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Background: Acute abdominal pain (AAP) constitutes 5–10% of all emergency department (ED) visits, with appendicitis being a prevalent AAP etiology often necessitating surgical intervention. The variability in AAP symptoms and causes, combined with the challenge of identifying appendicitis, complicate timely intervention. To estimate the risk of appendicitis, scoring systems such as the Alvarado score have been developed. However, diagnostic errors and delays remain common. Although various machine learning (ML) models have been proposed to enhance appendicitis detection, none have been seamlessly integrated into the ED workflows for AAP or are specifically designed to diagnose appendicitis as early as possible within the clinical decision-making process. To mimic daily clinical practice, this proof-of-concept study aims to develop ML models that support decision-making using comprehensive clinical data up to key decision points in the ED workflow to detect appendicitis in patients presenting with AAP. Methods: Data from the Dutch triage system at the ED, vital signs, complete medical history and physical examination findings and routine laboratory test results were retrospectively extracted from 350 AAP patients presenting to the ED of a Dutch teaching hospital from 2016 to 2023. Two eXtreme Gradient Boosting ML models were developed to differentiate cases with appendicitis from other AAP causes: one model used all data up to and including physical examination, and the other was extended with routine laboratory test results. The performance of both models was evaluated on a validation set (n = 68) and compared to the Alvarado scoring system as well as three ED physicians in a reader study. Results: The ML models achieved AUROCs of 0.919 without laboratory test results and 0.923 with the addition of laboratory test results. The Alvarado scoring system attained an AUROC of 0.824. ED physicians achieved AUROCs of 0.894, 0.826, and 0.791 without laboratory test results, increasing to AUROCs of 0.923, 0.892, and 0.859 with laboratory test results. Conclusions: Both ML models demonstrated comparable high accuracy in predicting appendicitis in patients with AAP, outperforming the Alvarado scoring system. The ML models matched or surpassed ED physician performance in detecting appendicitis, with the largest potential performance gain observed in absence of laboratory test results. Integration could assist ED physicians in early and accurate diagnosis of appendicitis. [ABSTRACT FROM AUTHOR]

Introduction: This study investigates the performance of a commercially available artificial intelligence (AI) system to identify normal chest radiographs and its potential to reduce radiologist workload. Methods: Retrospective analysis included consecutive chest radiographs from two medical centers between Oct 1, 2016 and Oct 14, 2016. Exclusions comprised follow-up exams within the inclusion period, bedside radiographs, incomplete images, imported radiographs, and pediatric radiographs. Three chest radiologists categorized findings into normal, clinically irrelevant, clinically relevant, urgent, and critical. A commercial AI system processed all radiographs, scoring 10 chest abnormalities on a 0–100 confidence scale. AI system performance was evaluated using the area under the ROC curve (AUC), assessing the detection of normal radiographs. Sensitivity was calculated for the default and a conservative operating point. the detection of negative predictive value (NPV) for urgent and critical findings, as well as the potential workload reduction, was calculated. Results: A total of 2603 radiographs were acquired in 2141 unique patients. Post-exclusion, 1670 radiographs were analyzed. Categories included 479 normal, 332 clinically irrelevant, 339 clinically relevant, 501 urgent, and 19 critical findings. The AI system achieved an AUC of 0.92. Sensitivity for normal radiographs was 92% at default and 53% at the conservative operating point. At the conservative operating point, NPV was 98% for urgent and critical findings, and could result in a 15% workload reduction. Conclusion: A commercially available AI system effectively identifies normal chest radiographs and holds the potential to lessen radiologists' workload by omitting half of the normal exams from reporting. Clinical relevance statement: The AI system is able to detect half of all normal chest radiographs at a clinically acceptable operating point, thereby potentially reducing the workload for the radiologists by 15%. Key Points: The AI system reached an AUC of 0.92 for the detection of normal chest radiographs. Fifty-three percent of normal chest radiographs were identified with a NPV of 98% for urgent findings. AI can reduce the workload of chest radiography reporting by 15%. [ABSTRACT FROM AUTHOR]

Objectives: To develop and validate an artificial intelligence (AI) system for measuring and detecting signs of carpal instability on conventional radiographs. Materials and methods: Two case-control datasets of hand and wrist radiographs were retrospectively acquired at three hospitals (hospitals A, B, and C). Dataset 1 (2178 radiographs from 1993 patients, hospitals A and B, 2018–2019) was used for developing an AI system for measuring scapholunate (SL) joint distances, SL and capitolunate (CL) angles, and carpal arc interruptions. Dataset 2 (481 radiographs from 217 patients, hospital C, 2017–2021) was used for testing, and with a subsample (174 radiographs from 87 patients), an observer study was conducted to compare its performance to five clinicians. Evaluation metrics included mean absolute error (MAE), sensitivity, and specificity. Results: Dataset 2 included 258 SL distances, 189 SL angles, 191 CL angles, and 217 carpal arc labels obtained from 217 patients (mean age, 51 years ± 23 [standard deviation]; 133 women). The MAE in measuring SL distances, SL angles, and CL angles was respectively 0.65 mm (95%CI: 0.59, 0.72), 7.9 degrees (95%CI: 7.0, 8.9), and 5.9 degrees (95%CI: 5.2, 6.6). The sensitivity and specificity for detecting arc interruptions were 83% (95%CI: 74, 91) and 64% (95%CI: 56, 71). The measurements were largely comparable to those of the clinicians, while arc interruption detections were more accurate than those of most clinicians. Conclusion: This study demonstrates that a newly developed automated AI system accurately measures and detects signs of carpal instability on conventional radiographs. Clinical relevance statement: This system has the potential to improve detections of carpal arc interruptions and could be a promising tool for supporting clinicians in detecting carpal instability. Key Points: Artificial intelligence can accurately measure and detect signs of carpal instability on conventional radiographs. It measures scapholunate distances, scapholunate and capitolunate angles, and detects carpal arc interruptions. The measurement accuracy was largely comparable to five clinicians with different specialties, and the arc interruption detections were more accurate than most clinicians. All measurements and detections are explainable, and a new heat map technique was developed for visualizing the degree and location of carpal arc interruptions in the image. [ABSTRACT FROM AUTHOR]

