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Protein kinase C is a family of kinases that play important roles in carcinogenesis. Medicinal plants from Plectranthus spp. (Lamiaceae) are a well-known source of interesting abietanes, such as 7α-acetoxy-6β-hydroxyroyleanone (Roy). This study aimed to extract and isolate Roy from P. grandidentatus Gürke, comparing two extraction methods (CO2 supercritical and ultrasound-assisted acetonic extraction), and design new royleanone derivatives for PKC modulation focusing on breast cancer therapy. The concentration of Roy in the extracts was determined by HPLC–DAD. The supercritical extraction method yielded 3.6% w/w, with the presence of 42.7 μg mg−1 of Roy (yield of 0.13%), while ultrasound-assisted acetonic extraction yielded 2.3% w/w, with the presence of 55.2 μg mg−1 of Roy (yield of 0.15%). The reactivity of Roy was investigated aiming at synthetizing new ester derivatives through standard benzoylation and esterification reactions. The benzoylated (Roy-12-Bz) and acetylated (Roy-12-Ac) derivatives in the C12 position were consistently prepared with overall good yields (33–86%). These results indicate the 12-OH position as the most reactive for esterification, affording derivatives under mild conditions. The reported di-benzoylated (RoyBz) and di-acetylated (RoyAc) derivatives were also synthesized after increasing the temperature (50 °C), reaction time, and using an excess of reagents. The cytotoxic potential of Roy and its derivatives was assessed against breast cancer cell lines, with RoyBz emerging as the most promising compound. Derivatization at position C-12 did not offer advantages over di-esterification at positions C-12 and C-6 or over the parent compound Roy and the presence of aromatic groups favored cytotoxicity. Evaluation of royleanones as PKC-α, βI, δ, ε, and ζ activators revealed DeRoy's efficacy across all isoforms, while RoyPr showed promising activation of PKC-δ but not PKC-ζ, highlighting the influence of slight structural changes on isoform selectivity. Molecular docking analysis emphasized the importance of microenvironmental factors in isoform specificity, underscoring the complexity of PKC modulation and the need for further exploration. [ABSTRACT FROM AUTHOR]

Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: To investigate the intra- and inter-rater reliability of the total radiomics quality score (RQS) and the reproducibility of individual RQS items' score in a large multireader study. Methods: Nine raters with different backgrounds were randomly assigned to three groups based on their proficiency with RQS utilization: Groups 1 and 2 represented the inter-rater reliability groups with or without prior training in RQS, respectively; group 3 represented the intra-rater reliability group. Thirty-three original research papers on radiomics were evaluated by raters of groups 1 and 2. Of the 33 papers, 17 were evaluated twice with an interval of 1 month by raters of group 3. Intraclass coefficient (ICC) for continuous variables, and Fleiss' and Cohen's kappa (k) statistics for categorical variables were used. Results: The inter-rater reliability was poor to moderate for total RQS (ICC 0.30–055, p < 0.001) and very low to good for item's reproducibility (k − 0.12 to 0.75) within groups 1 and 2 for both inexperienced and experienced raters. The intra-rater reliability for total RQS was moderate for the less experienced rater (ICC 0.522, p = 0.009), whereas experienced raters showed excellent intra-rater reliability (ICC 0.91–0.99, p < 0.001) between the first and second read. Intra-rater reliability on RQS items' score reproducibility was higher and most of the items had moderate to good intra-rater reliability (k − 0.40 to 1). Conclusions: Reproducibility of the total RQS and the score of individual RQS items is low. There is a need for a robust and reproducible assessment method to assess the quality of radiomics research. Clinical relevance statement: There is a need for reproducible scoring systems to improve quality of radiomics research and consecutively close the translational gap between research and clinical implementation. Key Points: • Radiomics quality score has been widely used for the evaluation of radiomics studies. • Although the intra-rater reliability was moderate to excellent, intra- and inter-rater reliability of total score and point-by-point scores were low with radiomics quality score. • A robust, easy-to-use scoring system is needed for the evaluation of radiomics research. [ABSTRACT FROM AUTHOR]

Temperature is a key factor that shapes the distribution of organisms. Having knowledge about how species respond to temperature is relevant to devise strategies for addressing the impacts of climate change. Aquatic insects are particularly vulnerable to climate change, yet there is still much to learn about their ecology and distribution. In the Yungas ecoregion of Northwestern Argentina, cold- and warm-adapted species of the orders Ephemeroptera, Plecoptera, and Trichoptera (EPT) are segregated by elevation. We modeled the ecological niche of South American EPT species in this region using available data and projected their potential distribution in geographic space. Species were grouped based on their ecogeographic similarity, and we analyzed their replacement pattern along elevation gradients, focusing on the ecotone where opposing thermal preferences converge. Along this interface, we identified critical points where the combined incidence of cold and warm assemblages maximizes, indicating a significant transition zone. We found that the Montane Cloud Forest holds the interface, with a particularly greater suitability at its lower boundary. The main axis of the interface runs in a N-S direction and falls between 14°C–16°C mean annual isotherms. The probability of a particular location within a basin being classified as part of the interface increases as Kira's warmth index approaches a score around 150. Understanding the interface is critical for defining the thermal limits of species distribution and designing biomonitoring programs. Changes in the location of thermal constants related to mountainous ecotones may cause vertical displacement of aquatic insects and vegetation communities. We have recognized significant temperature thresholds that serve as indicators of suitability for the interface. As global warming is anticipated to shift these indicators, we suggest using them to monitor the imprints of climate change on mountain ecosystems. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. Key points • The incorporation of artificial intelligence (AI) in radiological practice demands increased monitoring of its utility and safety. • Cooperation between developers, clinicians, and regulators will allow all involved to address ethical issues and monitor AI performance. • AI can fulfil its promise to advance patient well-being if all steps from development to integration in healthcare are rigorously evaluated. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the model-, code-, and data-sharing practices in the current radiomics research landscape and to introduce a radiomics research database. Methods: A total of 1254 articles published between January 1, 2021, and December 31, 2022, in leading radiology journals (European Radiology, European Journal of Radiology, Radiology, Radiology: Artificial Intelligence, Radiology: Cardiothoracic Imaging, Radiology: Imaging Cancer) were retrospectively screened, and 257 original research articles were included in this study. The categorical variables were compared using Fisher's exact tests or chi-square test and numerical variables using Student's t test with relation to the year of publication. Results: Half of the articles (128 of 257) shared the model by either including the final model formula or reporting the coefficients of selected radiomics features. A total of 73 (28%) models were validated on an external independent dataset. Only 16 (6%) articles shared the data or used publicly available open datasets. Similarly, only 20 (7%) of the articles shared the code. A total of 7 (3%) articles both shared code and data. All collected data in this study is presented in a radiomics research database (RadBase) and could be accessed at https://github.com/EuSoMII/RadBase. Conclusion: According to the results of this study, the majority of published radiomics models were not technically reproducible since they shared neither model nor code and data. There is still room for improvement in carrying out reproducible and open research in the field of radiomics. Clinical relevance statement: To date, the reproducibility of radiomics research and open science practices within the radiomics research community are still very low. Ensuring reproducible radiomics research with model-, code-, and data-sharing practices will facilitate faster clinical translation. Key Points: • There is a discrepancy between the number of published radiomics papers and the clinical implementation of these published radiomics models. • The main obstacle to clinical implementation is the lack of model-, code-, and data-sharing practices. • In order to translate radiomics research into clinical practice, the radiomics research community should adopt open science practices. [ABSTRACT FROM AUTHOR]

