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Potential Usefulness of Presentation of Histological Classifications with Computer-Aided Diagnosis (CAD) Scheme in Differential Diagnosis of Clustered Microcalcifications on Mammograms

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

Abstract

We compared the usefulness of the presentation of likelihood of histological classifications with that of malignancy evaluated by a CAD scheme in the differential diagnosis of clustered microcalcifications (MCs) on magnified spot mammograms. The likelihood of histological classifications was evaluated by the CAD scheme using 5 objective features that radiologists commonly use for describing MCs. The likelihood of malignancy was evaluated based on the likelihood of histological classifications. Unknown cases for an observer study consisted of 22 benign MCs (15 micro-cysts and 7 mastopathies) and 26 malignant MCs (10 DCISs of comedo type and 16 DCISs of noncomedo type). Thirteen observers independently provided their confidence level regarding the malignancy of the unknown case before viewing the evaluated result by the CAD scheme, after viewing the likelihood of malignancy and after viewing the likelihood of histological classifications. The results were evaluated with multi-reader, multi-case receiver operating characteristic (ROC) analysis. The average area under the curve (AUC) for all observers without CAD, with CAD for malignancy and with CAD for histological calcifications was 0.670, 0.802 and 0.819 (P < .01), respectively. The presentation of the likelihood of histological classifications improved radiologists’ performance than that of malignancy in the differential diagnosis of MCs.

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© 2014 Springer International Publishing Switzerland

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Nakayama, R., Namba, K., Watanabe, R., Nakahara, H., Smathers, R. (2014). Potential Usefulness of Presentation of Histological Classifications with Computer-Aided Diagnosis (CAD) Scheme in Differential Diagnosis of Clustered Microcalcifications on Mammograms. 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_17

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  • DOI: https://doi.org/10.1007/978-3-319-07887-8_17

  • 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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