Objectives: Severity of degenerative scoliosis (DS) is assessed by measuring the Cobb angle on anteroposterior radiographs. However, MRI images are often available to study the degenerative spine. This retrospective study aims to develop and evaluate the reliability of a novel automatic method that measures coronal Cobb angles on lumbar MRI in DS patients. Materials and methods: Vertebrae and intervertebral discs were automatically segmented using a 3D AI algorithm, trained on 447 lumbar MRI series. The segmentations were used to calculate all possible angles between the vertebral endplates, with the largest being the Cobb angle. The results were validated with 50 high-resolution sagittal lumbar MRI scans of DS patients, in which three experienced readers measured the Cobb angle. Reliability was determined using the intraclass correlation coefficient (ICC). Results: The ICCs between the readers ranged from 0.90 (95% CI 0.83–0.94) to 0.93 (95% CI 0.88–0.96). The ICC between the maximum angle found by the algorithm and the average manually measured Cobb angles was 0.83 (95% CI 0.71–0.90). In 9 out of the 50 cases (18%), all readers agreed on both vertebral levels for Cobb angle measurement. When using the algorithm to extract the angles at the vertebral levels chosen by the readers, the ICCs ranged from 0.92 (95% CI 0.87–0.96) to 0.97 (95% CI 0.94–0.98). Conclusion: The Cobb angle can be accurately measured on MRI using the newly developed algorithm in patients with DS. The readers failed to consistently choose the same vertebral level for Cobb angle measurement, whereas the automatic approach ensures the maximum angle is consistently measured. Clinical relevance statement: Our AI-based algorithm offers reliable Cobb angle measurement on routine MRI for degenerative scoliosis patients, potentially reducing the reliance on conventional radiographs, ensuring consistent assessments, and therefore improving patient care. Key Points: • While often available, MRI images are rarely utilized to determine the severity of degenerative scoliosis. • The presented MRI Cobb angle algorithm is more reliable than humans in patients with degenerative scoliosis. • Radiographic imaging for Cobb angle measurements is mitigated when lumbar MRI images are available. [ABSTRACT FROM AUTHOR]

This paper presents a large publicly available multi-center lumbar spine magnetic resonance imaging (MRI) dataset with reference segmentations of vertebrae, intervertebral discs (IVDs), and spinal canal. The dataset includes 447 sagittal T1 and T2 MRI series from 218 patients with a history of low back pain and was collected from four different hospitals. An iterative data annotation approach was used by training a segmentation algorithm on a small part of the dataset, enabling semi-automatic segmentation of the remaining images. The algorithm provided an initial segmentation, which was subsequently reviewed, manually corrected, and added to the training data. We provide reference performance values for this baseline algorithm and nnU-Net, which performed comparably. Performance values were computed on a sequestered set of 39 studies with 97 series, which were additionally used to set up a continuous segmentation challenge that allows for a fair comparison of different segmentation algorithms. This study may encourage wider collaboration in the field of spine segmentation and improve the diagnostic value of lumbar spine MRI. [ABSTRACT FROM AUTHOR]

Objectives: To map the clinical use of CE-marked artificial intelligence (AI)–based software in radiology departments in the Netherlands (n = 69) between 2020 and 2022. Materials and methods: Our AI network (one radiologist or AI representative per Dutch hospital organization) received a questionnaire each spring from 2020 to 2022 about AI product usage, financing, and obstacles to adoption. Products that were not listed on www.AIforRadiology.com by July 2022 were excluded from the analysis. Results: The number of respondents was 43 in 2020, 36 in 2021, and 33 in 2022. The number of departments using AI has been growing steadily (2020: 14, 2021: 19, 2022: 23). The diversity (2020: 7, 2021: 18, 2022: 34) and the number of total implementations (2020: 19, 2021: 38, 2022: 68) has rapidly increased. Seven implementations were discontinued in 2022. Four hospital organizations said to use an AI platform or marketplace for the deployment of AI solutions. AI is mostly used to support chest CT (17), neuro CT (17), and musculoskeletal radiograph (12) analysis. The budget for AI was reserved in 13 of the responding centers in both 2021 and 2022. The most important obstacles to the adoption of AI remained costs and IT integration. Of the respondents, 28% stated that the implemented AI products realized health improvement and 32% assumed both health improvement and cost savings. Conclusion: The adoption of AI products in radiology departments in the Netherlands is showing common signs of a developing market. The major obstacles to reaching widespread adoption are a lack of financial resources and IT integration difficulties. Clinical relevance statement: The clinical impact of AI starts with its adoption in daily clinical practice. Increased transparency around AI products being adopted, implementation obstacles, and impact may inspire increased collaboration and improved decision-making around the implementation and financing of AI products. Key Points: • The adoption of artificial intelligence products for radiology has steadily increased since 2020 to at least a third of the centers using AI in clinical practice in the Netherlands in 2022. • The main areas in which artificial intelligence products are used are lung nodule detection on CT, aided stroke diagnosis, and bone age prediction. • The majority of respondents experienced added value (decreased costs and/or improved outcomes) from using artificial intelligence–based software; however, major obstacles to adoption remain the costs and IT-related difficulties. [ABSTRACT FROM AUTHOR]

