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Evan Zucker, MD

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Especialidades

Diagnostic Radiology

Trabajo y Educación

Formación Profesional

Harvard Medical School, Boston, MA, 2008

Internado

Newton-Wellesley Hospital Transitional Year, Newton, MA, 06/24/2009

Residencia

Tufts Medical Center Radiology Residency, Boston, MA, 06/30/2013

Compañerismo

Lucile Packard Children's Hospital, Stanford University School of Medicine, Stanford, CA, United States of America, 06/30/2014

Massachusetts General Hospital Dept of Radiology, Boston, MA, United States of America, 06/30/2016

Certificaciones Médicas

Cardiovascular Computed Tomography, Council for Certification in Cardiovascular Imaging

Diagnostic Radiology, American Board of Radiology

Pediatric Radiology, American Board of Radiology

Servicios

Radiología

Todo Publicaciones

Ultrafast pediatric chest computed tomography: comparison of free-breathing vs. breath-hold imaging with and without anesthesia in young children. Pediatric radiology Kino, A., Zucker, E. J., Honkanen, A., Kneebone, J., Wang, J., Chan, F., Newman, B. 2018

Abstract

BACKGROUND: General anesthesia (GA) or sedation has been used to obtain good-quality motion-free breath-hold chest CT scans in young children; however pulmonary atelectasis is a common and problematic accompaniment that can confound diagnostic utility. Dual-source multidetector CT permits ultrafast high-pitch sub-second examinations, minimizing motion artifact and potentially eliminating the need for a breath-hold.OBJECTIVE: The purpose of this study was to evaluate the feasibility of free-breathing ultrafast pediatric chest CT without GA and to compare it with breath-hold and non-breath-hold CT with GA.MATERIALS AND METHODS: Young (3years old) pediatric outpatients scheduled for chest CT under GA were recruited into the study and scanned using one of three protocols: GA with intubation, lung recruitment and breath-hold; GA without breath-hold; and free-breathing CT without anesthesia. In all three protocols an ultrafast high-pitch CT technique was used. We evaluated CT images for overall image quality, presence of atelectasis and motion artifacts.RESULTS: We included 101 scans in the study. However the GA non-breath-hold technique was discontinued after 15 scans, when it became clear that atelectasis was a major issue despite diligent attempts to mitigate it. This technique was therefore not included in statistical evaluation (86 remaining patients). Overall image quality was higher (P=0.001) and motion artifacts were fewer (P<.001) for scans using the GA with intubation and recruitment technique compared to scans in the non-GA free-breathing group. However no significant differences were observed regarding the presence of atelectasis between these groups.CONCLUSION: We demonstrated that although overall image quality was best and motion artifact least with a GA-breath-hold intubation and recruitment technique, free-breathing ultrafast pediatric chest CT without anesthesia provides sufficient image quality for diagnostic purposes and can be successfully performed both without and with contrast agent in young infants.

View details for DOI 10.1007/s00247-018-4295-5

View details for PubMedID 30413857

Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet. PLoS medicine Bien, N., Rajpurkar, P., Ball, R. L., Irvin, J., Park, A., Jones, E., Bereket, M., Patel, B. N., Yeom, K. W., Shpanskaya, K., Halabi, S., Zucker, E., Fanton, G., Amanatullah, D. F., Beaulieu, C. F., Riley, G. M., Stewart, R. J., Blankenberg, F. G., Larson, D. B., Jones, R. H., Langlotz, C. P., Ng, A. Y., Lungren, M. P. 2018; 15 (11): e1002699