Percutaneous drainage is a first-line therapy for abscesses and other fluid collections. However, experimental data on the viscosity of body fluids are scarce. This study analyses the apparent viscosity of serous, purulent and biliary fluids to provide reference data for the evaluation of drainage catheters. Serous, purulent and biliary fluid samples were collected during routine drainage procedures. In a first setup, the apparent kinematic viscosity of 50 fluid samples was measured using an Ubbelohde viscometer. In a second setup, the apparent dynamic viscosity of 20 fluid samples obtained during CT-guided percutaneous drainage was measured using an in-house designed capillary extrusion experiment. The median apparent kinematic viscosity was 0.96 mm2/s (IQR 0.90–1.15 mm2/s) for serous samples, 0.98 mm2/s (IQR 0.97–0.99 mm2/s) for purulent samples and 2.77 mm2/s (IQR 1.75–3.70 mm2/s) for biliary samples. The median apparent dynamic viscosity was 1.63 mPa*s (IQR 1.27–2.09 mPa*s) for serous samples, 2.45 mPa*s (IQR 1.69–3.22 mPa*s) for purulent samples and 3.50 mPa*s (IQR 2.81–3.90 mPa*s) for biliary samples (all differences p < 0.01). Relative to water, dynamic viscosities were increased by a factor of 1.36 for serous fluids, 2.26 for purulent fluids, and 4.03 for biliary fluids. Serous fluids have apparent viscosities similar to water, but biliary and purulent fluids are more viscous. These data can be used as a reference when selecting the drainage catheter size, with 8F catheters being appropriate for most percutaneous drainage cases. [ABSTRACT FROM AUTHOR]

Structured reporting in radiology continues to hold substantial potential to improve the quality of service provided to patients and referring physicians. Despite many physicians' preference for structured reports and various efforts by radiological societies and some vendors, structured reporting has still not been widely adopted in clinical routine. While in many countries national radiological societies have launched initiatives to further promote structured reporting, cross-institutional applications of report templates and incentives for usage of structured reporting are lacking. Various legislative measures have been taken in the USA and the European Union to promote interoperable data formats such as Fast Healthcare Interoperability Resources (FHIR) in the context of the EU Health Data Space (EHDS) which will certainly be relevant for the future of structured reporting. Lastly, recent advances in artificial intelligence and large language models may provide innovative and efficient approaches to integrate structured reporting more seamlessly into the radiologists' workflow. The ESR will remain committed to advancing structured reporting as a key component towards more value-based radiology. Practical solutions for structured reporting need to be provided by vendors. Policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Critical relevance statement Over the past years, the benefits of structured reporting in radiology have been widely discussed and agreed upon; however, implementation in clinical routine is lacking due—policy makers should incentivize the usage of structured radiological reporting, especially in cross-institutional setting. Key points 1. Various national societies have established initiatives for structured reporting in radiology. 2. Almost no monetary or structural incentives exist that favor structured reporting. 3. A consensus on technical standards for structured reporting is still missing. 4. The application of large language models may help structuring radiological reports. 5. Policy makers should incentivize the usage of structured radiological reporting. [ABSTRACT FROM AUTHOR]

Objective: To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging (RNMMI). Methods: Web of Science was queried for relevant publications in RNMMI and medicine along with their associated data from 2000 to 2021. Bibliometric techniques utilised were co-occurrence, co-authorship, citation burst, and thematic evolution analyses. Growth rate and doubling time were also estimated using log-linear regression analyses. Results: According to the number of publications, RNMMI (11,209; 19.8%) was the most prominent category in medicine (56,734). USA (44.6%) and China (23.1%) were the two most productive and collaborative countries. USA and Germany experienced the strongest citation bursts. Thematic evolution has recently exhibited a significant shift toward deep learning. In all analyses, the annual number of publications and citations demonstrated exponential growth, with deep learning-based publications exhibiting the most prominent growth pattern. Estimated continuous growth rate, annual growth rate, and doubling time of the AI and machine learning publications in RNMMI were 26.1% (95% confidence interval [CI], 12.0–40.2%), 29.8% (95% CI, 12.7–49.5%), and 2.7 years (95% CI, 1.7–5.8), respectively. In the sensitivity analysis using data from the last 5 and 10 years, these estimates ranged from 47.6 to 51.1%, 61.0 to 66.7%, and 1.4 to 1.5 years. Conclusion: This study provides an overview of AI and radiomics research conducted mainly in RNMMI. These results may assist researchers, practitioners, policymakers, and organisations in gaining a better understanding of both the evolution of these fields and the importance of supporting (e.g., financial) these research activities. Key Points: • In terms of the number of publications on AI and ML, Radiology, Nuclear Medicine, and Medical Imaging was the most prominent category compared to the other categories related to medicine (e.g., Health Policy & Services, Surgery). • All evaluated analyses (i.e., AI, its subfields, and radiomics), based on the annual number of publications and citations, demonstrated exponential growth, with decreasing doubling time, which indicates increasing interest from researchers, journals, and, in turn, the medical imaging community. • The most prominent growth pattern was observed in deep learning-based publications. However, the further thematic analysis demonstrated that deep learning has been underdeveloped but highly relevant to the medical imaging community. [ABSTRACT FROM AUTHOR]

Be sure your imaging data is heterogeneous with distributional similarity among data partitions. The dataset must be split appropriately before performing any action on the imaging data set, such as image and radiomics feature preprocessing and feature dimensionality reduction [[13]]. Several algorithms perform better when feature values are in a common scale (i.e., feature scaling), which must be fitted exclusively to the training data [[18]]. Feature selection or data transformations should also be applied to reduce the dimensionality of the data. [Extracted from the article]

Purpose: To investigate the epidemiology and distribution of disease characteristics of urolithiasis by data mining structured radiology reports. Methods: The content of structured radiology reports of 2028 urolithiasis CTs was extracted from the department's structured reporting (SR) platform. The investigated cohort represented the full spectrum of a tertiary care center, including mostly symptomatic outpatients as well as inpatients. The prevalences of urolithiasis in general and of nephro- and ureterolithasis were calculated. The distributions of age, sex, calculus size, density and location, and the number of ureteral and renal calculi were calculated. For ureterolithiasis, the impact of calculus characteristics on the degree of possible obstructive uropathy was calculated. Results: The prevalence of urolithiasis in the investigated cohort was 72%. Of those patients, 25% had nephrolithiasis, 40% ureterolithiasis, and 35% combined nephro- and ureterolithiasis. The sex distribution was 2.3:1 (M:F). The median patient age was 50 years (IQR 36–62). The median number of calculi per patient was 1. The median size of calculi was 4 mm, and the median density was 734 HU. Of the patients who suffered from ureterolithiasis, 81% showed obstructive uropathy, with 2nd-degree uropathy being the most common. Calculus characteristics showed no impact on the degree of obstructive uropathy. Conclusion: SR-based data mining is a simple method by which to obtain epidemiologic data and distributions of disease characteristics, for the investigated cohort of urolithiasis patients. The added information can be useful for multiple purposes, such as clinical quality assurance, radiation protection, and scientific or economic investigations. To benefit from these, the consistent use of SR is mandatory. However, in clinical routine SR usage can be elaborate and requires radiologists to adapt. [ABSTRACT FROM AUTHOR]