Objective: To study trends in the incidence of reported pulmonary nodules and stage I lung cancer in chest CT. Methods: We analyzed the trends in the incidence of detected pulmonary nodules and stage I lung cancer in chest CT scans in the period between 2008 and 2019. Imaging metadata and radiology reports from all chest CT studies were collected from two large Dutch hospitals. A natural language processing algorithm was developed to identify studies with any reported pulmonary nodule. Results: Between 2008 and 2019, a total of 74,803 patients underwent 166,688 chest CT examinations at both hospitals combined. During this period, the annual number of chest CT scans increased from 9955 scans in 6845 patients in 2008 to 20,476 scans in 13,286 patients in 2019. The proportion of patients in whom nodules (old or new) were reported increased from 38% (2595/6845) in 2008 to 50% (6654/13,286) in 2019. The proportion of patients in whom significant new nodules (≥ 5 mm) were reported increased from 9% (608/6954) in 2010 to 17% (1660/9883) in 2017. The number of patients with new nodules and corresponding stage I lung cancer diagnosis tripled and their proportion doubled, from 0.4% (26/6954) in 2010 to 0.8% (78/9883) in 2017. Conclusion: The identification of incidental pulmonary nodules in chest CT has steadily increased over the past decade and has been accompanied by more stage I lung cancer diagnoses. Clinical relevance statement: These findings stress the importance of identifying and efficiently managing incidental pulmonary nodules in routine clinical practice. Key Points: • The number of patients who underwent chest CT examinations substantially increased over the past decade, as did the number of patients in whom pulmonary nodules were identified. • The increased use of chest CT and more frequently identified pulmonary nodules were associated with more stage I lung cancer diagnoses. [ABSTRACT FROM AUTHOR]

Objectives: We evaluated whether lesion‐to‐fat ratio measured by shear wave elastography in patients with Breast Imaging Reporting and Data System (BI‐RADS) 3 or 4 lesions has the potential to further refine the assessment of B‐mode ultrasound alone in breast cancer diagnostics. Methods: This was a secondary analysis of an international diagnostic multicenter trial (NCT02638935). Data from 1288 women with breast lesions categorized as BI‐RADS 3 and 4a–c by conventional B‐mode ultrasound were analyzed, whereby the focus was placed on differentiating lesions categorized as BI‐RADS 3 and BI‐RADS 4a. All women underwent shear wave elastography and histopathologic evaluation functioning as reference standard. Reduction of benign biopsies as well as the number of missed malignancies after reclassification using lesion‐to‐fat ratio measured by shear wave elastography were evaluated. Results: Breast cancer was diagnosed in 368 (28.6%) of 1288 lesions. The assessment with conventional B‐mode ultrasound resulted in 53.8% (495 of 1288) pathologically benign lesions categorized as BI‐RADS 4 and therefore false positives as well as in 1.39% (6 of 431) undetected malignancies categorized as BI‐RADS 3. Additional lesion‐to‐fat ratio in BI‐RADS 4a lesions with a cutoff value of 1.85 resulted in 30.11% biopsies of benign lesions which correspond to a reduction of 44.04% of false positives. Conclusions: Adding lesion‐to‐fat ratio measured by shear wave elastography to conventional B‐mode ultrasound in BI‐RADS 4a breast lesions could help reduce the number of benign biopsies by 44.04%. At the same time, however, 1.98% of malignancies were missed, which would still be in line with American College of Radiology BI‐RADS 3 definition of <2% of undetected malignancies. [ABSTRACT FROM AUTHOR]

Objectives: To assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs. Methods: Four datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen's kappa coefficient (κ), fracture localization precision, and reading time. Results: The algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists' mean], p ≥.05). AI assistance improved five out of ten pairs of inter-observer Cohen's κ agreements (p <.05) and reduced reading time in four radiologists (p <.001), but did not improve other metrics in the majority of radiologists (p ≥.05). Conclusions: The AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time. Key Points: • An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists. [ABSTRACT FROM AUTHOR]

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement. [ABSTRACT FROM AUTHOR]

Objectives: Over 2500 percutaneous transhepatic cholangiography and biliary drainage (PTCD) procedures are yearly performed in the Netherlands. Most interventions are performed for treatment of biliary obstruction following unsuccessful endoscopic biliary cannulation. Our aim was to evaluate complication rates and risk factors for complications in PTCD patients after failed ERCP. Methods: We performed an observational study collecting data from a cohort that was subjected to PTCD during a 5-year period in one academic and four teaching hospitals. Primary objective was the development of infectious (sepsis, cholangitis, abscess, or cholecystitis) and non-infectious complications (bile leakage, severe hemorrhage, etc.) and mortality within 30 days of the procedure. Subsequently, risk factors for complications and mortality were analyzed with a multilevel logistic regression analysis. Results: A total of 331 patients underwent PTCD of whom 205 (61.9%) developed PTCD-related complications. Of the 224 patients without a pre-existent infection, 91 (40.6%) developed infectious complications, i.e., cholangitis in 26.3%, sepsis in 24.6%, abscess formation in 2.7%, and cholecystitis in 1.3%. Non-infectious complications developed in 114 of 331 patients (34.4%). 30-day mortality was 17.2% (N = 57). Risk factors for infectious complications included internal drainage and drain obstruction, while multiple re-interventions were a risk factor for non-infectious complications. Conclusion: Both infectious and non-infectious complications are frequent after PTCD, most often due to biliary drain obstruction. [ABSTRACT FROM AUTHOR]