Abstract

BACKGROUND: Magnetic resonance imaging (MRI) of the knee is the preferred method for diagnosing knee injuries. However, interpretation of knee MRI is time-intensive and subject to diagnostic error and variability. An automated system for interpreting knee MRI could prioritize high-risk patients and assist clinicians in making diagnoses. Deep learning methods, in being able to automatically learn layers of features, are well suited for modeling the complex relationships between medical images and their interpretations. In this study we developed a deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams. We then measured the effect of providing the model's predictions to clinical experts during interpretation.METHODS AND FINDINGS: Our dataset consisted of 1,370 knee MRI exams performed at Stanford University Medical Center between January 1, 2001, and December 31, 2012 (mean age 38.0 years; 569 [41.5%] female patients). The majority vote of 3 musculoskeletal radiologists established reference standard labels on an internal validation set of 120 exams. We developed MRNet, a convolutional neural network for classifying MRI series and combined predictions from 3 series per exam using logistic regression. In detecting abnormalities, ACL tears, and meniscal tears, this model achieved area under the receiver operating characteristic curve (AUC) values of 0.937 (95% CI 0.895, 0.980), 0.965 (95% CI 0.938, 0.993), and 0.847 (95% CI 0.780, 0.914), respectively, on the internal validation set. We also obtained a public dataset of 917 exams with sagittal T1-weighted series and labels for ACL injury from Clinical Hospital Centre Rijeka, Croatia. On the external validation set of 183 exams, the MRNet trained on Stanford sagittal T2-weighted series achieved an AUC of 0.824 (95% CI 0.757, 0.892) in the detection of ACL injuries with no additional training, while an MRNet trained on the rest of the external data achieved an AUC of 0.911 (95% CI 0.864, 0.958). We additionally measured the specificity, sensitivity, and accuracy of 9 clinical experts (7 board-certified general radiologists and 2 orthopedic surgeons) on the internal validation set both with and without model assistance. Using a 2-sided Pearson's chi-squared test with adjustment for multiple comparisons, we found no significant differences between the performance of the model and that of unassisted general radiologists in detecting abnormalities. General radiologists achieved significantly higher sensitivity in detecting ACL tears (p-value = 0.002; q-value = 0.019) and significantly higher specificity in detecting meniscal tears (p-value = 0.003; q-value = 0.019). Using a 1-tailed t test on the change in performance metrics, we found that providing model predictions significantly increased clinical experts' specificity in identifying ACL tears (p-value < 0.001; q-value = 0.006). The primary limitations of our study include lack of surgical ground truth and the small size of the panel of clinical experts.CONCLUSIONS: Our deep learning model can rapidly generate accurate clinical pathology classifications of knee MRI exams from both internal and external datasets. Moreover, our results support the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation. Further research is needed to validate the model prospectively and to determine its utility in the clinical setting.

View details for DOI 10.1371/journal.pmed.1002699

View details for PubMedID 30481176

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS medicine Rajpurkar, P., Irvin, J., Ball, R. L., Zhu, K., Yang, B., Mehta, H., Duan, T., Ding, D., Bagul, A., Langlotz, C. P., Patel, B. N., Yeom, K. W., Shpanskaya, K., Blankenberg, F. G., Seekins, J., Amrhein, T. J., Mong, D. A., Halabi, S. S., Zucker, E. J., Ng, A. Y., Lungren, M. P. 2018; 15 (11): e1002686

Abstract

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not available. Recently, deep learning approaches have been able to achieve expert-level performance in medical image interpretation tasks, powered by large network architectures and fueled by the emergence of large labeled datasets. The purpose of this study is to investigate the performance of a deep learning algorithm on the detection of pathologies in chest radiographs compared with practicing radiologists.METHODS AND FINDINGS: We developed CheXNeXt, a convolutional neural network to concurrently detect the presence of 14 different pathologies, including pneumonia, pleural effusion, pulmonary masses, and nodules in frontal-view chest radiographs. CheXNeXt was trained and internally validated on the ChestX-ray8 dataset, with a held-out validation set consisting of 420 images, sampled to contain at least 50 cases of each of the original pathology labels. On this validation set, the majority vote of a panel of 3 board-certified cardiothoracic specialist radiologists served as reference standard. We compared CheXNeXt's discriminative performance on the validation set to the performance of 9 radiologists using the area under the receiver operating characteristic curve (AUC). The radiologists included 6 board-certified radiologists (average experience 12 years, range 4-28 years) and 3 senior radiology residents, from 3 academic institutions. We found that CheXNeXt achieved radiologist-level performance on 11 pathologies and did not achieve radiologist-level performance on 3 pathologies. The radiologists achieved statistically significantly higher AUC performance on cardiomegaly, emphysema, and hiatal hernia, with AUCs of 0.888 (95% confidence interval [CI] 0.863-0.910), 0.911 (95% CI 0.866-0.947), and 0.985 (95% CI 0.974-0.991), respectively, whereas CheXNeXt's AUCs were 0.831 (95% CI 0.790-0.870), 0.704 (95% CI 0.567-0.833), and 0.851 (95% CI 0.785-0.909), respectively. CheXNeXt performed better than radiologists in detecting atelectasis, with an AUC of 0.862 (95% CI 0.825-0.895), statistically significantly higher than radiologists' AUC of 0.808 (95% CI 0.777-0.838); there were no statistically significant differences in AUCs for the other 10 pathologies. The average time to interpret the 420 images in the validation set was substantially longer for the radiologists (240 minutes) than for CheXNeXt (1.5 minutes). The main limitations of our study are that neither CheXNeXt nor the radiologists were permitted to use patient history or review prior examinations and that evaluation was limited to a dataset from a single institution.CONCLUSIONS: In this study, we developed and validated a deep learning algorithm that classified clinically important abnormalities in chest radiographs at a performance level comparable to practicing radiologists. Once tested prospectively in clinical settings, the algorithm could have the potential to expand patient access to chest radiograph diagnostics.