Purpose: The radiologists' workload is increasing, and computational imaging techniques may have the potential to identify visually unequivocal lesions, so that the radiologist can focus on equivocal and critical cases. The purpose of this study was to assess radiomics versus dual-energy CT (DECT) material decomposition to objectively distinguish visually unequivocal abdominal lymphoma and benign lymph nodes. Methods: Retrospectively, 72 patients [m, 47; age, 63.5 (27–87) years] with nodal lymphoma (n = 27) or benign abdominal lymph nodes (n = 45) who had contrast-enhanced abdominal DECT between 06/2015 and 07/2019 were included. Three lymph nodes per patient were manually segmented to extract radiomics features and DECT material decomposition values. We used intra-class correlation analysis, Pearson correlation and LASSO to stratify a robust and non-redundant feature subset. Independent train and test data were applied on a pool of four machine learning models. Performance and permutation-based feature importance was assessed to increase the interpretability and allow for comparison of the models. Top performing models were compared by the DeLong test. Results: About 38% (19/50) and 36% (8/22) of the train and test set patients had abdominal lymphoma. Clearer entity clusters were seen in t-SNE plots using a combination of DECT and radiomics features compared to DECT features only. Top model performances of AUC = 0.763 (CI = 0.435–0.923) were achieved for the DECT cohort and AUC = 1.000 (CI = 1.000–1.000) for the radiomics feature cohort to stratify visually unequivocal lymphomatous lymph nodes. The performance of the radiomics model was significantly (p = 0.011, DeLong) superior to the DECT model. Conclusions: Radiomics may have the potential to objectively stratify visually unequivocal nodal lymphoma versus benign lymph nodes. Radiomics seems superior to spectral DECT material decomposition in this use case. Therefore, artificial intelligence methodologies may not be restricted to centers with DECT equipment. [ABSTRACT FROM AUTHOR]

Enhancing oil recovery in reservoirs with light oil and high gas content relies on optimizing the miscible water alternating gas (WAG) injection profile. However, this can be costly and time-consuming due to computationally demanding compositional simulation models and numerous other well control variables. This study introduces WAGeq, a novel approach that expedites the convergence of the optimization algorithm for miscible water alternating gas (WAG) injection in carbonate reservoirs. The WAGeq leverages production data to create flexible solutions that maximize the net present value (NPV) of the field, while providing practical implementation of individual WAG profiles for each injector. The WAGeq utilizes an injection priority index to rank the wells and determine which should inject water or gas at each time interval. The index is built using a parametric equation that considers factors such as producer and injector relationship, water cut (WCUT), gas–oil ratio (GOR), and wells cumulative gas production, to induce desirable effects on production and WAG profile. To evaluate WAGeq's effectiveness, two other approaches were compared: a benchmark solution named WAGbm, in which the injected fluid is optimized for each well over time, and a traditional baseline strategy with fixed 6-month WAG cycles. The procedures were applied to a synthetic simulation case (SEC1_2022) with characteristics of a Brazilian pre-salt carbonate field with karstic formations and high CO2 content. The WAGeq outperformed the baseline procedure, improving the NPV by 6.7% or 511 USD million. Moreover, WAGeq required fewer simulations (less than 350) than WAGbm (up to 2000), while delivering a slightly higher NPV. The terms of the equation were also found to be essential for producing a WAG profile with regular patterns on each injector, resulting in a more practical solution. In conclusion, WAGeq significantly reduces computational requirements while creating consistent patterns across injectors, which are crucial factors to consider when planning a practical WAG strategy. [ABSTRACT FROM AUTHOR]

As the enthusiasm surrounding Deep Learning grows, both medical practitioners and regulatory bodies are exploring ways to safely introduce image segmentation in clinical practice. One frontier to overcome when translating promising research into the clinical open world is the shift from static to continual learning. Continual learning, the practice of training models throughout their lifecycle, is seeing growing interest but is still in its infancy in healthcare. We present Lifelong nnU-Net, a standardized framework that places continual segmentation at the hands of researchers and clinicians. Built on top of the nnU-Net—widely regarded as the best-performing segmenter for multiple medical applications—and equipped with all necessary modules for training and testing models sequentially, we ensure broad applicability and lower the barrier to evaluating new methods in a continual fashion. Our benchmark results across three medical segmentation use cases and five continual learning methods give a comprehensive outlook on the current state of the field and signify a first reproducible benchmark. [ABSTRACT FROM AUTHOR]

Various studies have shown that medical professionals are prone to follow the incorrect suggestions offered by algorithms, especially when they have limited inputs to interrogate and interpret such suggestions and when they have an attitude of relying on them. We examine the effect of correct and incorrect algorithmic suggestions on the diagnosis performance of radiologists when (1) they have no, partial, and extensive informational inputs for explaining the suggestions (study 1) and (2) they are primed to hold a positive, negative, ambivalent, or neutral attitude towards AI (study 2). Our analysis of 2760 decisions made by 92 radiologists conducting 15 mammography examinations shows that radiologists' diagnoses follow both incorrect and correct suggestions, despite variations in the explainability inputs and attitudinal priming interventions. We identify and explain various pathways through which radiologists navigate through the decision process and arrive at correct or incorrect decisions. Overall, the findings of both studies show the limited effect of using explainability inputs and attitudinal priming for overcoming the influence of (incorrect) algorithmic suggestions. [ABSTRACT FROM AUTHOR]

Co-registration is required when the alignment of two or more point clouds obtained for mapping natural and built environments is needed. While closed-form solutions are suitable for co-registration, most of the existing approaches rely on unit quaternion solutions for the estimation of transformation parameters from point or plane correspondences. This paper presents a novel co-registration of terrestrial light detection and ranging point clouds solution to create globally consistent 3-D environments. Our method exploits the advantages of the dual quaternion solution combining both points and plane correspondences. The role of our relaxation labeling technique in 3-D matching (3PRL) is investigated, and its efficiency to find the best plane correspondences is shown. The paper also presents a method to treat degenerate plane configurations with corresponding virtual points. Experimental results reveal that our 3PRL technique can update and improve the 3-D matching probabilities using binary relations. At the same time, the proposed dual quaternions point- and plane-based optimization indicated that the mathematical optimization might represent a valid model for co-registration of point clouds. A closer inspection of co-registration accuracy revealed that the translation and rotation error mean decreased drastically, with margins between 0.10 m and 0.17 m and 0.01° and 0.33°, respectively. Experiments have shown that our method generally achieves better results than existing methods. [ABSTRACT FROM AUTHOR]

The article discusses the topic of structured reporting in radiology and its slow adoption in daily practice. It highlights the need for harmonization of reporting templates and incentives for radiologists to adopt structured reporting. The article also mentions the potential use of large language models (LLMs) to structure unstructured reports and the challenges and potential biases associated with relying on commercial AI-based solutions. Overall, the article emphasizes the importance of structured reporting in transforming radiology practice and leveraging the valuable data contained in radiological reports. [Extracted from the article]

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature. Key points: The workflow of radiomics is complex with several methodological steps and nuances, which often leads to inadequate reproducibility, reporting, and evaluation. The CLEAR checklist proposes a single documentation standard for radiomics research that can guide authors, providing the minimum requirements for presenting clinical radiomics research. The CLEAR checklist aims to include all necessary items to support reviewer evaluation of radiomics-related manuscripts. [ABSTRACT FROM AUTHOR]