Objectives: AI-based algorithms for medical image analysis showed comparable performance to human image readers. However, in practice, diagnoses are made using multiple imaging modalities alongside other data sources. We determined the importance of this multi-modal information and compared the diagnostic performance of routine breast cancer diagnosis to breast ultrasound interpretations by humans or AI-based algorithms.Methods: Patients were recruited as part of a multicenter trial (NCT02638935). The trial enrolled 1288 women undergoing routine breast cancer diagnosis (multi-modal imaging, demographic, and clinical information). Three physicians specialized in ultrasound diagnosis performed a second read of all ultrasound images. We used data from 11 of 12 study sites to develop two machine learning (ML) algorithms using unimodal information (ultrasound features generated by the ultrasound experts) to classify breast masses which were validated on the remaining study site. The same ML algorithms were subsequently developed and validated on multi-modal information (clinical and demographic information plus ultrasound features). We assessed performance using area under the curve (AUC).Results: Of 1288 breast masses, 368 (28.6%) were histopathologically malignant. In the external validation set (n = 373), the performance of the two unimodal ultrasound ML algorithms (AUC 0.83 and 0.82) was commensurate with performance of the human ultrasound experts (AUC 0.82 to 0.84; p for all comparisons > 0.05). The multi-modal ultrasound ML algorithms performed significantly better (AUC 0.90 and 0.89) but were statistically inferior to routine breast cancer diagnosis (AUC 0.95, p for all comparisons ≤ 0.05).Conclusions: The performance of humans and AI-based algorithms improves with multi-modal information.Key Points: • The performance of humans and AI-based algorithms improves with multi-modal information. • Multimodal AI-based algorithms do not necessarily outperform expert humans. • Unimodal AI-based algorithms do not represent optimal performance to classify breast masses. [ABSTRACT FROM AUTHOR]

Background: Limited evidence is available on the clinical impact of artificial intelligence (AI) in radiology. Early health technology assessment (HTA) is a methodology to assess the potential value of an innovation at an early stage. We use early HTA to evaluate the potential value of AI software in radiology. As a use-case, we evaluate the cost-effectiveness of AI software aiding the detection of intracranial large vessel occlusions (LVO) in stroke in comparison to standard care. We used a Markov based model from a societal perspective of the United Kingdom predominantly using stroke registry data complemented with pooled outcome data from large, randomized trials. Different scenarios were explored by varying missed diagnoses of LVOs, AI costs and AI performance. Other input parameters were varied to demonstrate model robustness. Results were reported in expected incremental costs (IC) and effects (IE) expressed in quality adjusted life years (QALYs). Results: Applying the base case assumptions (6% missed diagnoses of LVOs by clinicians, $40 per AI analysis, 50% reduction of missed LVOs by AI), resulted in cost-savings and incremental QALYs over the projected lifetime (IC: − $156, − 0.23%; IE: + 0.01 QALYs, + 0.07%) per suspected ischemic stroke patient. For each yearly cohort of patients in the UK this translates to a total cost saving of $11 million. Conclusions: AI tools for LVO detection in emergency care have the potential to improve healthcare outcomes and save costs. We demonstrate how early HTA may be applied for the evaluation of clinically applied AI software for radiology. [ABSTRACT FROM AUTHOR]

In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laboratory tests or chest imaging may be available, DFCN is designed to be robust to missing input data. An ablation study extensively evaluates the performance benefits of the DFCN as well as its robustness to missing inputs. Data from 1088 patients with confirmed RT-PCR results are obtained from two independent medical facilities. The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model. Training and test datasets are taken from different medical facilities. Data is made publicly available. The performance of DFCN in predicting the RT-PCR result is compared with 3 related architectures as well as a Random Forest baseline. All models are trained with varying levels of masked input data to encourage robustness to missing inputs. Missing data is simulated at test time by masking inputs randomly. DFCN outperforms all other models with statistical significance using random subsets of input data with 2-27 available inputs. When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any other model. Furthermore, with clinically meaningful subsets of parameters consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than any other model, with values of 0.909 and 0.919. [ABSTRACT FROM AUTHOR]

Objectives: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence.Methods: We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications ( www.aiforradiology.com ). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy.Results: The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor.Conclusions: Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact.Key Points: • Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. • Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. • There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology. [ABSTRACT FROM AUTHOR]

