urography (n = 46,048, 48.8%). A standardized questionnaire was used to ascertain patient's information, known risk factors, renal function status, premedication, type of examination, injection of contrast agent, imaging quality, diagnostic value, and safety., Results: A total of 469 patients (0.49%) experienced an adverse event (AE), and 24 patients (0.025%) reported a serious AE (SAE). All patients recovered and no fatal event occurred. The prevalence of AE was significantly higher in patients with at least one risk factor, with premedication, with a history of AE, in female and patients younger than 60 years old (P < 0.05). Presence of at least one risk factor is the only predictive factor for the prevalence of SAE (P = 0.042). In patients with a history of AE, premedication did not significantly lower the AE rate (P = 0.737). No statistically significant difference in the prevalence of AE between the different combination of cortisone and/or antihistamines as pretreatment was found., Conclusions: Iobitridol is a safe contrast medium with a high tolerability and efficacy. Presence of risk factors such as cardiovascular diseases, allergies, or asthma was the only significant predictive factor for an AE and an SAE. Premedication did not significantly lower the occurrence of an AE and an SAE.

Clinical/methodical Issue: In view of the diagnostic complexity and the large number of examinations, modern radiology is challenged to identify clinically significant prostate cancer (PCa) with high sensitivity and specificity. Meanwhile overdiagnosis and overtreatment of clinically nonsignificant carcinomas need to be avoided., Standard Radiological Methods: Increasingly, international guidelines recommend multiparametric magnetic resonance imaging (mpMRI) as first-line investigation in patients with suspected PCa., Methodical Innovations: Image interpretation according to the PI-RADS criteria is limited by interobserver variability. Thus, rapid developments in the field of automated image analysis tools, including radiomics and artificial intelligence (AI; machine learning, deep learning), give hope for further improvement in patient care., Performance: AI focuses on the automated detection and classification of PCa, but it also attempts to stratify tumor aggressiveness according to the Gleason score. Recent studies present good to very good results in radiomics or AI-supported mpMRI diagnosis. Nevertheless, these systems are not widely used in clinical practice., Achievements and Practical Recommendations: In order to apply these innovative technologies, a growing awareness for the need of structured data acquisition, development of robust systems and an increased acceptance of AI as diagnostic support are needed. If AI overcomes these obstacles, it may play a key role in the quantitative and reproducible image-based diagnosis of ever-increasing prostate MRI examination volumes.

Aims: To evaluate the feasibility, safety and efficacy of renal sympathetic denervation via endoluminal transaortic periarterial ethanol injection., Methods and Results: In 11 normotensive pigs transaortic puncture was performed with a 90-cm 21G needle with subsequent unilateral ethanol injection to the periarterial space. Needle placement was achieved using a 7F steerable sheath fluoroscopically positioned slightly above the renal artery origin. Blood pressure measurements and abdominal CT scans were performed immediately pre- and postintervention and 4 weeks later. After euthanasia norepinephrine concentration of both kidneys (RTNEC) was determined and renal arteries and surrounding tissues histologically examined to assess induced nerve fibre degeneration., Results: All but one procedure were technically successful. One major complication with accidental ethanol injection into the renal artery and subsequent infarction occurred. One pig died from no intervention-related cardiac arrest. The 4-week follow-up was uneventful in the remaining 10 animals. RTNEC was significantly lower on the treated side in eight of ten pigs (mean decrease 36.6%) with correlating histopathological signs of nerve degeneration., Conclusions: Renal sympathicolysis by transaortic periarterial ethanol injection was feasible and effective in a porcine model. This approach may be an alternative to catheter-based RFA or other methods of renal denervation.