Copyright of Die Radiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: To evaluate the implementation process of structured reporting (SR) in a tertiary care institution over a period of 7 years. Methods: We analysed the content of our image database from January 2016 to December 2022 and compared the numbers of structured reports and free-text reports. For the ten most common SR templates, usage proportions were calculated on a quarterly basis. Annual modality-specific SR usage was calculated for ultrasound, CT, and MRI. During the implementation process, we surveyed radiologists and clinical referring physicians concerning their views on reporting in radiology. Results: As of December 2022, our reporting platform contained more than 22,000 structured reports. Use of the ten most common SR templates increased markedly since their implementation, leading to a mean SR usage of 77% in Q4 2022. The highest percentages of SR usage were shown for trauma CT, focussed assessment with ultrasound for trauma (FAST), and prostate MRI: 97%, 95%, and 92%, respectively, in 2022. Overall modality-specific SR usage was 17% for ultrasound, 13% for CT, and 6% for MRI in 2022. Both radiologists and referring physicians were more satisfied with structured reports and rated SR better than free-text reporting (FTR) on various attributes. Conclusions: The increasing SR usage during the period under review and the positive attitude towards SR among both radiologists and clinical referrers show that SR can be successfully implemented. We therefore encourage others to take this step in order to benefit from the advantages of SR. Key points: Structured reporting usage increased markedly since its implementation at our institution in 2016. Mean usage for the ten most popular structured reporting templates was 77% in 2022. Both radiologists and referring physicians preferred structured reports over free-text reports. Our data shows that structured reporting can be successfully implemented. We strongly encourage others to implement structured reporting at their institutions. [ABSTRACT FROM AUTHOR]

Background: Fast and accurate diagnostics are key for personalised medicine. Particularly in cancer, precise diagnosis is a prerequisite for targeted therapies, which can prolong lives. In this work, we focus on the automatic identification of gastroesophageal adenocarcinoma (GEA) patients that qualify for a personalised therapy targeting epidermal growth factor receptor 2 (HER2). We present a deep-learning method for scoring microscopy images of GEA for the presence of HER2 overexpression. Methods: Our method is based on convolutional neural networks (CNNs) trained on a rich dataset of 1602 patient samples and tested on an independent set of 307 patient samples. We additionally verified the CNN's generalisation capabilities with an independent dataset with 653 samples from a separate clinical centre. We incorporated an attention mechanism in the network architecture to identify the tissue regions, which are important for the prediction outcome. Our solution allows for direct automated detection of HER2 in immunohistochemistry-stained tissue slides without the need for manual assessment and additional costly in situ hybridisation (ISH) tests. Results: We show accuracy of 0.94, precision of 0.97, and recall of 0.95. Importantly, our approach offers accurate predictions in cases that pathologists cannot resolve and that require additional ISH testing. We confirmed our findings in an independent dataset collected in a different clinical centre. The attention-based CNN exploits morphological information in microscopy images and is superior to a predictive model based on the staining intensity only. Conclusions: We demonstrate that our approach not only automates an important diagnostic process for GEA patients but also paves the way for the discovery of new morphological features that were previously unknown for GEA pathology. [ABSTRACT FROM AUTHOR]

Objectives: To systematically review current research applications of radiomics in patients with cholangiocarcinoma and to assess the quality of CT and MRI radiomics studies. Methods: A systematic search was conducted on PubMed/Medline, Web of Science, and Scopus databases to identify original studies assessing radiomics of cholangiocarcinoma on CT and/or MRI. Three readers with different experience levels independently assessed quality of the studies using the radiomics quality score (RQS). Subgroup analyses were performed according to journal type, year of publication, quartile and impact factor (from the Journal Citation Report database), type of cholangiocarcinoma, imaging modality, and number of patients. Results: A total of 38 original studies including 6242 patients (median 134 patients) were selected. The median RQS was 9 (corresponding to 25.0% of the total RQS; IQR 1–13) for reader 1, 8 (22.2%, IQR 3–12) for reader 2, and 10 (27.8%; IQR 5–14) for reader 3. The inter-reader agreement was good with an ICC of 0.75 (95% CI 0.62–0.85) for the total RQS. All studies were retrospective and none of them had phantom assessment, imaging at multiple time points, nor performed cost-effectiveness analysis. The RQS was significantly higher in studies published in journals with impact factor > 4 (median 11 vs. 4, p = 0.048 for reader 1) and including more than 100 patients (median 11.5 vs. 0.5, p < 0.001 for reader 1). Conclusions: Quality of radiomics studies on cholangiocarcinoma is insufficient based on the radiomics quality score. Future research should consider prospective studies with a standardized methodology, validation in multi-institutional external cohorts, and open science data. Key points: The quality of current radiomics studies on cholangiocarcinoma is insufficient, with a median radiomics quality score of 8–10, corresponding to 22–28% of the ideal quality score. None of the current studies conducted phantom assessment, imaging at multiple time points, prospective registration in a trial database, nor cost-effectiveness analysis. The inter-reader agreement of the radiomics quality score is good (ICC of 0.75; 95% CI 0.62–0.85) among readers with different levels of experience. [ABSTRACT FROM AUTHOR]

Objectives: Low bone mineral density (BMD) was recently identified as a novel risk factor for patients with hepatocellular carcinoma (HCC). In this multicenter study, we aimed to validate the role of BMD as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE). Methods: This retrospective multicenter trial included 908 treatment-naïve patients with HCC who were undergoing TACE as a first-line treatment, at six tertiary care centers, between 2010 and 2020. BMD was assessed by measuring the mean Hounsfield units (HUs) in the midvertebral core of the 11th thoracic vertebra, on contrast-enhanced computer tomography performed before treatment. We assessed the influence of BMD on median overall survival (OS) and performed multivariate analysis including established estimates for survival. Results: The median BMD was 145 HU (IQR, 115–175 HU). Patients with a high BMD (≥ 114 HU) had a median OS of 22.2 months, while patients with a low BMD (< 114 HU) had a lower median OS of only 16.2 months (p <.001). Besides albumin, bilirubin, tumor number, and tumor diameter, BMD remained an independent prognostic factor in multivariate analysis. Conclusions: BMD is an independent predictive factor for survival in elderly patients with HCC undergoing TACE. The integration of BMD into novel scoring systems could potentially improve survival prediction and clinical decision-making. Key Points: • Bone mineral density can be easily assessed in routinely acquired pre-interventional computed tomography scans. • Bone mineral density is an independent predictive factor for survival in elderly patients with HCC undergoing TACE. • Thus, bone mineral density is a novel imaging biomarker for prognosis prediction in elderly patients with HCC undergoing TACE. [ABSTRACT FROM AUTHOR]