No official data exist on the status of musculoskeletal (MSK) radiology in Europe. The Committee for National Societies conducted an international survey to understand the status of training, subspecialization, and local practice among the European Society of Musculoskeletal Radiology (ESSR) partner societies. This article reports the results of that survey. An online questionnaire was distributed to all 26 European national associations that act as official partner societies of the ESSR. The 24 questions were subdivided into six sections: society structure, relationship with the national radiological society, subspecialization, present radiology practice, MSK interventional procedures, and MSK ultrasound. The findings of our study show a lack of standardized training and/or accreditation methods in the field of MSK radiology at a national level. The European diploma in musculoskeletal radiology is directed to partly overcome this problem; however, this certification is still underrecognized. Using certification methods, a more homogeneous European landscape could be created in the future with a view to subspecialist training. MSK ultrasound and MSK interventional procedures should be performed by a health professional with a solid knowledge of the relevant imaging modalities and sufficient training in MSK radiology. Recognition of MSK radiology as an official subspecialty would make the field more attractive for younger colleagues as well as attracting the brightest and best, an important key to further development of both clinical and academic radiology. KEY POINTS: · Standardized training and/or accreditation methods in the field of MSK radiology is lacking at a national level.. · With certification methods, such as the European diploma in musculoskeletal radiology, a more homogeneous European landscape could be created in the future with a view to subspecialist training.. · Recognition of MSK radiology as an official subspecialty would make the field more attractive for younger colleagues as well as attracting the brightest and best, an important key to further development of both clinical and academic radiology.. [ABSTRACT FROM AUTHOR]

Objective: To evaluate trends and patterns in CT usage among children (aged 0-17 years) in The Netherlands during the period 1990-2012.Methods: Lists of electronically archived paediatric CT scans were requested from the Radiology Information Systems (RIS) of Dutch hospitals which reported >10 paediatric CT scans annually in a survey conducted in 2010. Data included patient identification, birth date, gender, scan date and body part scanned. For non-participating hospitals and for years prior to electronic archiving in some participating hospitals, data were imputed by calendar year and hospital type (academic, general with <500 beds, general with ≥ 500 beds).Results: Based on 236,066 CT scans among 146,368 patients performed between 1990 and 2012, estimated annual numbers of paediatric CT scans in The Netherlands increased from 7,731 in 1990 to 26,023 in 2012. More than 70 % of all scans were of the head and neck. During the last decade, substantial increases of more than 5 % per year were observed in general hospitals with fewer than 500 beds and among children aged 10 years or older.Conclusion: The estimated number of paediatric CT scans has more than tripled in The Netherlands during the last two decades.Key Points: • Paediatric CT in The Netherlands has tripled during the last two decades. • The number of paediatric CTs increased through 2012 in general hospitals. • Paediatric CTs continued to increase among children aged 10 years or older. [ABSTRACT FROM AUTHOR]

Between January 1, 2011, and December 31, 2016, we studied the incidence, management and outcome of high‐risk breast lesions in a consecutive series of 376,519 screens of women who received biennial screening mammography. During the 6‐year period covered by the study, the proportion of women who underwent core needle biopsy (CNB) after recall remained fairly stable, ranging from 39.2% to 48.1% (mean: 44.2%, 5,212/11,783), whereas the proportion of high‐risk lesions at CNB (i.e., flat epithelial atypia, atypical ductal hyperplasia, lobular carcinoma in situ and papillary lesions) gradually increased from 3.2% (25/775) in 2011 to 9.5% (86/901) in 2016 (p < 0.001). The mean proportion of high‐risk lesions at CNB that were subsequently treated with diagnostic surgical excision was 51.4% (169/329) and varied between 41.0% and 64.3% through the years, but the excision rate for high‐risk lesions per 1,000 screens and per 100 recalls increased from 0.25 (2011) to 0.70 (2016; p < 0.001) and from 0.81 (2011) to 2.50 (2016; p < 0.001), respectively. The proportion of all diagnostic surgical excisions showing in situ or invasive breast cancer was 29.0% (49/169) and varied from 22.2% (8/36) in 2014 to 38.5% (5/13) in 2011. In conclusion, the proportion of high‐risk lesions at CNB tripled in a 6‐year period, with a concomitant increased excision rate for these lesions. As the proportion of surgical excisions showing in situ or invasive breast cancer did not increase, a rising number of screened women underwent invasive surgical excision with benign outcome. What's new? Screening mammography aims to catch breast cancer early to reduce associated morbidity and mortality. Women with suspect findings at mammography frequently are recalled for further testing with core needle biopsy (CNB). In this investigation, the proportion of high‐risk lesions detected at CNB was found to have tripled among women in the Netherlands who underwent mammographic screening between 2011 and 2016. This increase was accompanied by an increase in lesion excision rates. Of excised lesions, little more than 14% proved to be malignant at two‐year follow‐up. The remainder of lesions exhibited benign pathology, suggesting that many women underwent potentially unnecessary surgery. [ABSTRACT FROM AUTHOR]

Objectives: To determine the effect of computer-aided-detection (CAD) software for automated breast ultrasound (ABUS) on reading time (RT) and performance in screening for breast cancer.Material and Methods: Unilateral ABUS examinations of 120 women with dense breasts were randomly selected from a multi-institutional archive of cases including 30 malignant (20/30 mammography-occult), 30 benign, and 60 normal cases with histopathological verification or ≥ 2 years of negative follow-up. Eight radiologists read once with (CAD-ABUS) and once without CAD (ABUS) with > 8 weeks between reading sessions. Readers provided a BI-RADS score and a level of suspiciousness (0-100). RT, sensitivity, specificity, PPV and area under the curve (AUC) were compared.Results: Average RT was significantly shorter using CAD-ABUS (133.4 s/case, 95% CI 129.2-137.6) compared with ABUS (158.3 s/case, 95% CI 153.0-163.3) (p < 0.001). Sensitivity was 0.84 for CAD-ABUS (95% CI 0.79-0.89) and ABUS (95% CI 0.78-0.88) (p = 0.90). Three out of eight readers showed significantly higher specificity using CAD. Pooled specificity (0.71, 95% CI 0.68-0.75 vs. 0.67, 95% CI 0.64-0.70, p = 0.08) and PPV (0.50, 95% CI 0.45-0.55 vs. 0.44, 95% CI 0.39-0.49, p = 0.07) were higher in CAD-ABUS vs. ABUS, respectively, albeit not significantly. Pooled AUC for CAD-ABUS was comparable with ABUS (0.82 vs. 0.83, p = 0.53, respectively).Conclusion: CAD software for ABUS may decrease the time needed to screen for breast cancer without compromising the screening performance of radiologists.Key Points: • ABUS with CAD software may speed up reading time without compromising radiologists' accuracy. • CAD software for ABUS might prevent non-detection of malignant breast lesions by radiologists. • Radiologists reading ABUS with CAD software might improve their specificity without losing sensitivity. [ABSTRACT FROM AUTHOR]

