Competency based curriculum for cardiovascular magnetic resonance: A position statement of the Society for Cardiovascular Magnetic Resonance.

This position statement guides cardiovascular magnetic resonance (CMR) imaging program directors and learners on the key competencies required for Level II and III CMR practitioners, whether trainees come from a radiology or cardiology background. This document is built upon existing curricula and was created and vetted by an international panel of cardiologists and radiologists on behalf of the Society for Cardiovascular Magnetic Resonance (SCMR).

Canadian Association of Radiologists Cardiovascular Imaging Referral Guideline.

The Canadian Association of Radiologists (CAR) Cardiovascular Expert Panel is made up of physicians from the disciplines of radiology, cardiology, and emergency medicine, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 30 clinical/diagnostic scenarios, a rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 48 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) for guidelines framework were used to develop 125 recommendation statements across the 30 scenarios (27 unique scenarios as 2 scenarios point to the CAR Thoracic Diagnostic Imaging Referral Guideline and the acute pericarditis subscenario is included under 2 main scenarios). This guideline presents the methods of development and the referral recommendations for acute chest pain syndromes, chronic chest pain, cardiovascular screening and risk stratification, pericardial syndromes, intracardiac/pericardial mass, suspected valvular disease cardiomyopathy, aorta, venous thrombosis, and peripheral vascular disease.

The uncovered biases and errors in clinical determination of bone age by using deep learning models.

OBJECTIVES: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA. METHODS: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM. RESULTS: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists. CONCLUSIONS: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered. KEY POINTS: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films.

Which Aspects of the CanMEDS Competencies are Most Valued in Radiologists? Perspectives of Trainees From Other Specialties.

PURPOSE: Radiologists work primarily in collaboration with other healthcare professionals. As such, these stakeholder perspectives are of value to the development and assessment of educational outcomes during the transition to competency-based medical education. Our aim in this study was to determine which aspects of the Royal College CanMEDS competencies for diagnostic radiology are considered most important by future referring physicians. METHODS: Institutional ethics approval was obtained. After pilot testing, an anonymous online survey was sent to all residents and clinical fellows at our university. Open-ended questions asked respondents to describe the aspects of radiologist service they felt were most important. Thematic analysis of the free-text responses was performed using a grounded theory approach. The resulting themes were mapped to the 2015 CanMEDS Key Competencies. RESULTS: 115 completed surveys were received from residents and fellows from essentially all specialties and years of training (out of 928 invited). Major themes were 1) timeliness and accessibility of service, 2) quality of reporting, and 3) acting as a valued team member. The competencies identified as important by resident physicians were largely consistent with the CanMEDS framework, although not all key competencies were covered in the responses. CONCLUSIONS: This study illustrates how CanMEDS roles and competencies may be exemplified in a concrete and specialty-specific manner from the perspective of key stakeholders. Our survey results provide further insight into specific objectives for teaching and assessing these competencies in radiology residency training, with the ultimate goal of improving patient care through strengthened communication and working relationships.

Radiologist Characteristics Associated with Interpretive Performance of Screening Mammography: A National Mammography Database (NMD) Study.

