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Purpose: To quantitatively and qualitatively evaluate and compare the performance of leading large language models (LLMs), including proprietary models (GPT-4, GPT-3.5 Turbo, Claude-3-Opus, and Gemini Ultra) and open-source models (Mistral-7b and Mistral-8×7b), in simplifying 109 interventional radiology reports., Methods: Qualitative performance was assessed using a five-point Likert scale for accuracy, completeness, clarity, clinical relevance, naturalness, and error rates, including trust-breaking and post-therapy misconduct errors. Quantitative readability was assessed using Flesch Reading Ease (FRE), Flesch-Kincaid Grade Level (FKGL), SMOG Index, and Dale-Chall Readability Score (DCRS). Paired t-tests and Bonferroni-corrected p-values were used for statistical analysis., Results: Qualitative evaluation showed no significant differences between GPT-4 and Claude-3-Opus for any metrics evaluated (all Bonferroni-corrected p-values: p = 1), while they outperformed other assessed models across five qualitative metrics (p < 0.001). GPT-4 had the fewest content and trust-breaking errors, with Claude-3-Opus second. However, all models exhibited some level of trust-breaking and post-therapy misconduct errors, with GPT-4-Turbo and GPT-3.5-Turbo with few-shot prompting showing the lowest error rates, and Mistral-7B and Mistral-8×7B showing the highest. Quantitatively, GPT-4 surpassed Claude-3-Opus in all readability metrics (all p < 0.001), with a median FRE score of 69.01 (IQR: 64.88-73.14) versus 59.74 (IQR: 55.47-64.01) for Claude-3-Opus. GPT-4 also outperformed GPT-3.5-Turbo and Gemini Ultra (both p < 0.001). Inter-rater reliability was strong (κ = 0.77-0.84)., Conclusions: GPT-4 and Claude-3-Opus demonstrated superior performance in generating simplified IR reports, but the presence of errors across all models, including trust-breaking errors, highlights the need for further refinement and validation before clinical implementation., Clinical Relevance/applications: With the increasing complexity of interventional radiology (IR) procedures and the growing availability of electronic health records, simplifying IR reports is critical to improving patient understanding and clinical decision-making. This study provides insights into the performance of various LLMs in rewriting IR reports, which can help in selecting the most suitable model for clinical patient-centered applications., 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.)

Rationale and Objectives: The purpose of this study was to evaluate the diagnostic utility of iterative metal artifact reduction (iMAR) in computed tomography (CT)-imaging of oral and oropharyngeal cancers when obscured by dental hardware artifacts and to determine the most appropriate iMAR settings for this purpose., Materials and Methods: The study retrospectively enrolled 27 patients (8 female, 19 male; mean age 64±12.7years) with histologically confirmed oral or oropharyngeal cancer obscured by dental artifacts in contrast-enhanced CT. Raw CT data were reconstructed with ascending iMAR strengths (levels 1/2/3/4/5) and one reconstruction without iMAR (level 0). For subjective analysis, two blinded radiologists rated tumor visualization and artifact severity on a five-point Likert scale. For objective analysis, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and artifact index (AI) were determined., Results: iMAR reconstructions improved the subjective image quality of tumor edge and contrast, and the objective parameters of tumor SNR and CNR, reaching their optimum at iMAR levels 4 and 5 (P<.001). AI decreased with iMAR reconstructions reaching its minimum at iMAR level 5 (P<.001). Tumor detection rates increased 2.4-fold with iMAR 5, 2.1-fold with iMAR 4, and 1.9-fold with iMAR 3 compared to reconstructions without iMAR. Disadvantages such as algorithm-induced artifacts increased significantly with higher iMAR strengths (P<.05), reaching a maximum with iMAR 5., Conclusion: iMAR significantly improves CT imaging of oral and oropharyngeal cancers, as confirmed by both subjective and objective measures, with best results at highest iMAR strengths., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Matthias Stefan May reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Wolfgang Wuest reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Jan-Peter Roth reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Michael Uder reports a relationship with Siemens Healthcare that includes: speaking and lecture fees. Christian Hofmann reports a relationship with Siemens Healthcare that includes: employment. Bernhard Schmidt reports a relationship with Siemens Healthcare that includes: employment., (Copyright © 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

The COVID-19 pandemic posed challenges to governments in terms of contact tracing. Like many other countries, Germany introduced a mobile-phone-based digital contact tracing solution ("Corona Warn App"; CWA) in June 2020. At the time of its release, however, it was hard to assess how effective such a solution would be, and a political and societal debate arose regarding its efficiency, also in light of its high costs. This study aimed to analyze the effectiveness of the CWA, considering prevented infections, hospitalizations, intensive care treatments, and deaths. In addition, its efficiency was to be assessed from a monetary point of view, and factors with a significant influence on the effectiveness and efficiency of the CWA were to be determined. Mathematical and statistical modeling was used to calculate infection cases prevented by the CWA, along with the numbers of prevented complications (hospitalizations, intensive care treatments, deaths) using publicly available CWA download numbers and incidences over time. The monetized benefits of these prevented cases were quantified and offset against the costs incurred. Sensitivity analysis was used to identify factors critically influencing these parameters. Between June 2020 and April 2022, the CWA prevented 1.41 million infections, 17,200 hospitalizations, 4600 intensive care treatments, and 7200 deaths. After offsetting costs and benefits, the CWA had a net present value of EUR 765 m in April 2022. Both the effectiveness and efficiency of the CWA are decisively and disproportionately positively influenced by the highest possible adoption rate among the population and a high rate of positive infection test results shared via the CWA.