Background: To determine the proportion of "true" interval cancers and tumor characteristics of interval breast cancers prior to, during and after the transition from screen-film mammography screening (SFM) to full-field digital mammography screening (FFDM).Methods: We included all women with interval cancers detected between January 2006 and January 2014. Breast imaging reports, biopsy results and breast surgery reports of all women recalled at screening mammography and of all women with interval breast cancers were collected. Two experienced screening radiologists reviewed the diagnostic mammograms, on which the interval cancers were diagnosed, as well as the prior screening mammograms and determined whether or not the interval cancer had been missed on the most recent screening mammogram. If not missed, the cancer was considered an occult ("true") interval cancer.Results: A total of 442 interval cancers had been diagnosed, of which 144 at SFM with a prior SFM (SFM-SFM), 159 at FFDM with a prior SFM (FFDM-SFM) and 139 at FFDM with a prior FFDM (FFDM-FFDM). The transition from SFM to FFDM screening resulted in the diagnosis of more occult ("true") interval cancers at FFDM-SFM than at SFM-SFM (65.4% (104/159) versus 49.3% (71/144), P < 0.01), but this increase was no longer statistically significant in women who had been screened digitally for the second time (57.6% (80/139) at FFDM-FFDM versus 49.3% (71/144) at SFM-SFM). Tumor characteristics were comparable for the three interval cancer cohorts, except of a lower porportion (75.7 and 78.0% versus 67.2% af FFDM-FFDM, P < 0.05) of invasive ductal cancers at FFDM with prior FFDM.Conclusions: An increase in the proportion of occult interval cancers is observed during the transition from SFM to FFDM screening mammography. However, this increase seems temporary and is no longer detectable after the second round of digital screening. Tumor characteristics and type of surgery are comparable for interval cancers detected prior to, during and after the transition from SFM to FFDM screening mammography, except of a lower proportion of invasive ductal cancers after the transition. [ABSTRACT FROM AUTHOR]

We present a case report and literature review of the ultrasound (US) and magentic resonance imaging (MRI) features of an intratendinous ganglion originating from the long head of the biceps tendon. Intratendinous ganglia are very rare entities and intratendinous ganglion of the long head of the biceps tendon has only been described once. To the best of our knowledge, this is the first case report presenting the sonographic features of an intratendinous ganglion originating from the long head of the biceps tendon. [ABSTRACT FROM AUTHOR]

Purpose: To prospectively assess the frequency of abnormal sonographic findings in patients with posttraumatic shoulder pain and/or disability in whom ultrasound (US) was not considered and to assess the effect of sonographic findings on working diagnosis and therapeutic strategy, to analyze the possible role of US in the diagnostic workup of these patients. Methods: A survey was performed under general practitioners and orthopedic surgeons. They were requested to refer patients with persistent posttraumatic complaints for an US examination of the shoulder and to fill out a questionnaire concerning working diagnosis and therapy. In 50 patients examinations were performed separately by two radiologists. Findings were confirmed with additional radiographs and/or MRI and/or surgery. Four weeks after the US examination, the survey was repeated to inquire about changes in diagnosis and/or treatment that resulted from US. Results: US showed relevant pathology in 45 (90%) of 50 patients, a proximal humerus fracture in 25 (50%) patients, and a rotator cuff tear in 43 (86%) patients. Twenty-three (92%) fractures were accompanied by a rotator cuff tear, and 23 (54%) rotator cuff tears were accompanied by a fracture. Ten fractures were initially missed radiographically. US findings changed the working diagnosis and therapeutic strategy in 37 (74%) and 26 (52%) patients, respectively. Conclusion: In patients with posttraumatic shoulder complaints, US showed a high rate (90%) of relevant pathology. This changed the initial working diagnosis in 74% of the patients and the therapeutic strategy in more than half of the patients. Active referral for US examination may identify these abnormalities in an earlier phase and improve clinical outcome. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the need for additional magnetic resonance imaging (MRI) following ultrasound (US) in patients with shoulder pain and/or disability and to compare the accuracy of both techniques for the detection of partial-thickness and full-thickness rotator cuff tears (RCT).Methods: In 4 years, 5,216 patients underwent US by experienced musculoskeletal radiologists. Retrospectively, patient records were evaluated if MRI and surgery were performed within 5 months of US. US and MRI findings were classified into intact cuff, partial-thickness and full-thickness RCT, and were correlated with surgical findings.Results: Additional MR imaging was performed in 275 (5.2%) patients. Sixty-eight patients underwent surgery within 5 months. US and MRI correctly depicted 21 (95%) and 22 (100%) of the 22 full-thickness tears, and 8 (89%) and 6 (67%) of the 9 partial-thickness tears, respectively. The differences in performance of US and MRI were not statistically significant (p = 0.15).Conclusions: MRI following routine shoulder US was requested in only 5.2% of the patients. The additional value of MRI was in detecting intra-articular lesions. In patients who underwent surgery, US and MRI yielded comparably high sensitivity for detecting full-thickness RCT. US performed better in detecting partial-thickness tears, although the difference was not significant. [ABSTRACT FROM AUTHOR]