Background Factors affecting radiologists' performance in screening mammography interpretation remain poorly understood. Purpose To identify radiologists characteristics that affect screening mammography interpretation performance. Materials and Methods This retrospective study included 1223 radiologists in the National Mammography Database (NMD) from 2008 to 2019 who could be linked to Centers for Medicare & Medicaid Services (CMS) datasets. NMD screening performance metrics were extracted. Acceptable ranges were defined as follows: recall rate (RR) between 5% and 12%; cancer detection rate (CDR) of at least 2.5 per 1000 screening examinations; positive predictive value of recall (PPV1) between 3% and 8%; positive predictive value of biopsies recommended (PPV2) between 20% and 40%; positive predictive value of biopsies performed (PPV3) between the 25th and 75th percentile of study sample; invasive CDR of at least the 25th percentile of the study sample; and percentage of ductal carcinoma in situ (DCIS) of at least the 25th percentile of the study sample. Radiologist characteristics extracted from CMS datasets included demographics, subspecialization, and clinical practice patterns. Multivariable stepwise logistic regression models were performed to identify characteristics independently associated with acceptable performance for the seven metrics. The most influential characteristics were defined as those independently associated with the majority of the metrics (at least four). Results Relative to radiologists practicing in the Northeast, those in the Midwest were more likely to achieve acceptable RR, PPV1, PPV2, and CDR (odds ratio [OR], 1.4-2.5); those practicing in the West were more likely to achieve acceptable RR, PPV2, and PPV3 (OR, 1.7-2.1) but less likely to achieve acceptable invasive CDR (OR, 0.6). Relative to general radiologists, breast imagers were more likely to achieve acceptable PPV1, invasive CDR, percentage DCIS, and CDR (OR, 1.4-4.4). Those performing diagnostic mammography were more likely to achieve acceptable PPV1, PPV2, PPV3, invasive CDR, and CDR (OR, 1.9-2.9). Those performing breast US were less likely to achieve acceptable PPV1, PPV2, percentage DCIS, and CDR (OR, 0.5-0.7). Conclusion The geographic location of the radiology practice, subspecialization in breast imaging, and performance of diagnostic mammography are associated with better screening mammography performance; performance of breast US is associated with lower performance. ©RSNA, 2021 Online supplemental material is available for this article.

Role of Key Guidelines in an Era of Precision Oncology: A Primer for the Radiologist.

OBJECTIVE. The purpose of this article is to familiarize radiologists with the evidence-based imaging guidelines of major oncologic societies and organizations and to discuss approaches to effective implementation of the most recent guidelines in daily radiology practice. CONCLUSION. In an era of precision oncology, radiologists in practice and radiologists in training are key stakeholders in multidisciplinary care, and their awareness and understanding of society guidelines is critically important.

New concept: "TARN friendly trauma reporting" (what radiologists say really does matter).

AIM: To establish if detailed review of trauma reports with reference to coding manual improved accuracy of ISS and to establish if demonstrated changes in coding affected performance and tariff payment. MATERIALS AND METHODS: A study was undertaken which gathered data from 6 months across the five trusts with information on imaging undertaken, mechanism of injury (MOI), Injury Severity Score (ISS), and injury descriptors was included. Patients with ISS near to a best practice tariff boundary of 9 and 16 (5-8 and 11-15) then had their imaging reviewed by the Radiology Department with direct reference to the ISS coding manual. Injuries were then re-coded and ISS recalculated. RESULTS: Over the 6-month period, 1,693 patients were admitted to the database from the five hospitals. One hundred and sixty-nine (9.9%) patients met the inclusion criteria for review. Thirty-five (20.7%) had a change in abbreviated (region specific) injury code, with 30 a change in the resultant ISS. Three had a decrease in ISS and 27 increased ISS with all 27 moving across an ISS best practice tariff and three moving across two payment tariff boundaries. With re-coding, there was a potential £15,000 of lost revenue from the major trauma centre (MTC) alone. CONCLUSION: Reporting with reference to ISS description improves the accuracy of ISS significantly. Radiologists improving the descriptions of specific injury patterns and adopting 'Trauma Audit and Research Network friendly' reporting strategies may improve data accuracy, performance, and payment of best practice tariffs to hospitals.

Emergency Computed Tomography: How Misinterpretations Vary According to the Periods of the Nightshift?

OBJECTIVE: To evaluate the accuracy of initial computed tomography (CT) interpretations made by radiology residents during nightshifts in the emergency department. METHODS: Preliminary CT reports performed by radiology residents during 120 consecutive nightshifts (08:30 pm to 08:30 am) were reviewed, attendings' final interpretation being the reference standard. Nightshifts were divided into four consecutive periods of 3 hours. Major misinterpretations were related to potentially life-threatening conditions if not treated immediately after CT. The rate of misinterpretations was calculated for all CT examinations, separately for nightshift's periods and for residents' training years. RESULTS: Misinterpretations were recorded in 155 (7.4%) of 2102 CT examinations, 0.6% (13/2102) were major. There were 2.2% (4/186) major misinterpretations that occurred during the last period of the nightshift versus 0.4% (9/1916) during the first periods of the night (P < 0.05). Of all misinterpretations, 8.5% (130/1526) were made by third- and fourth-year residents and 4.3% (25/576) by fifth-year residents (P < 0.005). CONCLUSIONS: Major misinterpretations occur at the end of the nightshift, which may be explained by the fatigue effect. The rate of misinterpretations is lower among fifth-year residents, which may be related to their prior experience in reading emergency cases.