The growth of primary tumors and metastases is associated with excess body fat. In bone metastasis formation, the bone marrow microenvironment, and particularly adipocytes, play a pivotal role as growth mediators of disseminated tumor cells in the bone marrow. The aim of the present study is to non-invasively characterize the pathophysiologic processes in experimental bone metastasis resulting from accelerated tumor progression within adipocyte-rich bone marrow using multimodal imaging from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT). To achieve this, we have employed small animal models after the administration of MDA-MB 231 breast cancer and B16F10 melanoma cells into the bone of nude rats or C57BL/6 mice, respectively. After tumor cell inoculation, ultra-high field MRI and µPET/CT were used to assess functional and metabolic parameters in the bone marrow of control animals (normal diet, ND), following a high-fat diet (HFD), and/or treated with the peroxisome proliferator-activated receptor-gamma (PPARγ) antagonist bisphenol-A-diglycidylether (BADGE), respectively. In the bone marrow of nude rats, dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI), as well as [18F]fluorodeoxyglucose-PET/CT([18F]FDG-PET/CT), was performed 10, 20, and 30 days after tumor cell inoculation, followed by immunohistochemistry. DCE-MRI parameters associated with blood volume, such as area under the curve (AUC), were significantly increased in bone metastases in the HFD group 30 days after tumor cell inoculation as compared to controls (p < 0.05), while the DWI parameter apparent diffusion coefficient (ADC) was not significantly different between the groups. [18F]FDG-PET/CT showed an enhanced glucose metabolism due to increased standardized uptake value (SUV) at day 30 after tumor cell inoculation in animals that received HFD (p < 0.05). BADGE treatment resulted in the inversion of quantitative DCE-MRI and [18F]FDG-PET/CT data, namely a significant decrease in AUC and SUV in HFD-fed animals as compared to ND-fed controls (p < 0.05). Finally, immunohistochemistry and qPCR confirmed the HFD-induced stimulation in vascularization and glucose activity in murine bone metastases. In conclusion, multimodal and multiparametric MRI and [18F]FDG-PET/CT were able to derive quantitative parameters in bone metastases, revealing an increase in vascularization and glucose metabolism following HFD. Thus, non-invasive imaging may serve as a biomarker for assessing the pathophysiology of bone metastasis in obesity, opening novel options for therapy and treatment monitoring by MRI and [18F]FDG-PET/CT.

Objectives: To assess the predictive value of multiparametric MRI for treatment response evaluation of induction chemo-immunotherapy in locally advanced head and neck squamous cell carcinoma., Methods: Twenty-two patients with locally advanced, histologically confirmed head and neck squamous cell carcinoma who were enrolled in the prospective multicenter phase II CheckRad-CD8 trial were included in the current analysis. In this unplanned secondary single-center analysis, all patients who received contrast-enhanced MRI at baseline and in week 4 after single-cycle induction therapy with cisplatin/docetaxel combined with the immune checkpoint inhibitors tremelimumab and durvalumab were included. In week 4, endoscopy with representative re-biopsy was performed to assess tumor response. All lesions were segmented in the baseline and restaging multiparametric MRI, including the primary tumor and lymph node metastases. The volume of interest of the respective lesions was volumetrically measured, and time-resolved mean intensities of the golden-angle radial sparse parallel-volume-interpolated gradient-echo perfusion (GRASP-VIBE) sequence were extracted. Additional quantitative parameters including the T1 ratio, short-TI inversion recovery ratio, apparent diffusion coefficient, and dynamic contrast-enhanced (DCE) values were measured. A model based on parallel random forests incorporating the MRI parameters from the baseline MRI was used to predict tumor response to therapy. Receiver operating characteristic (ROC) curves were used to evaluate the prognostic performance., Results: Fifteen patients (68.2%) showed pathologic complete response in the re-biopsy, while seven patients had a residual tumor (31.8%). In all patients, the MRI-based primary tumor volume was significantly lower after treatment. The baseline DCE parameters of time to peak and wash-out were significantly different between the pathologic complete response group and the residual tumor group (p < 0.05). The developed model, based on parallel random forests and DCE parameters, was able to predict therapy response with a sensitivity of 78.7% (95% CI 71.24-84.93) and a specificity of 78.6% (95% CI 67.13-87.48). The model had an area under the ROC curve of 0.866 (95% CI 0.819-0.914)., Conclusions: DCE parameters indicated treatment response at follow-up, and a random forest machine learning algorithm based on DCE parameters was able to predict treatment response to induction chemo-immunotherapy., Competing Interests: MH reports the following conflicts of interest: Merck Serono (advisory role, speakers’ bureau, honoraria, travel expenses, research funding); MSD (advisory role, speakers’ bureau, travel expenses, research funding); AstraZeneca (research funding); Novartis (research funding); BMS (advisory role, honoraria, speakers’ bureau); and Teva (travel expenses). ME reports the following conflicts of interest: Diaceutics (employment, honoraria, advisory role, speakers’ bureau, travel expenses); Cepheid (research funding, advisory role); AstraZeneca (honoraria, advisory role, speakers’ bureau, travel expenses); Roche (honoraria, travel expenses); MSD (honoraria, speakers’ bureau); GenomicHealth (honoraria, advisory role, speakers bureau, travel expenses); Astellas (honoraria, speakers’ bureau); Janssen-Cilag (honoraria, advisory role, research funding, travel expenses); and Stratifyer (research funding, patents).). UG received support for presentation activities for Dr Sennewald Medizintechnik GmbH, has received support for investigator initiated clinical studies (IITs) from MSD and AstraZeneca and contributed at Advisory Boards Meetings of AstraZeneca and Bristol-Myers Squibb. SS reports the following conflicts of interest: stockholder of Siemens Healthineers. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hellwig, Ellmann, Eckstein, Wiesmueller, Rutzner, Semrau, Frey, Gaipl, Gostian, Hartmann, Iro, Fietkau, Uder, Hecht and Bäuerle.)

