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Understanding the Role of Correct Lesion Assessment in Radiologists’ Reporting of Breast Cancer

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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

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.

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© 2014 Springer International Publishing Switzerland

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Mello-Thoms, C., Trieu, P.D., Rawashdeh, M.A., Tapia, K., Lee, W.B., Brennan, P.C. (2014). Understanding the Role of Correct Lesion Assessment in Radiologists’ Reporting of Breast Cancer. 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_48

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  • DOI: https://doi.org/10.1007/978-3-319-07887-8_48

  • 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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