There has been an exponential rise in artificial intelligence (AI) research in imaging in recent years. While the dissemination of study data that has the potential to improve clinical practice is welcomed, the level of detail included in early AI research reporting has been highly variable and inconsistent, particularly when compared to more traditional clinical research. However, inclusion checklists are now commonly available and accessible to those writing or reviewing clinical research papers. AI-specific reporting guidelines also exist and include distinct requirements, but these can be daunting for radiologists new to the field. Given that pediatric radiology is a specialty faced with workforce shortages and an ever-increasing workload, AI could help by offering solutions to time-consuming tasks, thereby improving workflow efficiency and democratizing access to specialist opinion. As a result, pediatric radiologists are expected to be increasingly leading and contributing to AI imaging research, and researchers and clinicians alike should feel confident that the findings reported are presented in a transparent way, with sufficient detail to understand how they apply to wider clinical practice. In this review, we describe two of the most clinically relevant and available reporting guidelines to help increase awareness and engage the pediatric radiologist in conducting AI imaging research. This guide should also be useful for those reading and reviewing AI imaging research and as a checklist with examples of what to expect. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to evaluate whether simple 2D measurements in axial slices of head and neck CT examinations correlate with generally established measurements of body composition in abdominal CT at the height of the third lumbar vertebra and thus allow for an estimation of muscle and fat masses. Methods: One hundred twenty-two patients who underwent concurrent CT of the head and neck and the abdomen between July 2016 and July 2020 were retrospectively included. For a subset of 30 patients, additional bioelectrical impedance analysis (BIA) was available. Areas of paraspinal muscles at the height of the third (C3) and fifth cervical vertebrae (C5) as well as the total cross-sectional area at the height of C3 and at the submandibular level were correlated with the results of abdominal measurements and BIA. Furthermore, intra- and interreader variabilities of all measurements were assessed. Results: Regarding adipose tissue, good correlations were found between the total cross-sectional area of the patient's body at the submandibular level and at the height of C3 between both abdominal measurements and BIA results (r = 0.8–0.92; all p < 0.001). Regarding muscle, the total paraspinal muscle area at the height of C3 and C5 showed strong correlations with abdominal measurements and moderate to strong correlations with BIA results (r = 0.44–0.80; all p < 0.001), with the muscle area on C5 yielding slightly higher correlations. Conclusions: Body composition information can be obtained with comparable reliability from head and neck CT using simple biplanar measurements as from abdominal CT. Key Points: • The total paraspinal muscle area at the height of C3 and C5 correlates strongly with abdominal muscle mass. • The total cross-sectional area at the submandibular level and at the height of C3 shows good correlations with abdominal fat mass. • The described measurements facilitate a rapid, opportunistic assessment of relevant body composition parameters. [ABSTRACT FROM AUTHOR]

Objectives: Splenic volume (SV) was proposed as a relevant prognostic factor for patients with hepatocellular carcinoma (HCC). We trained a deep-learning algorithm to fully automatically assess SV based on computed tomography (CT) scans. Then, we investigated SV as a prognostic factor for patients with HCC undergoing transarterial chemoembolization (TACE). Methods: This retrospective study included 327 treatment-naïve patients with HCC undergoing initial TACE at our tertiary care center between 2010 and 2020. A convolutional neural network was trained and validated on the first 100 consecutive cases for spleen segmentation. Then, we used the algorithm to evaluate SV in all 327 patients. Subsequently, we evaluated correlations between SV and survival as well as the risk of hepatic decompensation during TACE. Results: The algorithm showed Sørensen Dice Scores of 0.96 during both training and validation. In the remaining 227 patients assessed with the algorithm, spleen segmentation was visually approved in 223 patients (98.2%) and failed in four patients (1.8%), which required manual re-assessments. Mean SV was 551 ml. Survival was significantly lower in patients with high SV (10.9 months), compared to low SV (22.0 months, p = 0.001). In contrast, overall survival was not significantly predicted by axial and craniocaudal spleen diameter. Furthermore, patients with a hepatic decompensation after TACE had significantly higher SV (p < 0.001). Conclusion: Automated SV assessments showed superior survival predictions in patients with HCC undergoing TACE compared to two-dimensional spleen size estimates and identified patients at risk of hepatic decompensation. Thus, SV could serve as an automatically available, currently underappreciated imaging biomarker. Key Points: • Splenic volume is a relevant prognostic factor for prediction of survival in patients with HCC undergoing TACE, and should be preferred over two-dimensional surrogates for splenic size. • Besides overall survival, progression-free survival and hepatic decompensation were significantly associated with splenic volume, making splenic volume a currently underappreciated prognostic factor prior to TACE. • Splenic volume can be fully automatically assessed using deep-learning methods; thus, it is a promising imaging biomarker easily integrable into daily radiological routine. [ABSTRACT FROM AUTHOR]

Objectives: To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT.Methods: In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection.Results: Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection.Conclusions: Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT.Key Points: • Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy. [ABSTRACT FROM AUTHOR]

Objectives: In response to the COVID-19 pandemic, many researchers have developed artificial intelligence (AI) tools to differentiate COVID-19 pneumonia from other conditions in chest CT. However, in many cases, performance has not been clinically validated. The aim of this study was to evaluate the performance of commercial AI solutions in differentiating COVID-19 pneumonia from other lung conditions.Methods: Four commercial AI solutions were evaluated on a dual-center clinical dataset consisting of 500 CT studies; COVID-19 pneumonia was microbiologically proven in 50 of these. Sensitivity, specificity, positive and negative predictive values, and AUC were calculated. In a subgroup analysis, the performance of the AI solutions in differentiating COVID-19 pneumonia from other conditions was evaluated in CT studies with ground-glass opacities (GGOs).Results: Sensitivity and specificity ranges were 62-96% and 31-80%, respectively. Negative and positive predictive values ranged between 82-99% and 19-25%, respectively. AUC was in the range 0.54-0.79. In CT studies with GGO, sensitivity remained unchanged. However, specificity was lower, and ranged between 15 and 53%. AUC for studies with GGO was in the range 0.54-0.69.Conclusions: This study highlights the variable specificity and low positive predictive value of AI solutions in diagnosing COVID-19 pneumonia in chest CT. However, one solution yielded acceptable values for sensitivity. Thus, with further improvement, commercial AI solutions currently under development have the potential to be integrated as alert tools in clinical routine workflow. Randomized trials are needed to assess the true benefits and also potential harms of the use of AI in image analysis.Key Points: • Commercial AI solutions achieved a sensitivity and specificity ranging from 62 to 96% and from 31 to 80%, respectively, in identifying patients suspicious for COVID-19 in a clinical dataset. • Sensitivity remained within the same range, while specificity was even lower in subgroup analysis of CT studies with ground-glass opacities, and interrater agreement between the commercial AI solutions was minimal to nonexistent. • Thus, commercial AI solutions have the potential to be integrated as alert tools for the detection of patients with lung changes suspicious for COVID-19 pneumonia in a clinical routine workflow, if further improvement is made. [ABSTRACT FROM AUTHOR]

The research article was carried out with the objective of studying the genetic variation on the resilience of buffaloes to negative energy balance—NEB (measured by changes in body weight in early lactation)—as well as investigating genomic regions of interest for this trait. A model of reaction norms was used, considering milk production as the trait to be analyzed and solutions of the contemporary groups to weight changes as environmental gradient. In this methodology, the genetic value of the slope represents the measure of resilience of the animals. After the estimation step, a genome-wide association analysis was performed for the slope of the reaction norms model, to obtain a list of windows and associated genes. The heritability estimates for milk production over the resilience gradient ranged from 0.13 to 0.28, with lower values in the intermediate environmental groups. Regarding the productive resilience of dairy buffalo cows to NEB, the genomic windows with the highest contribution to the genetic variance were detected on chromosomes BBU 1, 2, 3, 4, 9, 12, 19, and 21. A functional analysis of the genes described in the selected windows indicated association with metabolic routes related to growth and immunity of the animals, with an emphasis on the STAT6 gene. The results presented indicate that there is for this trait genetic variation to be used as selection criteria, in addition to genomic regions that can increase the precision of the selection. [ABSTRACT FROM AUTHOR]

