Book chapter
Understanding the Role of Correct Lesion Assessment in Radiologists’ Reporting of Breast Cancer
Breast Imaging, pp.341-347
Lecture Notes in Computer Science, Springer International Publishing
2014
DOI: 10.1007/978-3-319-07887-8_48
Abstract
Despite the innovations in breast imaging technology, the miss rates of breast cancers at mammography screening have remained stable, ranging from 10-30% per year. While many factors have been linked to radiologist performance (such as volume of cases read, years of experience reading mammograms), little is known about the relationship between the cancers correctly reported by the radiologists and the characteristics of the background and the malignant lesion. In this study we have used the BREAST platform to allow 92 radiologists to read a case set of 60 digital mammograms, of which 20 depicted cancer. Readers were divided in 4 groups, obtained from the quartiles of the median localization sensitivity performance. Median location sensitivity for all readers was 0.71 (IQR=0.21). Statistically significant differences were observed among the groups in correctly reporting several types of lesion; for example, stellate masses were correctly reported only 37.5% by the poorest performers (median location sensitivity < 0.5), vs 88.9% by the top performers (median location sensitivity ≥ 0.92, z=-3.317, P=0.0017). When compared to top performers, the poorest performers had more difficulty reporting smaller lesions (<10mm) (40.9% vs 90.9% from top performers, z=-3.354, P=0.0008). Results suggest a link between the types of lesions more often missed by radiologists and their median location sensitivity.
Details
- Title: Subtitle
- Understanding the Role of Correct Lesion Assessment in Radiologists’ Reporting of Breast Cancer
- Creators
- Claudia Mello-Thoms - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaPhuong Dung Trieu - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaMohammed A Rawashdeh - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaKriscia Tapia - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaWarwick B Lee - BreastScreen NSW, Cancer Institute, AustraliaPatrick C Brennan - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, Australia
- Resource Type
- Book chapter
- Publication Details
- Breast Imaging, pp.341-347
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-07887-8_48
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2014
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984051873002771
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