Jeffrey D Poot1, Alison L Chetlen2. 1. Department of Radiology, H066, Penn State Hershey Medical Center, P.O. Box 850, 500 University Drive, Hershey, PA 17033. Electronic address: jpoot@hmc.psu.edu. 2. Department of Radiology, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania.
Abstract
RATIONALE AND OBJECTIVES: To improve mammographic screening training and breast cancer detection, radiology residents participated in a simulation screening mammography module in which they interpreted an enriched set of screening mammograms with known outcomes. This pilot research study evaluates the effectiveness of the simulation module while tracking the progress, efficiency, and accuracy of radiology resident interpretations and also compares their performance against national benchmarks. MATERIALS AND METHODS: A simulation module was created with 266 digital screening mammograms enriched with high-risk breast lesions (seven cases) and breast malignancies (65 cases). Over a period of 27 months, 39 radiology residents participated in the simulation screening mammography module. Resident sensitivity and specificity were compared to Breast Cancer Surveillance Consortium (BCSC data through 2009) national benchmark and American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) acceptable screening mammography audit ranges. RESULTS: The sensitivity, the percentage of cancers with an abnormal initial interpretation (BI-RADS 0), among residents was 84.5%, similar to the BCSC benchmark sensitivity of 84.9% (sensitivity for tissue diagnosis of cancer within 1 year following the initial examination) and within the acceptable ACR BI-RADS medical audit range of ≥75%. The specificity, the percentage of noncancers that had a negative image interpretation (BI-RADS 1 or 2), among residents was 83.2% compared to 90.3% reported in the BCSC benchmark data, but lower than the suggested ACR BI-RADS range of 88%-95%. CONCLUSIONS: Using simulation modules for interpretation of screening mammograms is a promising method for training radiology residents to detect breast cancer and to help them achieve competence toward national benchmarks.
RATIONALE AND OBJECTIVES: To improve mammographic screening training and breast cancer detection, radiology residents participated in a simulation screening mammography module in which they interpreted an enriched set of screening mammograms with known outcomes. This pilot research study evaluates the effectiveness of the simulation module while tracking the progress, efficiency, and accuracy of radiology resident interpretations and also compares their performance against national benchmarks. MATERIALS AND METHODS: A simulation module was created with 266 digital screening mammograms enriched with high-risk breast lesions (seven cases) and breast malignancies (65 cases). Over a period of 27 months, 39 radiology residents participated in the simulation screening mammography module. Resident sensitivity and specificity were compared to Breast Cancer Surveillance Consortium (BCSC data through 2009) national benchmark and American College of Radiology (ACR) Breast Imaging Reporting and Data System (BI-RADS) acceptable screening mammography audit ranges. RESULTS: The sensitivity, the percentage of cancers with an abnormal initial interpretation (BI-RADS 0), among residents was 84.5%, similar to the BCSC benchmark sensitivity of 84.9% (sensitivity for tissue diagnosis of cancer within 1 year following the initial examination) and within the acceptable ACR BI-RADS medical audit range of ≥75%. The specificity, the percentage of noncancers that had a negative image interpretation (BI-RADS 1 or 2), among residents was 83.2% compared to 90.3% reported in the BCSC benchmark data, but lower than the suggested ACR BI-RADS range of 88%-95%. CONCLUSIONS: Using simulation modules for interpretation of screening mammograms is a promising method for training radiology residents to detect breast cancer and to help them achieve competence toward national benchmarks.
Authors: Tong Li; Seyedamir Tavakoli Taba; Pek-Lan Khong; Tom X-L Tan; Phuong Dung Yun Trieu; Edward Chan; Moayyad E Suleiman; Ying Li; Patrick Brennan; Sarah Lewis Journal: Asian Pac J Cancer Prev Date: 2020-09-01