Purpose: Esophageal perforation is associated with high morbidity and mortality. In addition to surgical treatment, endoscopic endoluminal stent placement and endoscopic vacuum therapy (EVT) are established methods in the management of this emergency condition. Although health-related quality of life (HRQoL) is becoming a major issue in the evaluation of any therapeutic intervention, not much is known about HRQoL, particularly in the long-term follow-up of patients treated for non-neoplastic esophageal perforation with different treatment strategies. The aim of this study was to evaluate patients' outcome after non-neoplastic esophageal perforation with focus on HRQoL in the long-term follow-up. Methods: Patients treated for non-neoplastic esophageal perforation at the University Hospital Cologne from January 2003 to December 2014 were included. Primary outcome and management of esophageal perforation were documented. Long-term quality of life was assessed using the Gastrointestinal Quality of Life Index (GIQLI), the Health-Related Quality of Life Index (HRQL) for patients with gastroesophageal reflux disease (GERD), and the European Organization for Research and Treatment of Cancer (EORTC) questionnaires for general and esophageal specific QoL (QLQ-C30 and QLQ-OES18). Results: Fifty-eight patients were included in the study. Based on primary treatment, patients were divided into an endoscopic (n = 27; 46.6%), surgical (n = 20; 34.5%), and a conservative group (n = 11; 19%). Short- and long-term outcome and quality of life were compared. HRQoL was measured after a median follow-up of 49 months. HRQoL was generally reduced in patients with non-neoplastic esophageal perforation. Endoscopically treated patients showed the highest GIQLI overall score and highest EORTC general health status, followed by the conservative and the surgical group. Conclusion: HRQoL in patients with non-neoplastic esophageal perforation is reduced even in the long-term follow-up. Temporary stent or EVT is effective and provides a good alternative to surgery, not only in the short-term but also in the long-term follow-up. [ABSTRACT FROM AUTHOR]

Background: Twitter has become one of the most important social media platforms in science communication. During scientific conferences, Twitter can facilitate the communication between audience and speakers present at the venue and can extend the reach of a conference to participants following along from home. To examine whether Twitter activity can serve as a surrogate parameter for attendance at the RSNA conferences in 2019 and in 2020, and to characterize changes in topics discussed due to the virtual character of the 2020 RSNA conference. Methods: The Twitter API and R Studio were used to analyze the absolute number and frequency of tweets, retweets, and conference-related hashtags during the 2019 and 2020 RSNA conference. Topics of discussion were compared across years by visualizing networks of co-occurring hashtags. Results: There was a 46% decrease in total tweets and a 39% decrease in individual Twitter users in 2020, mirroring a 43% decrease in registered attendees during the virtual conference. Hashtags related to social initiatives in radiology (e.g., "#radxx" and "#womeninradiology" for promoting women's empowerment in radiology or "#pinksocks," "#weareradiology" and "#diversityisgenius" for diversity in general) were less frequently used in 2020 than in 2019. Conclusion: Twitter and congress attendance were highly related and interpersonal topics underwent less discussion during the virtual meeting. Overall engagement during the virtual conference in 2020 was lower compared to the in-person conference in 2019. [ABSTRACT FROM AUTHOR]

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Der Radiologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Due to the outbreak of the coronavirus disease 2019 (COVID-19), it proved necessary to rapidly change medical education from on-site to online teaching. Thus, medical educators were forced to rethink the purpose of teaching and the best form of transmission of knowledge. In cooperation with the European Society of Radiology (ESR), we investigated the attitudes of radiologists in Europe and North America toward innovative online teaching concepts. Methods: In total, 224 radiologists from 31 different countries participated in our cross-sectional, web-based survey study. On a 7-point Likert scale, participants had to answer 27 questions about the online teaching situation before/during the pandemic, technical and social aspects of online teaching and the future role of online teaching in radiology. Results: An overwhelming majority stated that radiology is particularly well-suited for online teaching (91%), that online teaching should play a more prominent role after the pandemic (73%) and that lecturers should be familiar with online teaching techniques (89%). Difficulties include a higher workload in preparing online courses (59%), issues with motivating students to follow online courses (56%) and the risk of social isolation (71%). Before the pandemic, only 12% of teaching was provided online; for the future, our participants deemed a proportion of approximately 50% online teaching appropriate. Conclusion: Our participants are open-minded about online teaching in radiology. As the best way of transferring knowledge in medical education is still unclear, online teaching offers potential for innovation in radiology education. To support online teaching development, a structured, framework-based "online curriculum" should be established. [ABSTRACT FROM AUTHOR]

Our purpose was to analyze the robustness and reproducibility of magnetic resonance imaging (MRI) radiomic features. We constructed a multi-object fruit phantom to perform MRI acquisition as scan-rescan using a 3 Tesla MRI scanner. We applied T2-weighted (T2w) half-Fourier acquisition single-shot turbo spin-echo (HASTE), T2w turbo spin-echo (TSE), T2w fluid-attenuated inversion recovery (FLAIR), T2 map and T1-weighted (T1w) TSE. Images were resampled to isotropic voxels. Fruits were segmented. The workflow was repeated by a second reader and the first reader after a pause of one month. We applied PyRadiomics to extract 107 radiomic features per fruit and sequence from seven feature classes. We calculated concordance correlation coefficients (CCC) and dynamic range (DR) to obtain measurements of feature robustness. Intraclass correlation coefficient (ICC) was calculated to assess intra- and inter-observer reproducibility. We calculated Gini scores to test the pairwise discriminative power specific for the features and MRI sequences. We depict Bland Altmann plots of features with top discriminative power (Mann–Whitney U test). Shape features were the most robust feature class. T2 map was the most robust imaging technique (robust features (rf), n = 84). HASTE sequence led to the least amount of rf (n = 20). Intra-observer ICC was excellent (≥ 0.75) for nearly all features (max–min; 99.1–97.2%). Deterioration of ICC values was seen in the inter-observer analyses (max–min; 88.7–81.1%). Complete robustness across all sequences was found for 8 features. Shape features and T2 map yielded the highest pairwise discriminative performance. Radiomics validity depends on the MRI sequence and feature class. T2 map seems to be the most promising imaging technique with the highest feature robustness, high intra-/inter-observer reproducibility and most promising discriminative power. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has made impressive progress over the past few years, including many applications in medical imaging. Numerous commercial solutions based on AI techniques are now available for sale, forcing radiology practices to learn how to properly assess these tools. While several guidelines describing good practices for conducting and reporting AI-based research in medicine and radiology have been published, fewer efforts have focused on recommendations addressing the key questions to consider when critically assessing AI solutions before purchase. Commercial AI solutions are typically complicated software products, for the evaluation of which many factors are to be considered. In this work, authors from academia and industry have joined efforts to propose a practical framework that will help stakeholders evaluate commercial AI solutions in radiology (the ECLAIR guidelines) and reach an informed decision. Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. Key Points: • Numerous commercial solutions based on artificial intelligence techniques are now available for sale, and radiology practices have to learn how to properly assess these tools. • We propose a framework focusing on practical points to consider when assessing an AI solution in medical imaging, allowing all stakeholders to conduct relevant discussions with manufacturers and reach an informed decision as to whether to purchase an AI commercial solution for imaging applications. • Topics to consider in the evaluation include the relevance of the solution from the point of view of each stakeholder, issues regarding performance and validation, usability and integration, regulatory and legal aspects, and financial and support services. [ABSTRACT FROM AUTHOR]

Objectives: The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows.Methods: All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324).Results: In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2-91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3-94.6%).Conclusions: Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension.Key Points: • Data from one single institution can be used to train a neural network for the correct detection of the 30 most common categories of plain radiographs. • The trained model achieved a high accuracy for the majority of categories and showed good generalizability to images from other institutions. • The neural network is made publicly available and can be used to automatically organize a PACS or to preselect radiographs so that they can be routed to more specialized neural networks for abnormality detection. [ABSTRACT FROM AUTHOR]

Key Points: • Although radiomics is potentially a promising approach to analyze medical image data, many pitfalls need to be considered to avoid a reproducibility crisis. • There is a translation gap in radiomics research, with many studies being published but so far little to no translation into clinical practice. • Going forward, more studies with higher levels of evidence are needed, ideally also focusing on prospective studies with relevant clinical impact. [ABSTRACT FROM AUTHOR]