View details for DOI 10.1371/journal.pmed.1002686

View details for PubMedID 30457988

Cross-sectional imaging of thoracic traumatic aortic injury. VASA. Zeitschrift fur Gefasskrankheiten Hahn, L. D., Prabhakar, A. M., Zucker, E. J. 2018: 112

Abstract

Aortic injury remains a major contributor to morbidity and mortality from acute thoracic trauma. While such injuries were once nearly uniformly fatal, the advent of cross-sectional imaging in recent years has facilitated rapid diagnosis and triage, greatly improving outcomes. In fact, cross-sectional imaging is now the diagnostic test of choice for traumatic aortic injury (TAI), specifically computed tomography angiography (CTA) in the acute setting and CTA or magnetic resonance angiography (MRA) in follow-up. In this review, we present an up-to-date discussion of acute traumatic thoracic aortic injury with a focus on optimal and emerging CT/MR techniques, imaging findings of TAI, and potential pitfalls.

View details for DOI 10.1024/0301-1526/a000741

View details for PubMedID 30264668

Perivascular Epicardial Fat Stranding at Coronary CT Angiography: A Marker of Acute Plaque Rupture and Spontaneous Coronary Artery Dissection RADIOLOGY Hedgire, S., Baliyan, V., Zucker, E. J., Bittner, D. O., Staziaki, P. V., Takx, R. P., Scholtz, J., Meyersohn, N., Hoffmann, U., Ghoshhajra, B. 2018; 287 (3): 80815
Perivascular Epicardial Fat Stranding at Coronary CT Angiography: A Marker of Acute Plaque Rupture and Spontaneous Coronary Artery Dissection. Radiology Hedgire, S., Baliyan, V., Zucker, E. J., Bittner, D. O., Staziaki, P. V., Takx, R. A., Scholtz, J., Meyersohn, N., Hoffmann, U., Ghoshhajra, B. 2018; 287 (3): 80815

Abstract

Purpose To evaluate the frequency and implications of perivascular fat stranding on coronary computed tomography (CT) angiograms obtained for suspected acute coronary syndrome (ACS). Materials and Methods This retrospective registry study was approved by the institutional review board. The authors reviewed the medical records and images of 1403 consecutive patients (796 men, 607 women; mean age, 52.8 years) who underwent coronary CT angiography at the emergency department from February 2012 to March 2016. Fat attenuation, length and number of circumferential quadrants of the affected segment, and attenuation values in the unaffected epicardial and subcutaneous fat were measured. "Cases" were defined as patients with perivascular fat stranding. Patients with significant stenosis but without fat stranding were considered control subjects. Baseline imaging characteristics, ACS frequency, and results of subsequent downstream testing were compared between cases and control subjects by using two-sample t, Mann-Whitney U, and Fisher tests. Results Perivascular fat stranding was seen in 11 subjects, nine with atherosclerotic lesions and two with spontaneous coronary artery dissections, with a mean fat stranding length of 19.2 mm and circumferential extent averaging 2.9 quadrants. The mean attenuation of perivascular fat stranding, normal epicardial fat, and normal subcutaneous fat was 17, -93.2, and -109.3 HU, respectively (P < .001). Significant differences (P < .05) between cases and control subjects included lower Agatston score, presence of wall motion abnormality, and initial elevation of serum troponin level. ACS frequency was 45.4% in cases and 3.8% in control subjects (P = .001). Conclusion Recognition of perivascular fat stranding may be a helpful additional predictor of culprit lesion and marker of risk for ACS in patients with significant stenosis or spontaneous coronary artery dissection. RSNA, 2018 Online supplemental material is available for this article.