Background: Little is known about the efficacy and safety of renal artery stenting in patients with atherosclerotic renal artery stenosis (ARAS) and impaired renal function. Objective: To determine the efficacy and safety of stent placement in patients with ARAS and impaired renal function. Design: Randomized clinical trial. Randomization was centralized and computer generated, and allocation was assigned by e-mail. Patients, providers, and persons who assessed outcomes were not blinded to treatment assignment. Setting: 10 European medical centers. Participants: 140 patients with creatinine clearance less than 80 mL/min per 1.73 m2 and ARAS of 50% or greater. Intervention: Stent placement and medical treatment (64 patients) or medical treatment only (76 patients). Medical treatment consisted of antihypertensive treatment, a statin, and aspirin. Measurements: The primary end point was a 20% or greater decrease in creatinine clearance. Secondary end points included safety and cardiovascular morbidity and mortality. Results: Forty-six of 64 patients assigned to stent placement had the procedure. Ten of the 64 patients (16%) in the stent placement group and 16 patients (22%) in the medication group reached the primary end point (hazard ratio, 0.73 [95% CI, 0.33 to 1.61]). Serious complications occurred in the stent group, including 2 procedure-related deaths (3%), 1 late death secondary to an infected hematoma, and 1 patient who required dialysis secondary to cholesterol embolism. The groups did not differ for other secondary end points. Limitation: Many patients were falsely identified as having renal artery stenosis greater than 50% by noninvasive imaging and did not ultimately require stenting. Conclusion: Stent placement with medical treatment had no clear effect on progression of impaired renal function but led to a small number of significant procedure-related complications. The study findings favor a conservative approach to patients with ARAS, focused on cardiovascular risk factor management and avoiding stenting. [ABSTRACT FROM AUTHOR]

To assess the variability in accuracy of contrast media introduction, leakage, required time and patient discomfort in four different centres, each using a different image-guided glenohumeral injection technique. Each centre included 25 consecutive patients. The ultrasound-guided anterior (USa) and posterior approach (USp), fluoroscopic-guided anterior (FLa) and posterior (FLp) approach were used. Number of injection attempts, effect of contrast leakage on diagnostic quality, and total room, radiologist and procedure times were measured. Pain was documented with a visual analogue scale (VAS) pain score. Access to the joint was achieved in all patients. A successful first attempt significantly occurred more often with US (94%) than with fluoroscopic guidance (72%). Leakage of contrast medium did not cause interpretative difficulties. With US guidance mean room, procedure and radiologist times were significantly shorter (p < 0.001). The USa approach was rated with the lowest pre- and post-injection VAS scores. The four image-guided injection techniques are successful in injection of contrast material into the glenohumeral joint. US-guided injections and especially the anterior approach are significantly less time consuming, more successful on the first attempt, cause less patient discomfort and obviate the need for radiation and iodine contrast. [ABSTRACT FROM AUTHOR]

Background/Aim: Peptic ulcers are the most common cause of acute upper gastrointestinal bleedings in the digestive tract. Most patients are poor surgical candidates. The aim was to describe the efficacy of embolization as the treatment of choice for bleeding peptic ulcers in high-risk patients when endoscopic treatment failed. Methods: All patients who underwent a selective embolization of branches of the superior mesenteric artery and/or branches of the gastroduodenal artery for a bleeding peptic ulcer in the period January 2004 until December 2007 were included in this retrospective descriptive study. We examined the known risk factors for surgery and mortality in upper gastrointestinal bleeding due to peptic ulcers and describe the clinical course and outcome. Primary endpoints were the primary technical success and the clinical success rates. The secondary endpoint was the 30-day mortality. Results: 16 patients were included. All patients had at least two risk factors for surgery and mortality. The clinical success rate was 81% (13/16). The first embolization failed in 3 patients, 1 was successful re-embolized and 2 were operated upon without re-embolization. The primary technical success rate, i.e. bleedings controlled by radiologic intervention, was 88% (14/16). 6 patients died due to non-embolization-related problems; 5 of them developed upper gastrointestinal bleeding during a stay in the hospital. Conclusion: Embolization was a successful, minimal invasive alternative for surgical intervention in high-risk patients with upper gastrointestinal bleeding after failure of endoscopic treatment. Copyright © 2009 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Background Blind injection of the subacromial-sub-deltoid bursa (SSB) for diagnostic purposes (Neer test) or therapeutic purposes (corticosteroid therapy) is frequently used. Poor response to previous blind injection or side effects may be due to a misplaced injection. It is assumed that ultrasound (US)-guided injections are more accurate than blind injections. In a randomized study, we compared the accuracy of blind injection to that of US-guided injection into the SSB. Patients and methods 20 consecutive patients with impingement syndrome of the shoulder were randomized for blind or US-guided injection in the SSB. Injection was performed either by an experienced orthopedic surgeon or by an experienced musculoskeletal radiologist. A mixture of 1 m'L methylprednisolone acetate, 4 mL prilocaine hydrochloride and 0.02 mL (0.01 mmol) Gadolinium DTPA was injected. Immediately after injection, a 3D-gradient T1-weighted magnetic resonance scan of the shoulder was performed. The location of the injected fluid was independently assessed by 2 radiologists who were blinded as to the injection technique used. Results The accuracy of blind and US-guided injection was the same. The fluid was injected into the bursa in all cases. Interpretation Blind injection into the SSB is as reliable as US-guided injection and could therefore be used in daily routine. US-guided injections may offer a useful alternative in difficult cases, such as with changed anatomy postoperatively or when there is no effective clinical outcome. [ABSTRACT FROM AUTHOR]