How embryology knowledge can help radiologists in the differential diagnosis of canal of Nuck pathologies.

The Canal of Nuck (CN) is an anatomical structure which is often forgotten. It is the female equivalent of the male processus vaginalis and corresponds to a protrusion of parietal peritoneum that extends from the inguinal canal to labia majora. Radiologists rarely encounter patients with pathology of CN, especially in adult population. It is well known that CN diseases can occur in paediatric patient (especially younger than 5 years of age) and they are associated to high morbidity (for example ovarian hernia with high risk of incarceration and torsion). The aim of our work is to review embryology, anatomy and pathologies of the CN thanks to a multi modal approach-ultrasound (US), Computed Tomography (CT) and Magnetic Resonance imaging (MRI)-to make radiologists more aware of such conditions and guarantee a prompt and correct diagnosis not only in paediatric patients but also in the adult population.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Artificial Intelligence Algorithm Detecting Lung Infection in Supine Chest Radiographs of Critically Ill Patients With a Diagnostic Accuracy Similar to Board-Certified Radiologists.

OBJECTIVES: Interpretation of lung opacities in ICU supine chest radiographs remains challenging. We evaluated a prototype artificial intelligence algorithm to classify basal lung opacities according to underlying pathologies. DESIGN: Retrospective study. The deep neural network was trained on two publicly available datasets including 297,541 images of 86,876 patients. PATIENTS: One hundred sixty-six patients received both supine chest radiograph and CT scans (reference standard) within 90 minutes without any intervention in between. MEASUREMENTS AND MAIN RESULTS: Algorithm accuracy was referenced to board-certified radiologists who evaluated supine chest radiographs according to side-separate reading scores for pneumonia and effusion (0 = absent, 1 = possible, and 2 = highly suspected). Radiologists were blinded to the supine chest radiograph findings during CT interpretation. Performances of radiologists and the artificial intelligence algorithm were quantified by receiver-operating characteristic curve analysis. Diagnostic metrics (sensitivity, specificity, positive predictive value, negative predictive value, and accuracy) were calculated based on different receiver-operating characteristic operating points. Regarding pneumonia detection, radiologists achieved a maximum diagnostic accuracy of up to 0.87 (95% CI, 0.78-0.93) when considering only the supine chest radiograph reading score 2 as positive for pneumonia. Radiologist's maximum sensitivity up to 0.87 (95% CI, 0.76-0.94) was achieved by additionally rating the supine chest radiograph reading score 1 as positive for pneumonia and taking previous examinations into account. Radiologic assessment essentially achieved nonsignificantly higher results compared with the artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did not significantly differ. Regarding the detection of pleural effusions, there was no significant performance difference between radiologist's and artificial intelligence algorithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points. CONCLUSIONS: Considering the minor level of performance differences between the algorithm and radiologists, we regard artificial intelligence as a promising clinical decision support tool for supine chest radiograph examinations in the clinical routine with high potential to reduce the number of missed findings in an artificial intelligence-assisted reading setting.

Radiation exposure and fluoroscopy time in mechanical thrombectomy of anterior circulation ischemic stroke depending on the interventionalist's experience-a retrospective single center experience.

