ArticleScientific reports2021
Improving radiologist's ability in identifying particular abnormal lesions on mammograms through training test set with immediate feedback.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Enhancing Diagnostic Precision in Breast Cancer Detection: A Digital Breast Tomosynthesis Workshop Approach for Clinicians in the Southeast Asian Population.Asia-Pacific journal of clinical oncology · 2026Article
- Development and evaluation of a technology-enhanced simulation to measure physician decision making in trauma triage.PloS one · 2026Article
- Henry S. Pennypacker's Contributions to Breast Cancer Detection: Developing a Behavioral Technology to Improve Breast Self-Exam Skills.Perspectives on behavior science · 2025Review
- Radiomic analysis of cohort-specific diagnostic errors in reading dense mammograms using artificial intelligence.The British journal of radiology · 2025Article
- Surgical and Radiology Trainees' Proficiency in Reading Mammograms: the Importance of Education for Cancer Localisation.Journal of cancer education : the official journal of the American Association for Cancer Education · 2024Article
- Global Radiomic Features from Mammography for Predicting Difficult-To-Interpret Normal Cases.Journal of digital imaging · 2023Article
- Using Radiomics-Based Machine Learning to Create Targeted Test Sets to Improve Specific Mammography Reader Cohort Performance: A Feasibility Study.Journal of personalized medicine · 2023Article
- Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Article
- Learning effects in visual grading assessment of model-based reconstruction algorithms in abdominal Computed Tomography.European journal of radiology open · 2023Article
- Look how far we have come: BREAST cancer detection education on the international stage.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It has been shown that there are differences in diagnostic accuracy of cancer detection on mammograms, from below 50% in developing countries to over 80% in developed world. One previous study reported that radiologists from a population in Asia displayed a low mammographic cancer detection of 48% compared with over 80% in developed countries, and more importantly, that most lesions missed by these radiologists were spiculated masses or stellate lesions. The aim of this study was to explore the performance of radiologists after undertaking a training test set which had been designed to improve the capability in detecting a specific type of cancers on mammograms. Twenty-five radiologists read two sets of 60 mammograms in a standardized mammogram reading room. The first test set focused on stellate or spiculated masses. When radiologists completed the first set, the system displayed immediate feedback to the readers comparing their performances in each case with the truth of cancer cases and cancer types so that the readers could identify individual-based errors. Later radiologists were asked to read the second set of mammograms which contained different types of cancers including stellate/spiculated masses, asymmetric density, calcification, discrete mass and architectural distortion. Case sensitivity, lesion sensitivity, specificity, receiver operating characteristics (ROC) and Jackknife alternative free-response receiver operating characteristics (JAFROC) were calculated for each participant and their diagnostic accuracy was compared between two sessions. Results showed significant improvement among radiologists in case sensitivity (+ 11.4%; P < 0.05), lesion sensitivity (+ 18.7%; P < 0.01) and JAFROC (+ 11%; P < 0.01) in the second set compared with the first set. The increase in diagnostic accuracy was also recorded in the detection of stellate/spiculated mass (+ 20.6%; P < 0.05). This indicated that the performance of radiologists in detecting malignant lesions on mammograms can be improved if an appropriate training intervention is applied after the readers' weakness and strength are identified.
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