The aim of this study was to describe a new sonographic sign of bone fracture and to determine if it can be helpful in decreasing the number of missed fractures of the proximal humerus. Ultrasound (US) of the shoulder was performed in 57 consecutive patients with shoulder pain and/or disability following trauma. All cases were prospectively reviewed for the presence of a humeral fracture. Sonographic signs of fractures, with special emphasis on what was termed the 'double line sign' (DLS), were assessed. Plain radiography was considered the standard of reference and in equivocal cases magnetic resonance imaging (MRI). Twenty-eight patients had a tuberosity complex fracture, which were all detected at US examination. Sonographic features of a fracture were periosteal elevation, corticol bone discontinuity, step-off deformity or a combination of these findings. This study showed that in 26 (93%) patients an additional sonographic feature, a DLS, could be demonstrated. The DLS is a helpful and probably reliable sonographic sign to indicate a humeral fracture. High-spatial-resolution US substantially increases the detection of fractures of the proximal humerus and should be considered as an alternative diagnostic tool prior to computed tomography (CT), MRI and arthroscopy in patients with persisting shoulder pain and/or disability following trauma. [ABSTRACT FROM AUTHOR]

Background: Chest radiography (CXR) is frequently performed in Western societies. There is insufficient knowledge of its diagnostic value in terms of changes in patient management decisions in primary care. Aim: To assess the influence of CXR on patient management in general practice. Design Of Study: Prospective cohort study. Setting: Seventy-eight GPs and three general hospitals in the Netherlands. Method: Patients (n = 792) aged > or =18 years referred by their GPs for CXR were included. The main outcome was change in patient management assessed by means of questionnaires filled in by GPs before and after CXR. Results: Mean age of the patients was 57.3+/-16.2 years and 53% were male. Clinically relevant abnormalities were found in 24% of the CXRs. Patient management changed in 60% of the patients following CXR. Main changes included: fewer referrals to a medical specialist (from 26 to 12%); reduction in initiation or change in therapy (from 24 to 15%); and more frequent reassurance (from 25 to 46%). However, this reassurance was not perceived as such in a quarter of these patients. A change in patient management occurred significantly more frequently in patients with complaints of cough (67%), those who exhibited abnormalities during physical examination (69%), or those with a suspected diagnosis of pneumonia (68%). Conclusion: Patient management by the GP changed in 60% of patients following CXR. CXR substantially reduced the number of referrals and initiation or change in therapy, and more patients were reassured by their GP. Thus, CXR is an important diagnostic tool for GPs and seems a cost-effective diagnostic test. [ABSTRACT FROM AUTHOR]

Background Chest radiography (CXR) is frequently performed in Western societies. There is insufficient knowledge of its diagnostic value in terms of changes in patient management decisions in patient care. Aim To assess the influence of CXR on patient management in general practice. Design of study Prospective cohort study. Setting Seventy-eight GPs and three general hospitals in the Netherlands. Method Patients (n = 792) aged ≥18 years referred by their GPs for CXR were included. The main outcome was change in patient management assessed by means of questionnaires filled in by GPs before and after CXR. Results Mean age of the patients was 57.3±16.2 years and 53% were male. Clinically relevant abnormalities were found in 24% of the CXRs. Patient management changed in 60% of the patients following CXR. Main changes included: fewer referrals to a medical specialist (from 26 to 12%); reduction in initiation or change in therapy (from 24 to 15%); and more frequent reassurance (from 25 to 46%). However, this reassurance was not perceived as such in a quarter of these patients. A change in patient management occurred significantly more frequently in patients with complaints of cough (67%), those who exhibited abnormalities during physical examination (69%), or those with a suspected diagnosis of pneumonia (68%). Conclusion Patient management by the GP changed in 60% of patients following CXR. CXR substantially reduced the number of referrals and initiation or change in therapy. and more patients were reassured by their GP. Thus, CXR is an important diagnostic tool for GPs and seems a cost-effective diagnostic test. [ABSTRACT FROM AUTHOR]

A 15-year-old girl was admitted with a high fever and progressive malaise, vomiting, anorexia and abdominal complaints. She previously had a sore throat and unilateral painful swollen lymph nodes in the neck. Laboratory investigation indicated a bacterial infection. Blood cultures were taken. There was infiltrate in the left lung. Pneumonia complicated with sepsis was suspected and ceftriaxone was started. The patient's condition deteriorated. Computed tomography (CT) scan showed progressive infiltrates in both lungs and pleural effusion. Blood culture led to the diagnosis. After changing antibiotics the patient slowly improved. Antibiotics were continued for several weeks. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 41-year-old man suffering from congenital antithrombin (AT)-III deficiency who under went endovascular stenting for the treatment of benign superior vena cava thrombosis.

A letter to the editor is presented in response to the article "Treatment of breast cancer in older women" by D. Bernardi, D. Errante, E. Galligioni, Crivellari, A. Bianco, and L. Salvagno.