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Breast cancer detection rates using four different types of mammography detectors



To compare the performance of different types of detectors in breast cancer detection.


A mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening.


No significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors’ FoMs were significantly lower than for DR detectors. The calcification cluster’s FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15 % and 22 % lower, respectively, than for DR detectors.


Cancer detection is affected by detector type, and the use of CR in mammography should be reconsidered.

Key Points

The type of mammography detector can affect the cancer detection rates.

CR detectors performed worse than DR detectors in mammography.

Needle phosphor CR performed better than powder phosphor CR.

Calcification clusters detection is more sensitive to detector type than other cancers.

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Fig. 1
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Fig. 5



Cancer detection fraction


Computed radiography


Degrees of freedom


Digital radiography


Figure of merit


False recall fraction


Jackknife alternative free-response receiver operating characteristics


Mean glandular dose


Needle image plate


Powder image plate


Region of interest


Screen film mammography


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We are indebted to the clinical support, clinical advice and participation of observers: Sue Barter, Susan Fleischer-Thompson, Christine Flis, Samantha Heller, Lisanne Khoo, Penny Moyle, Charul Patel, Mamatha Reddy, Mary Sinclair, Kathryn Taylor, Louise Wilkinson, Paula Willsher. The scientific guarantor of this publication is Prof Kenneth Young. The authors of this manuscript declare relationships with the following companies: Agfa Healthcare, Carestream Health Inc., and MIS Healthcare. This study has received funding by a Cancer research-UK (CRUK)–Optimam2 grant, number: C30682/A17321. One author (DPC) was supported in part by a grant from the Department of Health and Human Services, National Institutes of Health (R01-EB005243). One of the authors (DPC) has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was waived by the Institutional Review Board. Some study subjects or cohorts have been previously reported in ‘Warren LM, Given-Wilson RM, Wallis MG, et al. (2014) The effect of image processing on the detection of cancers in digital mammography. Am J Roentgenol; 203:387-393’. Methodology: retrospective, experimental study, multicentre study.

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Correspondence to Alistair Mackenzie.

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Mackenzie, A., Warren, L.M., Wallis, M.G. et al. Breast cancer detection rates using four different types of mammography detectors. Eur Radiol 26, 874–883 (2016).

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  • Breast cancer screening
  • Digital mammography
  • Computed radiography
  • Image quality
  • Observer study