Purpose: The second version of the PI-RADS™ (Prostate Imaging Reporting and Data System) was introduced in 2015 to standardize the interpretation and reporting of prostate multiparametric magnetic resonance imaging. Recently low cancer detection rates were reported for PI-RADS version 2 category 4 lesions. Therefore the aim of the study was to evaluate the cancer detection rate of PI-RADS version 2 in a large prospective cohort., Materials and Methods: The study included 704 consecutive men with primary or prior negative biopsies who underwent magnetic resonance imaging/ultrasound fusion guided targeted biopsy and 10-core systematic prostate biopsy between September 2015 and May 2017. All lesions were rated according to PI-RADS version 2 and lesions with PI-RADS version 2 category 3 or greater were biopsied. An ISUP (International Society of Urological Pathology) score of 2 or greater (ie Gleason 3 + 4 or greater) was defined as clinically significant prostate cancer., Results: The overall cancer detection rate of PI-RADS version 2 categories 3, 4 and 5 was 39%, 72% and 91% for all prostate cancer, and 23%, 49% and 77% for all clinically significant prostate cancer, respectively. If only targeted biopsy had been performed, 59 clinically significant tumors (16%) would have been missed. The PI-RADS version 2 score was significantly associated with the presence of prostate cancer (p <0.001), the presence of clinically significant prostate cancer (p <0.001) and the ISUP grade (p <0.001)., Conclusions: PI-RADS version 2 is significantly associated with the presence of clinically significant prostate cancer. The cancer detection rate of PI-RADS version 2 category 4 lesions was considerably higher than previously reported. When performing targeted biopsy, the combination with systematic biopsy still provides the highest detection of clinically significant prostate cancer., (Copyright © 2018 American Urological Association Education and Research, Inc. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: Particularly for patients with heart arrhythmias, conventional BestSystole (BS) and BestDiastole (BD) reconstruction techniques in computed tomography (CT) frequently show artifacts that hinder the readability of the coronary tree. To address this problem, this paper presents an alternative reconstruction method that combines the technique "reconstructions with identical filling" (RIF) with motion mapping: This new technique is called "RIF in motion mapping" (RIMM). This study compares the diagnostic quality of images generated with RIMM to that of the other reconstruction techniques., Materials and Methods: Having shown major artifacts in standard reconstructions, the CT datasets of 23 patients with suspected coronary artery disease or prior to transcatheter aortic valve replacement were selected manually. Each dataset was evaluated with four reconstruction techniques: BS, BD, RIF, and RIMM. Two radiologists, blinded to the applied reconstruction type, then evaluated the entire coronary tree of each sample using the 15-segment American Heart Association model and the six-grade Likert scale., Results: Of the 345 analyzed coronary segments, the RIMM technique showed a significant number of images with reliable diagnostic quality (n = 228, 66%) as compared to RIF (P = 0.002) and BS/BD reconstructions (P < 0.001). Per coronary segment, vessel, and patient, the RIMM technique scored significantly better than the conventional BS/BD reconstructions (P = 0.003) and better than the RIF reconstructions with regard to the right coronary artery (P = 0.041)., Conclusions: This new technique works: Using RIMM on the worst CT images substantially erased many of these artifacts, thereby enabling the radiologists to clearly visualize these segments. As RIMM considerably eliminates artifacts, this new CT reconstruction technique can help make a fast reliable evaluation of a patient's coronary tree. Thus, this enhanced visualization of cardiac images by RIMM avoids the need for further invasive diagnostic procedures., (Copyright © 2017. Published by Elsevier Inc.)

Renal microvascular rarefaction characterizes chronic kidney disease (CKD). In murine models of CKD, micro-CT imaging reflected capillary rarefaction using quantification of renal relative blood volume (rBV). In addition, micro-CT imaging revealed morphological alterations of the intrarenal vasculature including reduced vascular branching and lumen diameter. Here, we retrospectively quantified rBV in contrast-enhanced CT angiography in patients and found that, compared to non-CKD patients, those with CKD and renal fibrosis had significantly reduced rBV in the renal cortex. rBV values closely mirrored capillary rarefaction in the corresponding nephrectomy specimens. In patients with follow-up CT angiography, reduction of renal function was paralleled by a decline in rBV. Using virtual autopsy, i.e., postmortem CT angiography, morphometry of intrarenal arteries in 3D-rendered CT images revealed significantly reduced arterial diameter and branching in CKD compared to non-CKD cases. In conclusion, in CKD patients, contrast-enhanced CT imaging with quantification of rBV correlates with functional renal vasculature, whereas virtual autopsy allows morphometric analyses of macrovascular changes. Importantly, the observed vascular alterations in CKD patients mirror those in animals with progressive CKD, suggesting a high relevance of animal models for studying vascular alterations in CKD and renal fibrosis.