PURPOSE: To quantify the influence of interventionalist's experience on procedure time, radiation exposure, and fluoroscopy time during mechanical thrombectomy (MT) in the anterior circulation. METHODS: Retrospective analysis of an institutional review board-approved stroke database of a comprehensive stroke center focusing on radiation exposure (as per dose area product in Gy × cm2, median [IQR]), procedure, and fluoroscopy time (in minutes, median [IQR]) in patients receiving MT in anterior circulation ischemic stroke. Procedures have been assigned according to the interventionalist's experience in MT into three sequential groups: A = 1-25 procedures, B = 26-50 procedures, and C = more than 50 procedures. RESULTS: Overall, 696 patients have been included in this analysis (A, n = 152; B, n = 151; C, n = 393). Procedure times (A, 86 [54-131]; B, 67 [48-103], p value 0.006), fluoroscopy times (A, 39 [25-72]; B, 32 [20-53], p value 0.001) as well as radiation exposure (A, 148.13 [89.58-243.37]; B, 111.60 [70.49-180.57], p value 0.001) were significantly shorter, respectively lower in group B than in group A. Procedure times (C, 59 [36-99]), fluoroscopy times (C, 31 [16-53]), and radiation exposure (C, 113.91 [68.48-182.88]) in group C were also significantly shorter/lower than in group A (all p values < 0.0001), but comparable with group B (p values 0.071, 0.809, and 0.934). CONCLUSION: This retrospective analysis demonstrates a significant influence of interventionalist's experience on procedure time, fluoroscopy time, and radiation exposure in mechanical thrombectomy in the anterior circulation. KEY POINTS: • There is a significant influence of interventionalist's experience on procedure time, fluoroscopy time, and radiation exposure in mechanical thrombectomy in the anterior circulation. • Interventionalists' learning curve is steepest during the first 25 cases. • These circumstances should be considered when reference levels or guide values are established and in training of physicians performing mechanical thrombectomy to promote optimization of patient doses in the future.

Interreader Concordance of the TI-RADS: Impact of Radiologist Experience.

OBJECTIVE. The objective of this article is to assess radiologist concordance in characterizing thyroid nodules using the American College of Radiology Thyroid Imaging Reporting and Data System (TI-RADS), focusing on the effect of radiologist experience on reader concordance. MATERIALS AND METHODS. Three experienced and three less experienced radiologists assessed 150 thyroid nodules using the TI-RADS lexicon. Percent concordance was determined for various endpoints. RESULTS. Interreader concordance for the five TI-RADS categories was 87.2% for shape, 81.2% for composition, 76.1% for echogenicity, 72.9% for margins, and 69.8% for echogenic foci. Concordance for individual features was 96.3% for rim calcifications, 90.8% for macrocalcifications, 90.1% for spongiform, 83.5% for comet tail artifact, and 77.7% for punctate echogenic foci. Concordance for the TI-RADS level and recommendation for fine-needle aspiration (FNA) were 50.4% and 78.9%, respectively. Concordance was significantly (p < 0.05) higher for less experienced readers in identifying margins (84.3% vs 67.4%), echogenic foci (76.9% vs 69.3%), comet tail artifact (89.6% vs 79.2%), and punctate echogenic foci (85.3% vs 75.5%), and lower for peripheral rim calcifications (95.0% vs 97.8 %), but was not different (p > 0.05) for the remaining categories and features. CONCLUSION. A range of TI-RADS categories, features, and recommendations for FNA had generally moderate interreader agreement among six radiologists. Our results show that concordance for numerous characteristics was significantly higher for the less experienced versus the more experienced readers. These results suggest that less experienced readers relied more on the explicit TI-RADS criteria, whereas the experienced radiologists partially relied on their accumulated experience when forming impressions. However, the overall TI-RADS level and recommendation for FNA were unaffected, supporting the robustness of the TI-RADS lexicon and its continued use in practice.

Perceptual Learning of Appendicitis Diagnosis in Radiological Images.

A sizeable body of work has demonstrated that participants have the capacity to show substantial increases in performance on perceptual tasks given appropriate practice. This has resulted in significant interest in the use of such perceptual learning techniques to positively impact performance in real-world domains where the extraction of perceptual information in the service of guiding decisions is at a premium. Radiological training is one clear example of such a domain. Here we examine a number of basic science questions related to the use of perceptual learning techniques in the context of a radiology-inspired task. On each trial of this task, participants were presented with a single axial slice from a CT image of the abdomen. They were then asked to indicate whether or not the image was consistent with appendicitis. We first demonstrate that, although the task differs in many ways from standard radiological practice, it nonetheless makes use of expert knowledge, as trained radiologists who underwent the task showed high (near ceiling) levels of performance. Then, in a series of four studies we show that (1) performance on this task does improve significantly over a reasonably short period of training (on the scale of a few hours); (2) the learning transfers to previously unseen images and to untrained image orientations; (3) purely correct/incorrect feedback produces weak learning compared to more informative feedback where the spatial position of the appendix is indicated in each image; and (4) there was little benefit seen from purposefully structuring the learning experience by starting with easier images and then moving on to more difficulty images (as compared to simply presenting all images in a random order). The implications for these various findings with respect to the use of perceptual learning techniques as part of radiological training are then discussed.