View details for DOI 10.1148/radiol.2017171568

View details for PubMedID 29401041

Abdominal aortic aneurysm screening: concepts and controversies CARDIOVASCULAR DIAGNOSIS AND THERAPY Zucker, E. J., Prabhakar, A. M. 2018; 8: S108S117

Abstract

Abdominal aortic aneurysms (AAAs) are a leading cause mortality and morbidity but often go undiagnosed until late stages unless imaging is performed. In 2005, the United States Preventive Services Task Force (USPSTF) for the first time recommended one-time ultrasound screening for elderly male smokers and selective screening in other populations. These guidelines were reaffirmed and updated in 2014; a proposal for potential further revisions is now in early planning stages. In this article, we review the past and current USPSTF AAA screening recommendations and techniques for performing optimal screening. Evidence supporting screening and alternative guidelines are also discussed. In addition, emerging concepts and controversies in AAA screening are highlighted, including conflicting data on screening benefits, screening underutilization, inconsistent follow-up recommendations, and the potential for duplicative testing, alternative screening modalities, and clinically significant incidental findings.

View details for DOI 10.21037/cdt.2017.09.13

View details for Web of Science ID 000431655700010

View details for PubMedID 29850423

View details for PubMedCentralID PMC5949596

Syndromes with aortic involvement: pictorial review CARDIOVASCULAR DIAGNOSIS AND THERAPY Zucker, E. J. 2018; 8: S71S81

Abstract

A variety of syndromes are associated with thoracoabdominal aortic pathologies. While these diseases are collectively rare, the presence of advanced or unusual aortic disease at a young age should raise suspicion of an underlying syndrome. Similarly, patients with a known syndrome require close monitoring in anticipation of future aortic disease. In this article, the syndromes most commonly encountered in clinical practice are reviewed, including Marfan syndrome (MFS) and other connective tissue disorders, Turner syndrome (TS), autosomal dominant polycystic kidney disease (ADPKD), neurofibromatosis (NF), Williams syndrome (WS), Alagille syndrome (AGS), and DiGeorge syndrome (DGS). The distinct clinical, imaging, and management features of each disorder are discussed. Attention is focused on the unique patterns of aortic disease in each syndrome, emphasizing the role of recent imaging modalities and treatment strategies. Ancillary and distinguishing aspects of the syndromes that aid in diagnosis are also highlighted.

View details for DOI 10.21037/cdt.2017.09.14

View details for Web of Science ID 000431655700007

View details for PubMedID 29850420

View details for PubMedCentralID PMC5949600

Advanced aortic imaging and intervention Preface CARDIOVASCULAR DIAGNOSIS AND THERAPY Zucker, E. J., Prabhakar, A. M., Ganguli, S. 2018; 8: S1S2

View details for Web of Science ID 000431655700001

View details for PubMedID 29850414

View details for PubMedCentralID PMC5949584

Free-breathing pediatric chest MRI: Performance of self-navigated golden-angle ordered conical ultrashort echo time acquisition. Journal of magnetic resonance imaging : JMRI Zucker, E. J., Cheng, J. Y., Haldipur, A., Carl, M., Vasanawala, S. S. 2017

Abstract

To assess the feasibility and performance of conical k-space trajectory free-breathing ultrashort echo time (UTE) chest magnetic resonance imaging (MRI) versus four-dimensional (4D) flow and effects of 50% data subsampling and soft-gated motion correction.Thirty-two consecutive children who underwent both 4D flow and UTE ferumoxytol-enhanced chest MR (mean age: 5.4 years, range: 6 days to 15.7 years) in one 3T exam were recruited. From UTE k-space data, three image sets were reconstructed: 1) one with all data, 2) one using the first 50% of data, and 3) a final set with soft-gating motion correction, leveraging the signal magnitude immediately after each excitation. Two radiologists in blinded fashion independently scored image quality of anatomical landmarks on a 5-point scale. Ratings were compared using Wilcoxon rank-sum, Wilcoxon signed-ranks, and Kruskal-Wallis tests. Interobserver agreement was assessed with the intraclass correlation coefficient (ICC).For fully sampled UTE, mean scores for all structures were 4 (good-excellent). Full UTE surpassed 4D flow for lungs and airways (P < 0.001), with similar pulmonary artery (PA) quality (P = 0.62). 50% subsampling only slightly degraded all landmarks (P < 0.001), as did motion correction. Subsegmental PA visualization was possible in >93% scans for all techniques (P = 0.27). Interobserver agreement was excellent for combined scores (ICC = 0.83).High-quality free-breathing conical UTE chest MR is feasible, surpassing 4D flow for lungs and airways, with equivalent PA visualization. Data subsampling only mildly degraded images, favoring lesser scan times. Soft-gating motion correction overall did not improve image quality.2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017.