Accurate sampling of cancer suspicious locations is critical in targeted prostate biopsy, but can be complicated by the motion of the prostate. We present an open-source software for intra-procedural tracking of the prostate and biopsy targets using deformable image registration. The software is implemented in 3D Slicer and is intended for clinical users. We evaluated accuracy, computation time and sensitivity to initialization, and compared implementations that use different versions of the Insight Segmentation Toolkit (ITK). Our retrospective evaluation used data from 25 in-bore MRI-guided prostate biopsy cases (343 registrations total). Prostate Dice similarity coefficient improved on average by 0.17 (p < 0.0001, range 0.02-0.48). Registration was not sensitive to operator variability. Computation time decreased significantly for the implementation using the latest version of ITK. In conclusion, we presented a fully functional open-source tool that is ready for prospective evaluation during clinical MRI-guided prostate biopsy interventions.

Rationale and Objectives: To investigate the correlation between prevalence and degree of coronary artery calcification (CAC) and extracoronary calcifications (ECCs), scored quantitatively according to Agatston and semiquantitatively by visual analysis, in chest computed tomography (CT) studies obtained for lung cancer screening in asymptomatic subjects and in patients with known coronary heart disease (CHD), and to compare the association of ECC and CAC to established cardiovascular risk factors., Materials and Methods: Prospective study on 501 males (67 ± 8 years) with a history of working dust exposure who underwent nongated low-dose chest CT for lung cancer screening. Of these, 63 (12.6%) had a history of CHD, the remaining 438 subjects (87.4%) were clinically asymptomatic and without a history of CHD. On the day of the CT study, subjects underwent a thorough clinical examination including blood tests and completed a standardized questionnaire to establish a complete medical history. ECC and CAC scores were quantified according to Agatston and, in addition, by visual rating of calcium load of individual vessel territories on a five-point scale from "absent" to "extensive." Results were correlated with the respective subjects' cardiovascular risk factors and with the presence or absence of CHD., Results: ECC scores correlated significantly with CAC scores (two-sided Spearman 0.515; P < .001). ECC scores were associated significantly (P < .001) with cardiovascular risk factors (smoking history, hypertension, diabetes, and hypercholesterolemia) and with subjects' Framingham/prospective cardiovascular münster study scores, whereas CAC scores were associated only with the presence of hypercholesterolemia. CAC scores were strongly associated with CHD than ECC scores (area under the curve, 0.88 vs. 0.66 at receiver operating characteristic analysis). Visual scoring of ECC/CAC load correlated closely with the respective Agatston values (P < .001) and revealed the same association (or lack thereof) with cardiovascular risk factors/CHD., Conclusions: In nongated low-dose CT for lung cancer screening, CAC and ECC load can be accurately established by visual analysis. ECC and CAC scores correlate closely, but not perfectly. There is a strong association between established cardiovascular risk factors and ECC load, but not CAC load, providing further evidence that ECC scoring may complement CAC scoring for broader risk assessment, for example, regarding prediction of extracoronary vascular events., (Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.)