[Acute abdomen : What does the nonradiologist expect from the radiologist?] / Akutes Abdomen : Was erwartet der Nicht-Radiologe vom Radiologen?

BACKGROUND: The acute abdomen is a life-threatening condition that demands urgent intervention. The required diagnostics should address the core problem and has to be chosen based upon the diagnostic strength of each diagnostic tool. Modalities with limited discriminating information regarding differential diagnosis have to be avoided. Expectancy and thoughts of the radiologist often differ from the view of the clinician in the emergency department. OBJECTIVE: The decision about which diagnostic tools are valuable or unnecessary in the emergency setting is made from a surgeon's point of view. Close communication with radiologists is mandatory. We demonstrate the importance of clinical signs and symptoms and their correlation with helpful radiologic diagnostics. CONCLUSION: The emergency radiologic diagnostic workup of acute abdomen has to be targeted and the radiologist must answer the questions in order to clarify whether an operation is indicated and to help define the surgical strategy. In emergency surgery as in acute abdomen extended diagnostics to reach a decision is a dangerous waste of time and must be avoided at all costs. Therefore close communication with the radiologist is crucial.

Pediatric cervical spine injuries on CT: difference in accuracy of interpretations by pediatric versus non-pediatric radiologists.

PURPOSE: To retrospectively compare the accuracy of interpretation of initial cervical computerized tomography (CCT) by a non-pediatric radiologist (NPR) versus a pediatric radiologist (PR). METHODS: IRB approval and consent waiver were granted to review all injured children from 2010 to 2014 in the trauma registry with CT and magnetic resonance imaging (MRI) of the cervical spine. Patients with negative CCT who subsequently had positive MRI from a single institution comprised the study group. Patients with negative CCT and MRI, matched by age, gender, and severity scores, comprised the control group. The CCTs from both groups were initially interpreted at the time of service by a NPR. Subsequently, a single PR with 20 years of experience blinded to clinical/imaging data reinterpreted these CCT examinations. CT interpretations were then compared with MRI results and evaluated for statistical significance using SSPS software. The data analysis utilized summary statistics, two-tailed binomial test, and univariate χ2 test. Significance for all comparisons was assessed at P < 0.05. RESULTS: The study group was comprised of the 21 patients with negative CCT and positive MRI. Of the cohort included, 76% (16) were male and 24% (5) were female. The age range was 1 month-17 years, with a mean age of 9.7 years. CCT interpretation by NPR had a specificity of 91.7% (sensitivity 71.2%, positive predictive value 81.3%, and negative predictive value 86.3%) compared with results of MRI. Six of the 21 negative CCTs were interpreted by the PR as positive, mainly craniocervical junction injuries, and confirmed by MRI (28.6%, P < .001 compared with the NPR); no control CCT was interpreted by the PR as positive (sensitivity 100%, positive predictive value 100%, and negative predictive value 58.3%). CONCLUSION: In our retrospective study, a pediatric radiologist has improved recognition of pediatric cervical spine injuries on CT compared with non-pediatric radiologist.

Influence of Mammography Volume on Radiologists' Performance: Results from BreastScreen Norway.