View details for DOI 10.1002/jmri.25776

View details for PubMedID 28570032

Impact of California Computed Tomography Dose Legislation: Survey of Radiologists JOURNAL OF MEDICAL IMAGING AND RADIATION SCIENCES Zucker, E. J., Barth, R. A. 2017; 48 (2): 14450
Abdominal Aortic Aneurysm Screening Practices: Impact of the 2014 U.S. Preventive Services Task Force Recommendations. Journal of the American College of Radiology Zucker, E. J., Misono, A. S., Prabhakar, A. M. 2017

Abstract

To assess changes in abdominal aortic aneurysm (AAA) ultrasound screening associated with the release of revised U.S. Preventive Services Task Force (USPSTF) recommendations on June 24,2014.All AAA screening ultrasound examinations performed in the Massachusetts General Hospital radiology department in the 15 months before and after the new guidelines were retrospectively reviewed to assess changes in examination volume and appropriateness, demographics, aneurysm detection rate and size at diagnosis, frequency and type of incidental findings, and radiologist recommendations. Examinations were considered "definitely appropriate" if meeting USPSTF grade "B" evidence and "possibly appropriate" if meeting grade "C" or "I" evidence, based on available guidelines. Means were compared with the t test.A total of 831 examinations were reviewed, 417 (50.2%) performed before and 414 (49.8%) after the new guidelines, with overall mean (SD) subject age 67.9 (6.8) years, 89.2% male. Appropriate examinations increased from 289 of 417 (69.3%) to 313 of 414 (75.6%) after the new guidelines (P= .04), mostly due to definitely appropriate examinations (253/417 [60.7%] versus 286/414 [69.1%], P= .01). Aneurysm detection rates increased from 23 of 417 (5.5%) to 39 of 414 (9.4%), P= .03. Mean (SD) aneurysm size (cm) at diagnosis decreased from 3.8 (0.7) to 3.3 (0.6), P= .01. Examination volume, demographics, and rates of incidentals and recommendations remained similar. Incidentals arose in 15.4% of all examinations, often iliac artery aneurysms or renal masses. Recommendations were made in 5.1%, mostly for cross-sectional imaging.The revised USPSTF guidelines have been associated with increased AAA screening appropriateness and aneurysm detection in our practice, with smaller aneurysm size at diagnosis.

View details for DOI 10.1016/j.jacr.2017.02.020

View details for PubMedID 28427905

Imaging of venous compression syndromes. Cardiovascular diagnosis and therapy Zucker, E. J., Ganguli, S., Ghoshhajra, B. B., Gupta, R., Prabhakar, A. M. 2016; 6 (6): 519-532

Abstract

Venous compression syndromes are a unique group of disorders characterized by anatomical extrinsic venous compression, typically in young and otherwise healthy individuals. While uncommon, they may cause serious complications including pain, swelling, deep venous thrombosis (DVT), pulmonary embolism, and post-thrombotic syndrome. The major disease entities are May-Thurner syndrome (MTS), variant iliac vein compression syndrome (IVCS), venous thoracic outlet syndrome (VTOS)/Paget-Schroetter syndrome, nutcracker syndrome (NCS), and popliteal venous compression (PVC). In this article, we review the key clinical features, multimodality imaging findings, and treatment options of these disorders. Emphasis is placed on the growing role of noninvasive imaging options such as magnetic resonance venography (MRV) in facilitating early and accurate diagnosis and tailored intervention.