In 2013, the new European Basic Safety Standards Directive 2013/59/Euratom (BSS Directive), which defines the new legal framework for the use of ionising radiation in medical imaging and radiotherapy, was published. In 2014, the ESR EuroSafe Imaging Initiative was founded with a goal in mind "to support and strengthen medical radiation protection across Europe following a holistic, inclusive approach". To support radiology departments in developing a programme of clinical audit, the ESR developed a Guide to Clinical Audit and an accompanying audit tool in 2017, with an expanded second edition released in 2019 and published under the name of Esperanto – ESR Guide to Clinical Audit in Radiology and the ESR Clinical Audit Tool, 2019. Audits represent specific aspects at a certain point in time, usually with retrospective evaluation of data. Key performance indicators (KPIs), on the other hand, are intended to enable continuous monitoring of relevant parameters, for example to provide warnings or a dashboard. KPIs, which can, for example, be recorded automatically and visualised in dashboards, are suitable for this purpose. This paper will discuss a selection of indicators covering different areas and include suggestions for their implementation. [ABSTRACT FROM AUTHOR]

The registration problem has been one of the most popular research topics in the geodesic applications. However, what makes the registration problem challenging are partially overlapping surfaces and when no information from the global positioning system (GPS) and inertial navigation system (INS) is given. In this paper, we propose a framework for registration of multiple 3D point clouds derived from a terrestrial laser scanning (TLS) system, which uses a closed-form coarse-to-fine scheme and globally eliminate the residual errors. The main contribution of this work lies in the closed-form multi-feature corresponding model for fine orientation task. Since the multi-feature with geometric constraint is used, a point cloud registration is more likely to be achieved. Due to the coarse orientation task corresponding point-to-plane and line features are non-iteratively obtained in this step. Consequently, a reliable alignment for the global registration solution can be achieved. The advantages of this framework are demonstrated using a challenging real dataset. Additionally, the approach is tested and evaluated against another state-of-the-art algorithm. The results of the experiments indicate that a sensor pose better than 10 mm can be achieved by using this approach. [ABSTRACT FROM AUTHOR]

Objectives: Interventional radiology (IR) is a growing field but is underrepresented in most medical school curricula. We tested whether endovascular simulator training improves medical students' attitudes towards IR.Materials and Methods: We conducted this prospective study at two university medical centers; overall, 305 fourth-year medical students completed a 90-min IR course. The class consisted of theoretical and practical parts involving endovascular simulators. Students completed questionnaires before the course, after the theoretical and after the practical part. On a 7-point Likert scale, they rated their interest in IR, knowledge of IR, attractiveness of IR, and the likelihood to choose IR as subspecialty. We used a crossover design to prevent position-effect bias.Results: The seminar/simulator parts led to the improvement for all items compared with baseline: interest in IR (pre-course 5.2 vs. post-seminar/post-simulator 5.5/5.7), knowledge of IR (pre-course 2.7 vs. post-seminar/post-simulator 5.1/5.4), attractiveness of IR (pre-course 4.6 vs. post-seminar/post-simulator 4.8/5.0), and the likelihood of choosing IR as a subspecialty (pre-course 3.3 vs. post-seminar/post-simulator 3.8/4.1). Effect was significantly stronger for simulator training compared with that for seminar for all items (p < 0.05). For simulator training, subgroup analysis of students with pre-existing positive attitude showed considerable improvement regarding "interest in IR" (× 1.4), "knowledge of IR" (× 23), "attractiveness of IR" (× 2), and "likelihood to choose IR" (× 3.2) compared with pretest.Conclusion: Endovascular simulator training significantly improves students' attitude towards IR regarding all items. Implementing such courses at a very early stage in the curriculum should be the first step to expose medical students to IR and push for IR.Key Points: • Dedicated IR-courses have a significant positive effect on students' attitudes towards IR. • Simulator training is superior to a theoretical seminar in positively influencing students' attitudes towards IR. • Implementing dedicated IR courses in medical school might ease recruitment problems in the field. [ABSTRACT FROM AUTHOR]

P-glycoprotein (P-gp, ABCB1) overexpression is, currently, one of the most important multidrug resistance (MDR) mechanisms in tumor cells. Thus, modulating drug efflux by P-gp has become one of the most promising approaches to overcome MDR in cancer. Yet, more insights on the molecular basis of drug specificity and efflux-related signal transmission mechanism between the transmembrane domains (TMDs) and the nucleotide binding domains (NBDs) are needed to develop molecules with higher selectivity and efficacy. Starting from a murine P-gp crystallographic structure at the inward-facing conformation (PDB ID: 4Q9H), we evaluated the structural quality of the herein generated human P-gp homology model. This initial human P-gp model, in the presence of the "linker" and inserted in a suitable lipid bilayer, was refined through molecular dynamics simulations and thoroughly validated. The best human P-gp model was further used to study the effect of four single-point mutations located at the TMDs, experimentally related with changes in substrate specificity and drug-stimulated ATPase activity. Remarkably, each P-gp mutation is able to induce transmembrane α-helices (TMHs) repacking, affecting the drug-binding pocket volume and the drug-binding sites properties (e.g. volume, shape and polarity) finally compromising drug binding at the substrate binding sites. Furthermore, intracellular coupling helices (ICH) also play an important role since changes in the TMHs rearrangement are shown to have an impact in residue interactions at the ICH-NBD interfaces, suggesting that identified TMHs repacking affect TMD-NBD contacts and interfere with signal transmission from the TMDs to the NBDs. [ABSTRACT FROM AUTHOR]

In view of the limitations of bone reconstruction surgeries using autologous grafts as a gold standard, tissue engineering is emerging as an alternative, which permits the fabrication and improvement of scaffolds to stimulate osteogenesis and angiogenesis, processes that are essential for bone repair. Polymers are used to mimic the extracellular bone matrix and support cell growth. In addition, bone neoformation can be induced by external factors such as laser irradiation, which stimulates bone metabolism. The objective of this study was to evaluate the regeneration of bone defects using collagen and elastin membranes derived from intestinal serosa and bovine auricular cartilage combined with low-level laser application. Thirty-six Wistar rats were operated to create a 3-mm defect in the distal metaphysis of the left femur and divided into six groups: G1 (control, no treatment); G2 (laser); G3 (elastin graft), G4 (elastin+laser); G5 (collagen graft); G6 (collagen+laser). The animals were sacrificed 6 weeks after surgery and the femurs were removed for analysis of bone repair. Macroscopic and radiological results showed the absence of an infectious process in the surgical area. This was confirmed by histological analysis, which revealed no inflammatory infiltrate. Histomorphometry showed that the formation of new bone started from the margins of the bone defect and its volume was greater in elastin+laser and collagen+laser. We conclude that newly formed bone in the graft area was higher in the groups that received the biomaterials and laser. The collagen and elastin matrices showed biocompatibility. [ABSTRACT FROM AUTHOR]