Background Several European screening mammography programs that use independent double reading with consensus recommend an annual minimum reading volume of 5000 mammograms per radiologist. This recommendation is based only on expert opinion, and the influence of reading volume on performance in such programs is unknown. Purpose To examine the influence of annual and cumulative reading volume on radiologists' reading performance for digital mammography in a screening program that uses independent double reading with consensus. Materials and Methods This retrospective study included data from digital mammographic examinations in BreastScreen Norway obtained from 2006 to 2016. Multilevel mixed-effects models were used to determine how sensitivity, rate of screening-detected breast cancer (SDC), and false-positive rate (FPR) before and after consensus meeting related to annual and cumulative reading volume. Results The study included 2 373 433 readings performed by 121 radiologists. The median annual reading volume ranged from 153 to 19 500 mammograms, and the median cumulative reading volume was 30 566 mammograms. Sensitivity and SDC rate were relatively stable at 87% to 90% and 4.9 to 4.7 per 1000 readings (0.49% and 0.47%), respectively, between 100 and 10 000 annual readings and at 88% to 89% and 4.8 to 4.9 per 1000 readings (0.48% and 0.49%) between 500 and 100 000 cumulative readings. There was a decreasing trend with higher annual volumes (P for trend < .001 for both) to a sensitivity of 81% and SDC rate of 3.9 per 1000 readings (0.39%) at 18 000 readings. There was a decreasing trend in FPR before and after consensus with higher annual and cumulative volumes (P for trend < .001 for all). FPRs before consensus meeting were 5.3% at 100 annual readings and 4.0% at 4000 annual readings and were 6.7% at 500 cumulative readings and 3.6% at 20 000 cumulative readings. FPRs after consensus meeting were 2.5% at 100 annual readings and 2.3% at 4000 annual readings and were 2.7% at 500 cumulative readings and 2.2% at 20 000 cumulative readings. Conclusion Annual reading volumes between 4000 and 10 000 mammograms and cumulative reading volumes greater than 20 000 mammograms may be the most optimal volumes for achieving high reading performance in a screening program with independent double reading. © RSNA, 2019 See also the editorial by Rosenberg and Seidenwurm in this issue.

Digital Breast Tomosynthesis: Radiologist Learning Curve.

Background There is growing evidence that digital breast tomosynthesis (DBT) results in lower recall rates and higher cancer detection rates when compared with digital mammography. However, whether DBT interpretative performance changes with experience (learning curve effect) is unknown. Purpose To evaluate screening DBT performance by cumulative DBT volume within 2 years after adoption relative to digital mammography (DM) performance 1 year before DBT adoption. Materials and Methods This prospective study included 106 126 DBT and 221 248 DM examinations in 271 362 women (mean age, 57.5 years) from 2010 to 2017 that were interpreted by 104 radiologists from 53 facilities in the Breast Cancer Surveillance Consortium. Conditional logistic regression was used to estimate within-radiologist effects of increasing cumulative DBT volume on recall and cancer detection rates relative to DM and was adjusted for examination-level characteristics. Changes were also evaluated by subspecialty and breast density. Results Before DBT adoption, DM recall rate was 10.4% (95% confidence interval [CI]: 9.5%, 11.4%) and cancer detection rate was 4.0 per 1000 screenings (95% CI: 3.6 per 1000 screenings, 4.5 per 1000 screenings); after DBT adoption, DBT recall rate was lower (9.4%; 95% CI: 8.2%, 10.6%; P = .02) and cancer detection rate was similar (4.6 per 1000 screenings; 95% CI: 4.0 per 1000 screenings, 5.2 per 1000 screenings; P = .12). Relative to DM, DBT recall rate decreased for a cumulative DBT volume of fewer than 400 studies (odds ratio [OR] = 0.83; 95% CI: 0.78, 0.89) and remained lower as volume increased (400-799 studies, OR = 0.8 [95% CI: 0.75, 0.85]; 800-1199 studies, OR = 0.81 [95% CI: 0.76, 0.87]; 1200-1599 studies, OR = 0.78 [95% CI: 0.73, 0.84]; 1600-2000 studies, OR = 0.81 [95% CI: 0.75, 0.88]; P < .001). Improvements were sustained for breast imaging subspecialists (OR range, 0.67-0.85; P < .02) and readers who were not breast imaging specialists (OR range, 0.80-0.85; P < .001). Recall rates decreased more in women with nondense breasts (OR range, 0.68-0.76; P < .001) than in those with dense breasts (OR range, 0.86-0.90; P ≤ .05; P interaction < .001). Cancer detection rates for DM and DBT were similar, regardless of DBT volume (P ≥ .10). Conclusion Early performance improvements after digital breast tomosynthesis (DBT) adoption were sustained regardless of DBT volume, radiologist subspecialty, or breast density. © RSNA, 2019 See also the editorial by Hooley in this issue.