Background: The coronavirus disease 2019 (COVID-19) pandemic led to far-reaching restrictions of social and professional life, affecting societies all over the world. To contain the virus, medical schools had to restructure their curriculum by switching to online learning. However, only few medical schools had implemented such novel learning concepts. We aimed to evaluate students' attitudes to online learning to provide a broad scientific basis to guide future development of medical education., Methods: Overall, 3286 medical students from 12 different countries participated in this cross-sectional, web-based study investigating various aspects of online learning in medical education. On a 7-point Likert scale, participants rated the online learning situation during the pandemic at their medical schools, technical and social aspects, and the current and future role of online learning in medical education., Results: The majority of medical schools managed the rapid switch to online learning (78%) and most students were satisfied with the quantity (67%) and quality (62%) of the courses. Online learning provided greater flexibility (84%) and led to unchanged or even higher attendance of courses (70%). Possible downsides included motivational problems (42%), insufficient possibilities for interaction with fellow students (67%) and thus the risk of social isolation (64%). The vast majority felt comfortable using the software solutions (80%). Most were convinced that medical education lags behind current capabilities regarding online learning (78%) and estimated the proportion of online learning before the pandemic at only 14%. In order to improve the current curriculum, they wish for a more balanced ratio with at least 40% of online teaching compared to on-site teaching., Conclusion: This study demonstrates the positive attitude of medical students towards online learning. Furthermore, it reveals a considerable discrepancy between what students demand and what the curriculum offers. Thus, the COVID-19 pandemic might be the long-awaited catalyst for a new "online era" in medical education., Competing Interests: The authors have declared that no competing interests exist.

Objectives: Due to its high sensitivity, DCE MRI of the breast (bMRI) is increasingly used for both screening and assessment purposes. The high number of detected lesions poses a significant logistic challenge in clinical practice. The aim was to evaluate a temporally and spatially resolved (4D) radiomics approach to distinguish benign from malignant enhancing breast lesions and thereby avoid unnecessary biopsies., Methods: This retrospective study included consecutive patients with MRI-suspicious findings (BI-RADS 4/5). Two blinded readers analyzed DCE images using a commercially available software, automatically extracting BI-RADS curve types and pharmacokinetic enhancement features. After principal component analysis (PCA), a neural network-derived A.I. classifier to discriminate benign from malignant lesions was constructed and tested using a random split simple approach. The rate of avoidable biopsies was evaluated at exploratory cutoffs (C 1 , 100%, and C 2 , ≥ 95% sensitivity)., Results: Four hundred seventy (295 malignant) lesions in 329 female patients (mean age 55.1 years, range 18-85 years) were examined. Eighty-six DCE features were extracted based on automated volumetric lesion analysis. Five independent component features were extracted using PCA. The A.I. classifier achieved a significant (p < .001) accuracy to distinguish benign from malignant lesion within the test sample (AUC: 83.5%; 95% CI: 76.8-89.0%). Applying identified cutoffs on testing data not included in training dataset showed the potential to lower the number of unnecessary biopsies of benign lesions by 14.5% (C 1 ) and 36.2% (C 2 )., Conclusion: The investigated automated 4D radiomics approach resulted in an accurate A.I. classifier able to distinguish between benign and malignant lesions. Its application could have avoided unnecessary biopsies., Key Points: • Principal component analysis of the extracted volumetric and temporally resolved (4D) DCE markers favored pharmacokinetic modeling derived features. • An A.I. classifier based on 86 extracted DCE features achieved a good to excellent diagnostic performance as measured by the area under the ROC curve with 80.6% (training dataset) and 83.5% (testing dataset). • Testing the resulting A.I. classifier showed the potential to lower the number of unnecessary biopsies of benign breast lesions by up to 36.2%, p < .001 at the cost of up to 4.5% (n = 4) false negative low-risk cancers., (© 2021. The Author(s).)