Objectives: To evaluate the performance of T2 mapping in discriminating prostate cancer from normal prostate tissue in the peripheral zone using a practical reduced field-of-view MRI sequence requiring less than 3 minutes of scan time., Materials and Methods: Thirty-six patients with biopsy-proven peripheral zone prostate cancer without prior treatment underwent routine multiparametric MRI at 3.0T with an endorectal coil. An Inner-Volume Carr-Purcell-Meiboom-Gill imaging sequence that required 2.8 minutes to obtain data for quantitative T2 mapping covering the entire prostate gland was added to the routine multiparametric protocol. Suspected cancer (SC) and suspected healthy (SH) tissue in the peripheral zone were identified in consensus by three radiologists and were correlated with available biopsy results. Differences in mean T2 values in SC and SH regions-of-interest (ROIs) were tested for significance using unpaired Student's two-tailed t-test. The area under the receiver operating characteristic curve was used to assess the optimal threshold T2 value for cancer discrimination., Results: ROI analyses revealed significantly (p<0.0001) shorter T2 values in SC (85.4±12.3ms) compared to SH (169.6±38.7ms). An estimated T2 threshold of 99ms yielded a sensitivity of 92% and a specificity of 97% for prostate cancer discrimination., Conclusions: Quantitative values derived from this clinically practical T2-mapping sequence allow high precision discrimination between healthy and cancerous peripheral zone in the prostate., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Purpose: Percutaneous punctures are often performed under computed tomography (CT) guidance using a freehand method. Especially in challenging cases, initial accuracy of the needle placement is highly dependent on the radiologist's experience. Thus, a miniature lightweight guidance device was developed which is capable of assisting a radiologist during the needle placement process., Methods: The device utilizes an accelerometer to measure the needle's tilt by calculating a set of orientation angles. This set can be matched with the coordinate system of the CT imaging software during a simple alignment process. After that, the needle's orientation can be expressed in terms of projected angles in the axial and sagittal planes. The accuracy of the device was evaluated in a phantom study, and initial clinical trials were carried out performing facet joint punctures in a swine cadaver., Results: The sensor was embedded in a cube with dimensions of [Formula: see text] and a total weight of about 11 g which can be attached to the puncture needle at its rear end or handgrip. A graphical user interface (GUÌ) has been created offering visual real-time orientation guidance. Results of the phantom experiments showed differences between planned target and performed puncture angles of [Formula: see text] for in-plane and [Formula: see text] for out-of-plane punctures., Conclusion: The results of the phantom and ex vivo study suggest that the device is useful to assist a radiologist in CT-guided percutaneous punctures and helps navigating the needle with high precision.

Rationale and Objectives: Development of imaging biomarkers often relies on their correlation with histopathology. Our aim was to compare two approaches for correlating pathology to multiparametric magnetic resonance (MR) imaging (mpMRI) for localization and quantitative assessment of prostate cancer (PCa) index tumor using whole mount (WM) pathology (WMP) as the reference., Materials and Methods: Patients (N = 30) underwent mpMRI that included diffusion-weighted imaging and dynamic contrast-enhanced (DCE) MRI at 3 T before radical prostatectomy (RP). RP specimens were processed using WM technique (WMP) and findings summarized in a standard surgical pathology report (SPR). Histology index tumor volumes (HTVs) were compared to MR tumor volumes (MRTVs) using two approaches for index lesion identification on mpMRI using annotated WMP slides as the reference (WMP) and using routine SPR as the reference. Consistency of index tumor localization, tumor volume, and mean values of the derived quantitative parameters (mean apparent diffusion coefficient [ADC], K(trans), and ve) were compared., Results: Index lesions from 16 of 30 patients met the selection criteria. There was WMP/SRP agreement in index tumor in 13 of 16 patients. ADC-based MRTVs were larger (P < .05) than DCE-based MRTVs. ADC MRTVs were smaller than HTV (P < .005). There was a strong correlation between HTV and MRTV (Pearson ρ > 0.8; P < .05). No significant differences were observed in the mean values of K(trans) and ADC between the WMP and SPR., Conclusions: WMP correlation is superior to SPR for accurate localization of all index lesions. The use of WMP is however not required to distinguish significant differences of mean values of quantitative MRI parameters within tumor volume., (Copyright © 2015 AUR. Published by Elsevier Inc. All rights reserved.)

