title: Radiology Trainee Performance in Digital Breast Tomosynthesis: Relationship Between Difficulty and Error-Making Patterns.
abstract: OBJECTIVE
  The aim of this study was to better understand the relationship between digital breast tomosynthesis (DBT) difficulty and radiology trainee performance.
  
  
  METHODS
  Twenty-seven radiology residents and fellows and three expert breast imagers reviewed 60 DBT studies consisting of unilateral craniocaudal and medial lateral oblique views. Trainees had no prior DBT experience. All readers provided difficulty ratings and final BI-RADS(®) scores. Expert breast imager consensus interpretations were used to determine the ground truth. Trainee sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated for low- and high-difficulty subsets of cases as assessed by each trainee him or herself (self-assessed difficulty) and consensus expert-assessed difficulty.
  
  
  RESULTS
  For self-assessed difficulty, the trainee AUC was 0.696 for high-difficulty and 0.704 for low-difficulty cases (P = .753). Trainee sensitivity was 0.776 for high-difficulty and 0.538 for low-difficulty cases (P < .001). Trainee specificity was 0.558 for high-difficulty and 0.810 for low-difficulty cases (P < .001). For expert-assessed difficulty, the trainee AUC was 0.645 for high-difficulty and 0.816 for low-difficulty cases (P < .001). Trainee sensitivity was 0.612 for high-difficulty and .784 for low-difficulty cases (P < .001). Trainee specificity was 0.654 for high-difficulty and 0.765 for low-difficulty cases (P = .021).
  
  
  CONCLUSIONS
  Cases deemed difficult by experts were associated with decreases in trainee AUC, sensitivity, and specificity. In contrast, for self-assessed more difficult cases, the trainee AUC was unchanged because of increased sensitivity and compensatory decreased specificity. Educators should incorporate these findings when developing educational materials to teach interpretation of DBT.
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina. Electronic address: lars.grimm@duke.edu.
authors.forename: Lars J
authors.initials: LJ
authors.lastname: Grimm
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina; Department of Computer Science, Lamar University, Beaumont, Texas.
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina; Department of Biomedical Engineering, Duke University, Durham, North Carolina.
authors.forename: Joseph Y
authors.initials: JY
authors.lastname: Lo
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina.
authors.forename: Karen S
authors.initials: KS
authors.lastname: Johnson
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina.
authors.forename: Sujata V
authors.initials: SV
authors.lastname: Ghate
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina.
authors.forename: Ruth
authors.initials: R
authors.lastname: Walsh
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, North Carolina; Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina.
authors.forename: Maciej A
authors.initials: MA
authors.lastname: Mazurowski
mesh_terms: D002983:Clinical Competence; D003951:Diagnostic Errors / Q000706:statistics & numerical data*; D004503:Education, Medical, Graduate; D005260:Female; D006801:Humans; D007396:Internship and Residency; D008327:Mammography; D011871:Radiology / Q000193:education*; D012680:Sensitivity and Specificity
keywords: breast cancer; difficulty; error-making patterns; residents; tomosynthesis