For therapeutic decisions regarding uni- or biventricular surgical repair in congenital heart disease (CHD), left ventricular mass (LVM) is an important factor. The aim of this retrospective study was to determine the LVM of infants with CHD in thoracic computed tomography angiographies (CTAs) and to evaluate its usefulness as a prognostic parameter, with special attention paid to hypoplastic left heart (HLH) patients. Manual segmentation of the left ventricular endo- and epicardial volumes was performed in CTAs of 132 infants. LVMs were determined from these volumes and normalized to body surface area. LVMs of patients with different types of CHD were compared to each other using analyses of variances (ANOVA). An LVM cutoff for discrimination between uni- and biventricular repair was determined using receiver operating characteristics. Survival rates were calculated using Kaplan-Meier statistics. Patients with a clinical diagnosis of an HLH had significantly lower mean LVM (21.88 g/m 2 ) compared to patients without applicable disease (50.22 g/m 2 ; p < 0.0001) and compared to other CHDs, including persistent truncus arteriosus, left ventricular outflow tract obstruction, transposition of the great arteries, pulmonary artery stenosis or atresia, and double-outlet right ventricle (all, p < 0.05). The LVM cutoff for uni- vs. biventricular surgery was 33.9 g/m 2 (sensitivity: 82.3%; specificity: 73.7%; PPV: 94.9%). In a subanalysis of HLH patients, a sensitivity of 50.0%, specificity of 100%, PPV of 100%, and NPV of 83.3% was determined. Patient survival was not significantly different between the surgical approaches or between patients with LVM above or below the cutoff. LVM can be measured in chest CTA of newborns with CHD and can be used as a prognostic factor.

Background and Purpose: The high diagnostic value of DWI for cholesteatoma diagnostics is undisputed. This study compares the diagnostic value of readout-segmented echo-planar DWI and single-shot TSE DWI for cholesteatoma diagnostics., Materials and Methods: Thirty patients with newly suspected cholesteatoma were examined with a dedicated protocol, including readout-segmented echo-planar DWI and single-shot TSE DWI at 1.5T. Acquisition parameters of both diffusion-weighted sequences were as follows: b =1000 s/mm, 2 axial and coronal section orientations, and section thickness of 3 mm. Image quality was evaluated by 2 readers on a 5-point Likert scale with respect to lesion conspicuity, the presence of susceptibility artifacts mimicking cholesteatomas, and overall subjective image quality. Sensitivity and specificity were calculated using histology results as the gold standard., Results: Twenty-five cases of histologically confirmed cholesteatomas were included in the study group. Lesion conspicuity was higher and fewer artifacts were found when using TSE DWI (both P  < .001). The overall subjective image quality, however, was better with readout-segmented DWI. For TSE DWI, the sensitivity for readers 1 and 2 was 92% (95% CI, 74%-99%) and 88% (95% CI, 69%-97%), respectively, while the specificity for both readers was 80% (95% CI, 28%-99%). For readout-segmented DWI, the sensitivity for readers 1 and 2 was 76% (95% CI, 55%-91%) and 68% (95% CI, 46%-85%), while the specificity for both readers was 60% (95% CI, 15%-95%)., Conclusions: The use of TSE DWI is advisable for cholesteatoma diagnostics and preferable over readout-segmented DWI., (© 2021 by American Journal of Neuroradiology.)

Background: The imaging of bone metastases, which is regularly performed by cross-sectional modalities, is clinically vital when characterizing and staging osseous lesions. In this paper, we aimed to establish a novel methodology using experimental ultrasound (US) techniques to assess the morphological, functional, and molecular features of breast cancer bone metastases in an animal model, compared with magnetic resonance imaging (MRI) and histological analysis., Materials and Methods: Nude rats were implanted intra-arterially with MDA-MB-231 breast cancer cells to induce osteolytic metastasis in their right hind legs. Once tumors had developed, an experimental US technique using automatic 3D scanning and MRI were performed. For assessment of perfusion, functional imaging techniques included contrast-enhanced US (CEUS) and dynamic contrast-enhanced MRI (DCE-MRI). For molecular ultrasound, anti-VEGFR2 conjugated microbubbles were applied and correlated with immunostaining for VEGFR2 expression., Results: 3D US enabled the automatic assessment of osteolytic lesions, including the largest tumor diameters along the x-, y- and z-axes as well as the segmented tumor volumes, without significant differences between US and MRI (p > 0.18). The CEUS and DCE-MRI of osseous lesions showed corresponding results for the parameters peak enhancement, wash-in area under the curve (both, r > 0.5) and wash-in perfusion index (r > 0.3) when differentiating between tumor, necrotic tissue and healthy muscle tissue (all, p < 0.01). Finally, molecular US allowed the non-invasive assessment of increased VEGFR2 expression in skeletal lesions compared with surrounding muscle tissue (p = 0.03), while a control antibody could not discriminate between these tissues (p = 0.44)-a factor which was confirmed by histological analysis., Conclusion: To the best of our knowledge, this is the first report on an imaging protocol for breast cancer bone metastasis using an experimental US scanner. Therefore, we present a novel methodology to characterize these osseous lesions on the morphological, functional, and molecular level in correlation with MRI and histological analysis., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Integration of medical imaging into preclinical anatomy courses is already underway in many medical schools. However, interpretation of two-dimensional grayscale images is difficult and conventional volume rendering techniques provide only images of limited quality. In this regard, a more photorealistic visualization provided by Cinematic Rendering (CR) may be more suitable for anatomical education. A randomized, two-period crossover study was conducted from July to December 2018, at the University Hospital of Erlangen, Germany to compare CR and conventional computed tomography (CT) imaging for speed and comprehension of anatomy. Sixteen students were randomized into two assessment sequences. During each assessment period, participants had to answer 15 anatomy-related questions that were divided into three categories: parenchymal, musculoskeletal, and vascular anatomy. After a washout period of 14 days, assessments were crossed over to the respective second reconstruction technique. The mean interperiod differences for the time to answer differed significantly between the CR-CT sequence (-204.21 ± 156.0 seconds) and the CT-CR sequence (243.33 ± 113.83 seconds; P < 0.001). Overall time reduction by CR was 65.56%. Cinematic Rendering visualization of musculoskeletal and vascular anatomy was higher rated compared to CT visualization (P < 0.001 and P = 0.003), whereas CT visualization of parenchymal anatomy received a higher scoring than CR visualization (P < 0.001). No carryover effects were observed. A questionnaire revealed that students consider CR to be beneficial for medical education. These results suggest that CR has a potential to enhance knowledge acquisition and transfer from medical imaging data in medical education., (© 2020 The Authors. Anatomical Sciences Education published by Wiley Periodicals LLC on behalf of American Association of Anatomists.)