Purpose: Devices constantly tracking the blood pressure (BP) of hypertensive patients are highly desired to facilitate effective patient management and to reduce hospitalization. We report on experiences gathered in a pilot study that was designed to evaluate the prototype of a newly developed, minimally invasive implantable sensor system for long-term BP monitoring., Methods: The device was implanted in the femoral artery (FA) of 12 sheep via standard FA catheterization under fluoroscopic control. Accuracy of the recorded blood pressure was determined by comparison with a reference catheter, which was positioned in the contralateral FA immediately after implantation. Regular follow-up included angiography, computed tomography (CT), and control of functionality and position of the BP sensor. Animals were euthanized after 6 months. FA segments with in situ pressure sensor underwent macroscopic and histopathologic examinations., Results: All implantations of the novel sensor device in the FA were successful and uneventful. High-quality BP recordings were documented. Bland-Altman plots indicate very good agreement. Comparison with measurements taken from the reference sensor revealed mean differences and standard deviations of -0.56 ± 0.85, 0.29 ± 1.44, and 0.85 ± 2.27 mmHg (diastolic, systolic, and pulse pressure, respectively) after exclusion of one outlier. CT uncovered deficiencies in cable stability that were addressed in a redesign. No thrombus formation, necrosis, or apoptosis were detected., Conclusions: The pilot study proved the technical feasibility of wireless BP measurement in the FA via a novel miniature sensor device.

Postmortem computed tomography (PMCT) is a modern tool that complements autopsy diagnostics. In clinical autopsies, a major cause of death is cardiovascular disease. To improve the performance of PMCT in cardiovascular disease, full body angiography was developed (PMCT angiography [PMCTA]). Twenty PMCTA scans generated before autopsy were compared with native PMCT and clinical autopsy. The objective of the study was to quantify the additional diagnostic value of adding angiography to native imaging and to compare PMCT and PMCTA findings to autopsy findings. The diagnosis of the cause of death was identical or overlapped in 80% of the cases that used PMCTA and 70% that used PMCT. The additional diagnostic yield given by PMCT and PMCTA in combination with autopsy was 55%. PMCT yielded additional diagnoses in the musculoskeletal system. The greatest additional diagnostic value of PMCTA was in association with cardiovascular diagnoses. The accuracy of PMCTA for cardiac causes of death was 80%, and the positive predictive value was 90%. The findings indicate that native PMCT cannot display the cardiovascular system sufficiently clearly for high-quality diagnostic assessment. However, PMCTA is a powerful tool in autopsy cases with a history of cardiovascular disease and/or a suspected cardiovascular cause of death. The combination of PMCTA and clinical autopsy enhances diagnostic quality and completeness of the autopsy report. Furthermore, in cases without consent or with a restricted consent for clinical autopsy, PMCTA has the potential to provide information on cardiovascular causes of death., (Copyright © 2014 Elsevier Inc. All rights reserved.)

This work presents concepts for complex endovascular procedures using electromagnetic navigation technology (EMT). Navigation software interfacing a standard commercially available navigation system was developed, featuring registration, electromagnetic field distortion correction, breathing motion detection and gating, and state-of-the-art 3D imaging post processing. Protocols for endovascularly placed, in-situ fenestrated abdominal aortic stent grafts and an EMT guided transjugular intrahepatic portosystemic shunt (TIPSS) creation have been designed. A dedicated set of interventional devices was developed for each of the procedures: For aortic in-situ fenestration a combination of high-porosity stentgrafts, steerable catheters and electromagnetically navigated guidewires was used, for TIPSS a dual-navigated (sheath and stylet) TIPSS-device was designed and manufactured. The developed devices underwent phantom testing, in preparation for animal experiments to prove the feasibility of the approach. Once established, these systems could aid in performing these challenging interventional radiology procedures, exploiting the unique characteristics of electromagnetic navigation and solving multiple of the problems associated with these interventions being performed under X-ray fluoroscopy, such as lacking real-time 3D information or extensive exposure to ionizing radiation.