Consensus on technical procedures in radiology to include in simulation-based training for residents: a European-wide needs assessment.

OBJECTIVES: To identify and prioritize technical procedures for simulation-based training that should be part of the education of residents in radiology. METHODS: This European-wide needs assessment study used a modified Delphi technique to gather consensus from different key education stakeholders in the field. The first round was a brainstorming phase to identify all procedures that a newly specialized radiologist should potentially be able to do. In the second round, each procedure was explored for the need for simulation training; the participants determined frequency, number of radiologists performing the procedure, impact on patient comfort and safety, and feasibility of simulation. The result of this round was sent back to the participants for final evaluation and prioritization. RESULTS: Seventy-one key education stakeholders from 27 European countries agreed to participate and were actively involved in the Delphi process: response rates were 72% and 82% in the second and third round, respectively. From 831 suggested procedures in the first round, these were grouped and categorized into 34 procedures that were pre-prioritized in the second round according to the need for simulation-based training. In the third round, 8 procedures were eliminated resulting in final inclusion of 26 procedures. Ultrasound procedures were highly ranked including basic skills such as probe handling; abdominal ultrasound; and ultrasound of kidneys, retroperitoneum, intestines, and scrotum. CONCLUSION: The prioritized list of procedures represents a consensus document decided upon by educational stakeholders in radiology across Europe. These procedures are suitable for simulation and should be an integral part of the education of radiologists. KEY POINTS: • The 26 identified procedures are listed according to priority and should be included as an integral part of simulation-based training curricula of radiologists across Europe. • This needs assessment is only the first step towards developing standardized simulation-based training programs that support the harmonization of education and training across Europe.

Simulator training improves ultrasound scanning performance on patients: a randomized controlled trial.

BACKGROUND: Simulation-based mastery training may improve clinical performance. The aim of this study was to determine the effect of simulation-based mastery training on clinical performance in abdominal diagnostic ultrasound for radiology residents. METHOD: This study was a multicenter randomized controlled trial registered at clinicaltrials.gov (identifier: NCT02921867) and reported using the Consolidated Standards of Reporting Trials (CONSORT) statement. Twenty radiology residents from 10 different hospitals were included in the study. Participants were randomized into two groups: (1) simulator-based training until passing a validated test scored by a blinded reviewer or (2) no intervention prior to standard clinical ultrasound training on patients. All scans performed during the first 6 weeks of clinical ultrasound training were scored. The primary outcome was performance scores assessed using Objective Structured Assessment of Ultrasound Skills (OSAUS). An exponential learning curve was fitted for the OSAUS score for the two groups using non-linear regression with random variation. Confidence intervals were calculated based on the variation between individual learning curves. RESULTS: After randomization, eleven residents completed the simulation intervention and nine received standard clinical training. The simulation group participants attended two to seven training sessions using between 6 and 17 h of simulation-based training. The performance score for the simulation group was significantly higher for the first 29 scans compared to that for the non-simulation group, such that scores reached approximately the same level after 49 and 77 scans, respectively. CONCLUSION: We showed improved performance in diagnostic ultrasound scanning on patients after simulation-based mastery learning for radiology residents. TRIAL REGISTRATION: NCT02921867 KEY POINTS: • Improvement in scanning performance on patients is seen after simulation-based mastery learning in diagnostic abdominal ultrasound. • Simulation-based mastery learning can prevent patients from bearing the burden of the initial steep part of trainees' learning curve.

The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications: Update 2018.

This manuscript describes the use of ultrasound elastography, with the exception of liver applications, and represents an update of the 2013 EFSUMB (European Federation of Societies for Ultrasound in Medicine and Biology) Guidelines and Recommendations on the clinical use of elastography.

Determining procedures for simulation-based training in radiology: a nationwide needs assessment.

OBJECTIVES: New training modalities such as simulation are widely accepted in radiology; however, development of effective simulation-based training programs is challenging. They are often unstructured and based on convenience or coincidence. The study objective was to perform a nationwide needs assessment to identify and prioritize technical procedures that should be included in a simulation-based curriculum. METHODS: A needs assessment using the Delphi method was completed among 91 key leaders in radiology. Round 1 identified technical procedures that radiologists should learn. Round 2 explored frequency of procedure, number of radiologists performing the procedure, risk and/or discomfort for patients, and feasibility for simulation. Round 3 was elimination and prioritization of procedures. RESULTS: Response rates were 67 %, 70 % and 66 %, respectively. In Round 1, 22 technical procedures were included. Round 2 resulted in pre-prioritization of procedures. In round 3, 13 procedures were included in the final prioritized list. The three highly prioritized procedures were ultrasound-guided (US) histological biopsy and fine-needle aspiration, US-guided needle puncture and catheter drainage, and basic abdominal ultrasound. CONCLUSION: A needs assessment identified and prioritized 13 technical procedures to include in a simulation-based curriculum. The list may be used as guide for development of training programs. KEY POINTS: • Simulation-based training can supplement training on patients in radiology. • Development of simulation-based training should follow a structured approach. • The CAMES Needs Assessment Formula explores needs for simulation training. • A national Delphi study identified and prioritized procedures suitable for simulation training. • The prioritized list serves as guide for development of courses in radiology.

Shadow effects in simulated ultrasound images derived from computed tomography images using a focused beam tracing model.