Background: Accurate staging of cervical lymph nodes (LN) is pivotal for further clinical management of patients with head and neck cancer. Functional magnetic resonance imaging (MRI) such as three-dimensional (3D) dynamic contrast-enhanced (DCE) acquisition might improve the diagnosis of cervical LN metastases., Purpose: To evaluate the additional diagnostic value of high-resolution 3D T1-weighted DCE in detecting LN metastasis compared to standard morphological imaging criteria in patients with head and neck tumors as correlated to histopathology., Material and Methods: Standard MRI with 3D DCE acquisition at voxel sizes of 1 × 1×1 mm was performed in 15 patients before surgery; 92 LN of the head and neck were histopathologically analyzed. A logistic regression analysis of semi-quantitative DCE parameters, time-intensity curve (TIC) shapes, and morphological criteria was performed to differentiate benign from malignant LN., Results: Standard MRI was sufficient for diagnosis of malignancy in LN with a short-axis diameter ≥ 15 mm (n = 17). For LN metastases with a short-axis diameter <15 mm (n = 12), however, the combination of 3D DCE MRI parameters, TIC shapes, and LN diameter significantly increased the sensitivity and specificity of diagnosing metastases (DCE + TIC shape + LN diameter: 92% and 88% vs. DCE only: 83% and 68% ( P  < 0.01) vs. LN diameter only: 83% and 77% ( P  = 0.04)., Conclusion: MRI including isotropic high-resolution 3D DCE acquisition combined with morphological criteria allows an accurate assessment of small cervical LN metastases in patients with head and neck cancer. For LN ≥ 15 mm diameter, morphologic imaging may suffice to diagnose metastatic disease to the LN., (© The Foundation Acta Radiologica 2020.)

Computer-aided diagnosis (CADx) approaches could help to objectify reporting on prostate mpMRI, but their use in many cases is hampered due to common-built algorithms that are not publicly available. The aim of this study was to develop an open-access CADx algorithm with high accuracy for classification of suspicious lesions in mpMRI of the prostate. This retrospective study was approved by the local ethics commission, with waiver of informed consent. A total of 124 patients with 195 reported lesions were included. All patients received mpMRI of the prostate between 2014 and 2017, and transrectal ultrasound (TRUS)-guided and targeted biopsy within a time period of 30 days. Histopathology of the biopsy cores served as a standard of reference. Acquired imaging parameters included the size of the lesion, signal intensity (T2w images), diffusion restriction, prostate volume, and several dynamic parameters along with the clinical parameters patient age and serum PSA level. Inter-reader agreement of the imaging parameters was assessed by calculating intraclass correlation coefficients. The dataset was stratified into a train set and test set (156 and 39 lesions in 100 and 24 patients, respectively). Using the above parameters, a CADx based on an Extreme Gradient Boosting algorithm was developed on the train set, and tested on the test set. Performance optimization was focused on maximizing the area under the Receiver Operating Characteristic curve (ROC AUC ). The algorithm was made publicly available on the internet. The CADx reached an ROC AUC of 0.908 during training, and 0.913 during testing ( p = 0.93). Additionally, established rule-in and rule-out criteria allowed classifying 35.8% of the malignant and 49.4% of the benign lesions with error rates of <2%. All imaging parameters featured excellent inter-reader agreement. This study presents an open-access CADx for classification of suspicious lesions in mpMRI of the prostate with high accuracy. Applying the provided rule-in and rule-out criteria might facilitate to further stratify the management of patients at risk.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accuracy exceeding its constituents. This protocol describes the development of an ML algorithm to predict the growth of breast cancer bone macrometastases in a rat model before any abnormalities are observable with standard imaging methods. Such an algorithm can facilitate the detection of early metastatic disease (i.e., micrometastasis) that is regularly missed during staging examinations. The applied metastasis model is site-specific, meaning that the rats develop metastases exclusively in their right hind leg. The model's tumor-take rate is 60%-80%, with macrometastases becoming visible in magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a subset of animals 30 days after induction, whereas a second subset of animals exhibit no tumor growth. Starting from image examinations acquired at an earlier time point, this protocol describes the extraction of features that indicate tissue vascularization detected by MRI, glucose metabolism by PET/CT, and the subsequent determination of the most relevant features for the prediction of macrometastatic disease. These features are then fed into a model-averaged neural network (avNNet) to classify the animals into one of two groups: one that will develop metastases and the other that will not develop any tumors. The protocol also describes the calculation of standard diagnostic parameters, such as overall accuracy, sensitivity, specificity, negative/positive predictive values, likelihood ratios, and the development of a receiver operating characteristic. An advantage of the proposed protocol is its flexibility, as it can be easily adapted to train a plethora of different ML algorithms with adjustable combinations of an unlimited number of features. Moreover, it can be used to analyze different problems in oncology, infection, and inflammation.

