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Mammography: Radiologist and Image Characteristics That Determine the Accuracy of Breast Cancer Diagnosis

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Breast Imaging (IWDM 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8539))

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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.

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Rawashdeh, M.A., Mello-Thoms, C., Bourne, R., Brennan, P.C. (2014). Mammography: Radiologist and Image Characteristics That Determine the Accuracy of Breast Cancer Diagnosis. In: Fujita, H., Hara, T., Muramatsu, C. (eds) Breast Imaging. IWDM 2014. Lecture Notes in Computer Science, vol 8539. Springer, Cham. https://doi.org/10.1007/978-3-319-07887-8_101

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  • DOI: https://doi.org/10.1007/978-3-319-07887-8_101

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07886-1

  • Online ISBN: 978-3-319-07887-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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