Book chapter
Mammography: Radiologist and Image Characteristics That Determine the Accuracy of Breast Cancer Diagnosis
Breast Imaging, pp.731-736
Lecture Notes in Computer Science, Springer International Publishing
2014
DOI: 10.1007/978-3-319-07887-8_101
Abstract
Variations in the performance of breast readers are well reported, but key lesion and reader parameters explaining such variations are not fully explored. This large study aims to: 1) measure diagnostic accuracy of breast radiologists, 2) identify parameters linked to higher levels of performance, and 3) establish the key morphological descriptors that impact detection of breast cancer. Methods: Sixty cases, 20 containing cancer, were shown to 129 radiologists. Each reader was asked to locate any malignancies and provide a confidence rating using a scale of 1-5. Details were obtained from each radiologist regarding experience and training and were correlated with jackknifing free response operating characteristic (JAFROC) figure of merit. Cancers were ranked according to the “detectability rating” that is, the number of readers who accurately detected and located the lesion divided by the total number of readers, and this was correlated with various mathematical lesion descriptors. Results: Higher reader performance was positively correlated with number of years reading mammograms (r=0.24, p=0.01), number of mammogram readings per year (r=0.28, p=0.001), and hours reading mammogram per week (r=0.19, p=0.04). For image features and lesion descriptors there was correlation between “detectability rating” and lesion size (r=0.65, p=0.005), breast density (r=-0.64, p=0.007), perimeter (r=0.66, p=0.0004), eccentricity (r= 0.49, p=0.02), and solidity (r=0.78, p< 0.0001). Radiologist experience and lesion morphology may contribute significantly to reduce cancer detection.
Details
- Title: Subtitle
- Mammography: Radiologist and Image Characteristics That Determine the Accuracy of Breast Cancer Diagnosis
- Creators
- Mohammad A Rawashdeh - Medical Image Optimisation and Perception Group (MIOPeG), Medical Imaging & Radiation Sciences, Faculty Research Group, The Faculty of Health Sciences, The University of Sydney, Lidcombe, AustraliaClaudia Mello-Thoms - Medical Image Optimisation and Perception Group (MIOPeG), Medical Imaging & Radiation Sciences, Faculty Research Group, The Faculty of Health Sciences, The University of Sydney, Lidcombe, AustraliaRoger Bourne - Medical Image Optimisation and Perception Group (MIOPeG), Medical Imaging & Radiation Sciences, Faculty Research Group, The Faculty of Health Sciences, The University of Sydney, Lidcombe, AustraliaPatrick C Brennan - Medical Image Optimisation and Perception Group (MIOPeG), Medical Imaging & Radiation Sciences, Faculty Research Group, The Faculty of Health Sciences, The University of Sydney, Lidcombe, Australia
- Resource Type
- Book chapter
- Publication Details
- Breast Imaging, pp.731-736
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-07887-8_101
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2014
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984051540302771
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