Purpose:  Good training is the basis for high job satisfaction and high-quality patient care in radiology. The aim of this survey was to record the current state of working conditions for residents in radiology training in Germany and to focus on the aspects of training and psychosocial workload. The description of the actual state should help to identify possible problem areas and to develop improvement approaches., Materials and Methods:  At the beginning of 2018, we sent an electronic questionnaire to the German Roentgen Society (DRG), the German Association of Chairmen in Academic Radiology (KLR), the Chief Physician Forum of the DRG (CAFRAD) and the Forum of Registered Radiologists (FUNRAD) with the request to forward it to radiology residents. With 63 questions, the questionnaire covered seven essential areas of medical working and training conditions. In order to ensure interdisciplinary comparability, most questions were identical to previous surveys among residents of other disciplines., Results:  643 residents started the survey. 501 (78 %) questionnaires were fully processed and included in the final analysis. 65 % of respondents were satisfied with their current job situation. At the same time, shortcomings, especially with regard to the reconciliation of family and work as well as scientific and clinical work, became clear. Only 36 % of participants with children were satisfied with the compatibility of family and work at their workplace. Only 31 % of the researchers were satisfied with their research conditions. In addition, residents experienced a high psychosocial workload., Conclusion:  Job satisfaction is high among radiology residents in direct comparison to other disciplines. However, based on this survey, adjustments to working conditions and training in radiology seem necessary to maintain the health of the physicians concerned, to encourage motivation for scientific work and to enhance development opportunities, especially for women, through a better compatibility of work and family life. The present survey identifies strategies and leadership tools that can help to achieve this., Key Points:  Residents in radiology training ... · have a relatively high job satisfaction.. · experience a high psychosocial workload.. · evaluate the compatibility of family and work as in need of improvement.. · are interested in research, but evaluate research conditions as insufficient., Citation Format: · Oechtering TH, Panagiotopoulos N, Völker M et al. Work and Training Conditions of German Residents in Radiology - Results from a Nationwide Survey Conducted by the Young Radiology Forum in the German Roentgen Society. Fortschr Röntgenstr 2020; 192: 458 - 469., Competing Interests: The authors declare that they have no conflict of interest., (© Georg Thieme Verlag KG Stuttgart · New York.)

Objectives: This study aimed to develop a tool for the classification of masses in breast MRI, based on ultrafast TWIST-VIBE Dixon (TVD) dynamic sequences combined with DWI. TVD sequences allow to abbreviate breast MRI protocols, but provide kinetic information only on the contrast wash-in, and because of the lack of the wash-out kinetics, their diagnostic value might be hampered. A special focus of this study was thus to maintain high diagnostic accuracy in lesion classification., Materials and Methods: Sixty-one patients who received breast MRI between 02/2014 and 04/2015 were included, with 83 reported lesions (60 malignant). Our institute's standard breast MRI protocol was complemented by an ultrafast TVD sequence. ADC and peak enhancement of the TVD sequences were integrated into a generalised linear model (GLM) for malignancy prediction. For comparison, a second GLM was calculated using ADC and conventional DCE curve type. The resulting GLMs were evaluated for standard diagnostic parameters. For easy application of the GLMs, nomograms were created., Results: The GLM based on peak enhancement of the TVD and ADC was as equally accurate as the GLM based on conventional DCE and ADC, with no significant differences (sensitivity, 93.3%/93.3%; specificity, 91.3%/87.0%; PPV, 96.6%/94.9%; NPV, 84.0%/83.3%; all, p ≥ 0.315)., Conclusions: This study presents a method to integrate ultrafast TVD sequences into a breast MRI protocol, allowing a reduction of the examination time while maintaining diagnostic accuracy. A GLM based on the combination of TVD-derived peak enhancement and ADC provides high diagnostic accuracy, and can be easily applied using a nomogram., Key Points: • Ultrafast TWIST-VIBE Dixon sequence protocols in combination with diffusion-weighted imaging allow to shorten breast MRI examinations, while diagnostic accuracy is maintained. • Integrating peak enhancement from the TWIST-VIBE Dixon sequence and the apparent diffusion coefficient into a generalised linear model provides a comprehensible image evaluation approach. • This approach is further facilitated by nomograms.