Simulation of ultrasound images based on computed tomography (CT) data has previously been performed with different approaches. Shadow effects are normally pronounced in ultrasound images, so they should be included in the simulation. In this study, a method to capture the shadow effects has been developed, which makes the simulated ultrasound images appear more realistic. The method using a focused beam tracing model gives diffuse shadows that are similar to the ones observed in measurements on real objects. Ultrasound images of a cod (Gadus morhua) were obtained with a BK Medical 2202 ProFocus ultrasound scanner (BK Medical, Herlev, Denmark) equipped with a dedicated research interface giving access to beamformed radio frequency data. CT images were obtained with an Aquilion ONE Toshiba CT scanner (Toshiba Medical Systems Corp., Tochigi, Japan). CT data were mapped from Hounsfield units to backscatter strength, attenuation coefficients, and characteristic acoustic impedance. The focused beam tracing model was used to create maps of the transmission coefficient and scattering strength maps. Field II was then used to simulate an ultrasound image of 38.9 × 55.3 × 4.5 mm, using 10(6) point scatterers. As there is no quantitative method to assess quality of a simulated ultrasound image compared to a measured one, visual inspection was used for evaluation.

Carotid Stenosis Assessment with Vector Concentration before and after Stenting.

Digital subtraction angiography (DSA) is considered the reference method for the assessment of carotid artery stenosis; however, the procedure is invasive and accompanied by ionizing radiation. Velocity estimation with duplex ultrasound (DUS) is widely used for carotid artery stenosis assessment since no radiation or intravenous contrast is required; however, the method is angle-dependent. Vector concentration (VC) is a parameter for flow complexity assessment derived from the angle independent ultrasound method vector flow imaging (VFI), and VC has shown to correlate strongly with stenosis degree. The aim of this study was to compare VC estimates and DUS estimated peak-systolic (PSV) and end-diastolic velocities (EDV) for carotid artery stenosis patients, with the stenosis degree obtained with DSA. Eleven patients with symptomatic carotid artery stenosis were examined with DUS, VFI, and DSA before and after stent treatment. Compared to DSA, VC showed a strong correlation (r = -0.79, p < 0.001), while PSV (r = 0.68, p = 0.002) and EDV (r = 0.51, p = 0.048) obtained with DUS showed a moderate correlation. VFI using VC calculations may be a useful ultrasound method for carotid artery stenosis and stent patency assessment.

The Performance of Deep Learning Algorithms on Automatic Pulmonary Nodule Detection and Classification Tested on Different Datasets That Are Not Derived from LIDC-IDRI: A Systematic Review.

The aim of this study was to systematically review the performance of deep learning technology in detecting and classifying pulmonary nodules on computed tomography (CT) scans that were not from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database. Furthermore, we explored the difference in performance when the deep learning technology was applied to test datasets different from the training datasets. Only peer-reviewed, original research articles utilizing deep learning technology were included in this study, and only results from testing on datasets other than the LIDC-IDRI were included. We searched a total of six databases: EMBASE, PubMed, Cochrane Library, the Institute of Electrical and Electronics Engineers, Inc. (IEEE), Scopus, and Web of Science. This resulted in 1782 studies after duplicates were removed, and a total of 26 studies were included in this systematic review. Three studies explored the performance of pulmonary nodule detection only, 16 studies explored the performance of pulmonary nodule classification only, and 7 studies had reports of both pulmonary nodule detection and classification. Three different deep learning architectures were mentioned amongst the included studies: convolutional neural network (CNN), massive training artificial neural network (MTANN), and deep stacked denoising autoencoder extreme learning machine (SDAE-ELM). The studies reached a classification accuracy between 68-99.6% and a detection accuracy between 80.6-94%. Performance of deep learning technology in studies using different test and training datasets was comparable to studies using same type of test and training datasets. In conclusion, deep learning was able to achieve high levels of accuracy, sensitivity, and/or specificity in detecting and/or classifying nodules when applied to pulmonary CT scans not from the LIDC-IDRI database.

Strain histograms are equal to strain ratios in predicting malignancy in breast tumours.

OBJECTIVES: To assess whether strain histograms are equal to strain ratios in predicting breast tumour malignancy and to see if either could be used to upgrade Breast Imaging Reporting and Data System (BI-RADS) 3 tumours for immediate biopsy. METHODS: Ninety-nine breast tumours were examined using B-mode BI-RADS scorings and strain elastography. Strain histograms and ratios were assessed, and areas- under-the-receiver-operating-characteristic-curve (AUROC) for each method calculated. In BI-RADS 3 tumours cut-offs for strain histogram and ratio values were calculated to see if some tumours could be upgraded for immediate biopsy. Linear regression was performed to evaluate the effect of tumour depth and size, and breast density on strain elastography. RESULTS: Forty-four of 99 (44.4%) tumours were malignant. AUROC of BI-RADS, strain histograms and strain ratios were 0.949, 0.830 and 0.794 respectively. There was no significant difference between AUROCs of strain histograms and strain ratios (P = 0.405), while they were both inferior to BI-RADS scoring (P<0.001, P = 0.008). Four out of 26 BI-RADS 3 tumours were malignant. When cut-offs of 189 for strain histograms and 1.44 for strain ratios were used to upgrade BI-RADS 3 tumours, AUROCS were 0.961 (Strain histograms and BI-RADS) and 0.941 (Strain ratios and BI-RADS). None of them was significantly different from BI-RADS scoring alone (P = 0.249 and P = 0.414). Tumour size and depth, and breast density influenced neither strain histograms (P = 0.196, P = 0.115 and P = 0.321) nor strain ratios (P = 0.411, P = 0.596 and P = 0.321). CONCLUSION: Strain histogram analyses are reliable and easy to do in breast cancer diagnosis and perform comparably to strain ratio analyses. No significant difference in AUROCs between BI-RADS scoring and elastography combined with BI-RADS scoring was found in this study.