Objectives: To analyze all artificial intelligence abstracts presented at the European Congress of Radiology (ECR) 2019 with regard to their topics and their adherence to the Standards for Reporting Diagnostic accuracy studies (STARD) checklist. Methods: A total of 184 abstracts were analyzed with regard to adherence to the STARD criteria for abstracts as well as the reported modality, body region, pathology, and use cases. Results: Major topics of artificial intelligence abstracts were classification tasks in the abdomen, chest, and brain with CT being the most commonly used modality. Out of the 10 STARD for abstract criteria analyzed in the present study, on average, 5.32 (SD = 1.38) were reported by the 184 abstracts. Specifically, the highest adherence with STARD for abstracts was found for general interpretation of results of abstracts (100.0%, 184 of 184), clear study objectives (99.5%, 183 of 184), and estimates of diagnostic accuracy (96.2%, 177 of 184). The lowest STARD adherence was found for eligibility criteria for participants (9.2%, 17 of 184), type of study series (13.6%, 25 of 184), and implications for practice (20.7%, 44 of 184). There was no significant difference in the number of reported STARD criteria between abstracts accepted for oral presentation (M = 5.35, SD = 1.31) and abstracts accepted for the electronic poster session (M = 5.39, SD = 1.45) (p =.86). Conclusions: The adherence with STARD for abstract was low, indicating that providing authors with the related checklist may increase the quality of abstracts. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate whether a computed tomography (CT) radiomics-based machine learning classifier can predict histopathology of lymph nodes (LNs) after post-chemotherapy LN dissection (pcRPLND) in patients with metastatic non-seminomatous testicular germ cell tumors (NSTGCTs).Methods: Eighty patients with retroperitoneal LN metastases and contrast-enhanced CT were included into this retrospective study. Resected LNs were histopathologically classified into "benign" (necrosis/fibrosis) or "malignant" (viable tumor/teratoma). On CT imaging, 204 corresponding LNs were segmented and 97 radiomic features per LN were extracted after standardized image processing. The dataset was split into training, test, and validation sets. After stepwise feature reduction based on reproducibility, variable importance, and correlation analyses, a gradient-boosted tree was trained and tuned on the selected most important features using the training and test datasets. Model validation was performed on the independent validation dataset.Results: The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a misclassification of 8 of 36 benign LNs as malignant and 4 of 25 malignant LNs as benign (sensitivity 88%, specificity 72%, negative predictive value 88%). In contrast, a model containing only the LN volume resulted in a classification accuracy of 0.68 with 64% sensitivity and 68% specificity.Conclusions: CT radiomics represents an exciting new tool for improved prediction of the presence of malignant histopathology in retroperitoneal LN metastases from NSTGCTs, aiming at reducing overtreatment in this group of young patients. Thus, the presented approach should be combined with established clinical biomarkers and further validated in larger, prospective clinical trials.Key Points: • Patients with metastatic non-seminomatous testicular germ cell tumors undergoing post-chemotherapy retroperitoneal lymph node dissection of residual lesions show overtreatment in up to 50%. • We assessed whether a CT radiomics-based machine learning classifier can predict histopathology of lymph nodes after post-chemotherapy lymph node dissection. • The trained machine learning classifier achieved a classification accuracy of 0.81 in the validation dataset with a sensitivity of 88% and a specificity of 78%, thus allowing for prediction of the presence of viable tumor or teratoma in retroperitoneal lymph node metastases. [ABSTRACT FROM AUTHOR]

Objectives: Podcasts are audio recordings distributed via the Internet. We review the availability of podcasts on the topic of radiology. Methods: A search for podcasts relating to radiology was performed using search engines and free public websites that either hosted or distributed podcasts. Only English language podcast series were included, and video podcasts were excluded. Data was gathered by manually interrogating the metadata on the primary hosting platform and related websites. Results: Forty-one podcast series met the inclusion criteria. The earliest was from 2005. In total, 56.1% of podcasts were defined as active and 43.9% inactive at the time of publication. Number of episodes for each podcast series ranged from 1 to 269 with 56.1% of podcasts having ≤ 10 episodes. There was a wide variation in podcast series' frequency/schedules. The most common subject topic was 'radiology current affairs' (43.9%), with the least common 'exam revision' (7.3%) and 'radiography' (7.3%). The majority of podcasts were targeted at radiologists (87.8%) and originated from the USA (70.1%). Podcast hosts consisted of doctors (63.4%), other professionals (29.3%) or unknown (7.3%). Additional supplementary media or information as show notes were provided by 26.8% of radiology podcast series. Conclusions: This gives a new insight into the world of 'radiology podcasting'. To the authors' knowledge, this is the first review in the literature and highlights the increasing availability of podcasting in radiology. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the correlation between simple planimetric measurements in axial computed tomography (CT) slices and measurements of patient body composition and anthropometric data performed with bioelectrical impedance analysis (BIA) and metric clinical assessments.Methods: In this prospective cross-sectional study, we analyzed data of a cohort of 62 consecutive, untreated adult patients with advanced malignant melanoma who underwent concurrent BIA assessments at their radiologic baseline staging by CT between July 2016 and October 2017. To assess muscle and adipose tissue mass, we analyzed the areas of the paraspinal muscles as well as the cross-sectional total patient area in a single CT slice at the height of the third lumbar vertebra. These measurements were subsequently correlated with anthropometric (body weight) and body composition parameters derived from BIA (muscle mass, fat mass, fat-free mass, and visceral fat mass). Linear regression models were built to allow for estimation of each parameter based on CT measurements.Results: Linear regression models allowed for accurate prediction of patient body weight (adjusted R2 = 0.886), absolute muscle mass (adjusted R2 = 0.866), fat-free mass (adjusted R2 = 0.855), and total as well as visceral fat mass (adjusted R2 = 0.887 and 0.839, respectively).Conclusions: Our data suggest that patient body composition can accurately and quantitatively be determined by using simple measurements in a single axial CT slice. This could be useful in various medical and scientific settings, where the knowledge of the patient's anthropometric parameters is not immediately or easily available.Key Points: • Easy to perform measurements on a single CT slice highly correlate with clinically valuable parameters of body composition. • Body composition data were acquired using bioelectrical impedance analysis to correlate CT measurements with a non-imaging-based method, which is frequently lacking in previous studies. • The obtained equations facilitate a quick, opportunistic assessment of relevant parameters of body composition. [ABSTRACT FROM AUTHOR]

Objectives: To predict the main component of pure and mixed kidney stones using dual-energy computed tomography and machine learning.Methods: 200 kidney stones with a known composition as determined by infrared spectroscopy were examined using a non-anthropomorphic phantom on a spectral detector computed tomography scanner. Stones were of either pure (monocrystalline, n = 116) or compound (dicrystalline, n = 84) composition. Image acquisition was repeated twice using both, normal and low-dose protocols, respectively (ND/LD). Conventional images and low and high keV virtual monoenergetic images were reconstructed. Stones were semi-automatically segmented. A shallow neural network was trained using data from ND1 acquisition split into training (70%), testing (15%) and validation-datasets (15%). Performance for ND2 and both LD acquisitions was tested. Accuracy on a per-voxel and a per-stone basis was calculated.Results: Main components were: Whewellite (n = 80), weddellite (n = 21), Ca-phosphate (n = 39), cysteine (n = 20), struvite (n = 13), uric acid (n = 18) and xanthine stones (n = 9). Stone size ranged from 3 to 18 mm. Overall accuracy for predicting the main component on a per-voxel basis attained by ND testing dataset was 91.1%. On independently tested acquisitions, accuracy was 87.1-90.4%.Conclusions: Even in compound stones, the main component can be reliably determined using dual energy CT and machine learning, irrespective of dose protocol.Key Points: • Spectral Detector Dual Energy CT and Machine Learning allow for an accurate prediction of stone composition. • Ex-vivo study demonstrates the dose independent assessment of pure and compound stones. • Lowest accuracy is reported for compound stones with struvite as main component. [ABSTRACT FROM AUTHOR]

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