To investigate whether automated volumetric radiomic analysis of breast cancer vascularization (VAV) can improve survival prediction in primary breast cancer. 314 consecutive patients with primary invasive breast cancer received standard clinical MRI before the initiation of treatment according to international recommendations. Diagnostic work-up, treatment, and follow-up was done at one tertiary care, academic breast-center (outcome: disease specific survival/DSS vs. disease specific death/DSD). The Nottingham Prognostic Index (NPI) was used as the reference method with which to predict survival of breast cancer. Based on the MRI scans, VAV was accomplished by commercially available, FDA-cleared software. DSD served as endpoint. Integration of VAV into the NPI gave NPI VAV . Prediction of DSD by NPI VAV compared to standard NPI alone was investigated (Cox regression, likelihood-test, predictive accuracy: Harrell's C, Kaplan Meier statistics and corresponding hazard ratios/HR, confidence intervals/CI). DSD occurred in 35 and DSS in 279 patients. Prognostication of the survival outcome by NPI (Harrell's C = 75.3%) was enhanced by VAV (NPI VAV : Harrell's C = 81.0%). Most of all, the NPI VAV identified patients with unfavourable outcome more reliably than NPI alone (hazard ratio/HR = 4.5; confidence interval/CI = 2.14-9.58; P = 0.0001). Automated volumetric radiomic analysis of breast cancer vascularization improved survival prediction in primary breast cancer. Most of all, it optimized the identification of patients at higher risk of an unfavorable outcome. Future studies should integrate MRI as a "gate keeper" in the management of breast cancer patients. Such a "gate keeper" could assist in selecting patients benefitting from more advanced diagnostic procedures (genetic profiling etc.) in order to decide whether are a more aggressive therapy (chemotherapy) is warranted.

We investigated whether the integration of machine learning (ML) into MRI interpretation can provide accurate decision rules for the management of suspicious breast masses. A total of 173 consecutive patients with suspicious breast masses upon complementary assessment (BI-RADS IV/V: n = 100/76) received standardized breast MRI prior to histological verification. MRI findings were independently assessed by two observers (R1/R2: 5 years of experience/no experience in breast MRI) using six (semi-)quantitative imaging parameters. Interobserver variability was studied by ICC (intraclass correlation coefficient). A polynomial kernel function support vector machine was trained to differentiate between benign and malignant lesions based on the six imaging parameters and patient age. Ten-fold cross-validation was applied to prevent overfitting. Overall diagnostic accuracy and decision rules (rule-out criteria) to accurately exclude malignancy were evaluated. Results were integrated into a web application and published online. Malignant lesions were present in 107 patients (60.8%). Imaging features showed excellent interobserver variability (ICC: 0.81-0.98) with variable diagnostic accuracy (AUC: 0.65-0.82). Overall performance of the ML algorithm was high (AUC = 90.1%; BI-RADS IV: AUC = 91.6%). The ML algorithm provided decision rules to accurately rule-out malignancy with a false negative rate <1% in 31.3% of the BI-RADS IV cases. Thus, integration of ML into MRI interpretation can provide objective and accurate decision rules for the management of suspicious breast masses, and could help to reduce the number of potentially unnecessary biopsies., Competing Interests: The authors of this manuscript declare relationships with the following companies: Michael Uder is on the speakers’ bureau for Bracco, Medtronic, Siemens and Bayer Schering. Rolf Janka is on the speakers’ bureau for Bracco. Tobias Bäuerle is on the speakers’ bureau for Bracco and Boehringer Ingelheim. For the remaining authors no potential conflicts of interest were declared. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Objective: Dynamic contrast-enhanced imaging of the initial (IP) and delayed phase (DP) is an integral part of any clinical breast MRI protocol. Furthermore, DWI is increasingly used as an add-on sequence by the breast-imaging community. We investigated whether DWI could be used as a substitute DP., Material and Methods: One hundred thirty-two consecutive patients with equivocal or suspicious findings at ultrasound and/or mammography received a full diagnostic breast MRI according to international recommendations. Histopathological verification served as reference standard. We evaluated three sections of the MRI protocol: IP, DP, and apparent diffusion coefficient (ADC) maps derived from DWI. Circular ROIs (regions of interest, mean size 5-10 mm 2 ) were drawn into the enhancing parts of the lesion (first postcontrast). ROIs were transferred to the corresponding location on ADC maps and IP and DP images. Mean ROI values were investigated signal intensity (SI): (1) Initial-phase enhancement = (SI(IP) - SI(precontrast))/SI(precontrast); (2) Delayed-phase enhancement = (SI(DP) - SI(IP))/SI(IP); (3) ADC. Multiparametric combinations were computed using logistic regression analysis: (1) IP+: Initial-phase enhancement and ADC; (2) Curve: Initial-phase enhancement and delayed-phase enhancement; (3) Curve+: Curve and ADC. The diagnostic performances of these feature combinations to diagnose malignancy were compared by the area under the receiver-operating characteristics curve (AUC)., Results: One hundred thirty-two patients (age: mean = 57.1 years, range 23-83 years) with 145 lesions were included (malignant/benign 101/44). IP+ (AUC = 0.877) outperformed Curve (AUC = 0.788, p = 0.03). Curve+ was not superior to IP+ (p = 1)., Conclusion: DWI could substitute DP. Because DWI is typically used as an add-on to IP and DP, our results might help to abbreviate and to simplify current practice of breast MRI., Key Points: • DWI provides similar but superior diagnostic information for diagnosis of malignancy in enhancing breast lesions compared to DP. • Adding DP to DWI does not provide incremental information to distinguish benign from malignant lesions. • DWI could substitute DP. As DWI is typically used as an add-on to IP and DP, our findings might help to abbreviate and to simplify current breast MRI practice.