View details for DOI 10.21037/cdt.2016.11.19

View details for PubMedID 28123973

View details for PubMedCentralID PMC5220205

Hemodynamic safety and efficacy of ferumoxytol as an intravenous contrast agents in pediatric patients and young adults MAGNETIC RESONANCE IMAGING Ning, P., Zucker, E. J., Wong, P., Vasanawala, S. S. 2016; 34 (2): 152-158
Perinatal Thoracic Mass Lesions: Pre- and Postnatal Imaging. Seminars in ultrasound, CT, and MR Zucker, E. J., Epelman, M., Newman, B. 2015; 36 (6): 501-521

View details for DOI 10.1053/j.sult.2015.05.016

View details for PubMedID 26614133

Added Value of Radiologist Consultation for Pediatric Ultrasound: Implementation and Survey Assessment. AJR. American journal of roentgenology Zucker, E. J., Newman, B., Larson, D. B., Rubesova, E., Barth, R. A. 2015; 205 (4): 822-826

Abstract

The purpose of this study was to determine whether radiologist-parent (guardian) consultation sessions for pediatric ultrasound with immediate disclosure of examination results if desired increases visit satisfaction, decreases anxiety, and increases understanding of the radiologist's role.Parents chaperoning any outpatient pediatric ultrasound were eligible and completed surveys before and after ultrasound examinations. Before the second survey, parents met with a pediatric radiologist on a randomized basis but could opt out and request or decline the consultation. Differences in anxiety and understanding of the radiologist's role before and after the examination were compared, and overall visit satisfaction measures were tabulated.Seventy-seven subjects participated, 71 (92%) of whom spoke to a radiologist, mostly on request. In the consultation group, the mean score (1, lowest; 4, highest) for overall experience was 3.8 0.4 (SD), consultation benefit was 3.7 0.6, and radiologist interaction was 3.7 0.6. Demographics were not predictive of satisfaction with statistical significance in a multivariate model. Forty-six of 68 (68%) respondents correctly described the radiologist's role before consultation. The number increased to 60 (88%) after consultation, and the difference was statistically significant (p < 0.001). There was also a statistically significant decrease in mean anxiety score from 2.0 1.0 to 1.5 0.8 after consultation (p < 0.001). Sixty-four of 70 (91%) respondents indicated that they would prefer to speak with a radiologist during every visit.Radiologist consultation is well received among parents and associated with decreased anxiety and increased understanding of the radiologist's role. The results of this study support the value of routine radiologist-parent interaction for pediatric ultrasound.

View details for DOI 10.2214/AJR.15.14542

View details for PubMedID 26397331

Radiologist Compliance With California CT Dose Reporting Requirements: A Single-Center Review of Pediatric Chest CT AMERICAN JOURNAL OF ROENTGENOLOGY Zucker, E. J., Larson, D. B., Newman, B., Barth, R. A. 2015; 204 (4): 810-816

Abstract

Effective July 1, 2012, CT dose reporting became mandatory in California. We sought to assess radiologist compliance with this legislation and to determine areas for improvement.We retrospectively reviewed reports from all chest CT examinations performed at our institution from July 1, 2012, through June 30, 2013, for errors in documentation of volume CT dose index (CTDIvol), dose-length product (DLP), and phantom size. Reports were considered as legally compliant if both CTDIvol and DLP were documented accurately and as institutionally compliant if phantom size was also documented accurately. Additionally, we tracked reports that did not document dose in our standard format (phantom size, CTDIvol for each series, and total DLP).Radiologists omitted CTDIvol, DLP, or both in nine of 664 examinations (1.4%) and inaccurately reported one or both of them in 56 of the remaining 655 examinations (8.5%). Radiologists omitted phantom size in 11 of 664 examinations (1.7%) and inaccurately documented it in 20 of the remaining 653 examinations (3.1%). Of 664 examinations, 599 (90.2%) met legal reporting requirements, and 583 (87.8%) met institutional requirements. In reporting dose, radiologists variably used less decimal precision than available, summed CTDIvol, included only series-level DLP, and specified dose information from the scout topogram or a nonchest series for combination examinations.Our institutional processes, which primarily rely on correct human performance, do not ensure accurate dose reporting and are prone to variation in dose reporting format. In view of this finding, we are exploring higher-reliability processes, including better-defined standards and automated dose reporting systems, to improve compliance.

View details for DOI 10.2214/AJR.14.13693

View details for Web of Science ID 000351614700037

View details for PubMedID 25794071