Interference with autoimmune-mediated cytokine production is a key yet poorly developed approach to treat autoimmune and inflammatory diseases such as rheumatoid arthritis. Herein, we show that soluble CD83 (sCD83) enhances the resolution of autoimmune antigen-induced arthritis (AIA) by strongly reducing the expression levels of cytokines such as IL-17A, IFNγ, IL-6, and TNFα within the joints. Noteworthy, also the expression of RANKL, osteoclast differentiation, and joint destruction was significantly inhibited by sCD83. In addition, osteoclasts which were cultured in the presence of synovial T cells, derived from sCD83 treated AIA mice, showed a strongly reduced number of multinuclear large osteoclasts compared to mock controls. Enhanced resolution of arthritis by sCD83 was mechanistically based on IDO, since inhibition of IDO by 1-methyltryptophan completely abrogated sCD83 effects on AIA. Blocking experiments, using anti-TGF-β antibodies further revealed that also TGF-β is mechanistically involved in the sCD83 induced reduction of bone destruction and cartilage damage as well as enhanced resolution of inflammation. Resolution of arthritis was associated with increased numbers of regulatory T cells, which are induced in a sCD83-IDO-TGF-β dependent manner. Taken together, sCD83 represents an interesting approach for downregulating cytokine production, inducing regulatory T cells and inducing resolution of autoimmune arthritis.

Macrometastases in bone are preceded by bone marrow invasion of disseminated tumor cells. This study combined functional imaging parameters from FDG-PET/CT and MRI in a rat model of breast cancer bone metastases to a Model-averaged Neural Network (avNNet) for the detection of early metastatic disease and prediction of future macrometastases. Metastases were induced in 28 rats by injecting MDA-MB-231 breast cancer cells into the right superficial epigastric artery, resulting in the growth of osseous metastases in the right hind leg of the animals. All animals received FDG-PET/CT and MRI at days 0, 10, 20 and 30 after tumor cell injection. In total, 18/28 rats presented with metastases at days 20 or 30 (64.3%). None of the animals featured morphologic bone lesions during imaging at day 10, and the imaging parameters acquired at day 10 did not differ significantly between animals with metastases at or after day 20 and those without (all p > 0.3). The avNNet trained with the imaging parameters acquired at day 10, however, achieved an accuracy of 85.7% (95% CI 67.3-96.0%) in predicting future macrometastatic disease (ROC AUC 0.90; 95% CI 0.76-1.00), and significantly outperformed the predictive capacities of all single parameters (all p ≤ 0.02). The integration of functional FDG-PET/CT and MRI parameters into an avNNet can thus be used to predict macrometastatic disease with high accuracy, and their combination might serve as a surrogate marker for bone marrow invasion as an early metastatic process that is commonly missed during conventional staging examinations., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Bone metastases are a common source of osseous malignancy in the skeleton and affect up to 70% of all cancer patients. Hybrid imaging modalities including positron emission tomography (PET)/computed tomography (CT) and PET/MRI play an increasing role for the detection and follow-up of metastatic disease, especially in monitoring treatment response upon local or systemic therapy. This review summarizes current applications of PET/CT and PET/MRI in the clinical setting for imaging of metastases., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Magnetic resonance imaging (MRI) allows non-invasive evaluation of inflammatory bowel disease (IBD) by assessing pathologically altered gut. Besides morphological changes, relaxation times and diffusion capacity of involved bowel segments can be obtained by MRI. The aim of this study was to assess the use of multiparametric MRI in the diagnosis of experimentally induced colitis in mice, and evaluate the diagnostic benefit of parameter combinations using machine learning. This study relied on colitis induction by Dextran Sodium Sulfate (DSS) and investigated the colon of mice in vivo as well as ex vivo. Receiver Operating Characteristics were used to calculate sensitivity, specificity, positive- and negative-predictive values (PPV and NPV) of these single values in detecting DSS-treatment as a reference condition. A Model Averaged Neural Network (avNNet) was trained on the multiparametric combination of the measured values, and its predictive capacity was compared to those of the single parameters using exact binomial tests. Within the in vivo subgroup (n = 19), the avNNet featured a sensitivity of 91.3% (95% CI: 86.6-96.0%), specificity of 92.3% (95% CI: 85.1-99.6%), PPV of 96.9% (94.0-99.9%) and NPV of 80.0% (95% CI: 69.9-90.1%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.003) and performing significantly worse compared to none of the single values. Within the ex vivo subgroup (n = 30), the avNNet featured a sensitivity of 87.4% (95% CI: 82.6-92.2%), specificity of 82.9% (95% CI: 76.1-89.7%), PPV of 88.9% (84.3-93.5%) and NPV of 80.8% (95% CI: 73.8-87.9%), significantly outperforming all single parameters in at least 2 accuracy measures (p < 0.015), exceeded by none of the single parameters. In experimental mouse colitis, multiparametric MRI and the combination of several single measured values to an avNNet can significantly increase diagnostic accuracy compared to the single parameters alone. This pilot study will provide new avenues for the development of an MR-derived colitis score for optimized diagnosis and surveillance of inflammatory bowel disease., Competing Interests: The authors have declared that no competing interests exist.