Radiologists’ Preferences for Digital Mammographic Display

Pisano, Etta D.; Cole, Elodia B.; Major, Stacey A; Zong, Shuqan; Hemminger, Bradley M.; Muller, Keith E.; Johnston, Richard; Walsh, Ruth; Conant, Emily F.; Fajardo, Laurie L.; Feig, Stephen A.; Nishikawa, Robert M.; Yaffe, Martin J.; Williams, Mark B.; Aylward, Stephen

doi:10.1148/radiology.216.3.r00se48820

Cited by 69 publications

(33 citation statements)

References 4 publications

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“…These algorithms differ between the manufacturers and, unfortunately due to competition between vendors, detailed information on how these work is difficult to acquire. In a small study, the visibility of lesions using different image-processing algorithms was compared [4]. Interestingly, the algorithms preferred by the radiologists did not always match those recommended by the vendors.…”

Section: Introductionmentioning

confidence: 99%

Full-field digital mammography compared to screen film mammography in the prevalent round of a population-based screening programme: the Vestfold County Study

et al. 2007

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The purpose of the study was to compare the performance of full-field digital mammography (FFDM) with soft-copy reading to screen film mammography (SFM) used during the first prevalent 2-year round of population-based screening. A total of 18,239 women aged 50-69 years were screened with FFDM as part of the Norwegian Breast Cancer Screening Programme (NBCSP). Process indicators were compared to data from 324,763 women screened with SFM using the common national database of the NBCSP. The cancer detection rates were 0.77% (140/18,239) for FFDM and 0.65% (2,105/324,763) for SFM (p = 0.058). For ductal carcinoma in situ (DCIS) alone, the results were: FFDM 0.21% (38/18,239) compared to SFM 0.11% (343/324,763) (p < 0.001). Recall rates due to positive mammography were for FFDM 4.09% (746/18,239), while for SFM 4.16% (13,520/324,764) (p = 0.645), due to technically insufficient imaging: FFDM 0.22% (40/18,239) versus SFM 0.61% (1,993/324,763) (p < 0.001). The positive predictive value (PPV) in the FFDM group was 16.6% (140/843), while 13.5% (2,105/15,537) for SFM (p = 0.014). No statistically significant differences were recorded concerning histological morphology, tumour size, or lymph node involvement. In conclusion FFDM had a significantly higher detection rate for DCIS than SFM. For invasive cancers no difference was seen. FFDM also had a significantly higher PPV and a significantly lower technical recall rate.

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Section: Introductionmentioning

confidence: 99%

Full-field digital mammography compared to screen film mammography in the prevalent round of a population-based screening programme: the Vestfold County Study

et al. 2007

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“…The visibility of lesion features is influenced substantially by the image processing algorithm and whether the lesion lies within a dense or fatty background (10). We believe that this is the most likely explanation for the conflicting results for dense and fatty breasts.…”

Section: Discussionmentioning

confidence: 96%

“…Screen-film mammography tended nonsignificantly to perform better in the subset of women aged 65 years or older with fatty breasts because of the improved conspicuity of lesions on film hard-copy images in this subgroup. This suggests that digital system manufacturers should improve the quality of their image processing algorithms, especially for fatty breastsperhaps by applying different algorithms, for different breast densities, ideally based on reader performance data and not simply aesthetic factors (10).…”

Section: Discussionmentioning

confidence: 99%

Cancer Cases from ACRIN Digital Mammographic Imaging Screening Trial: Radiologist Analysis with Use of a Logistic Regression Model

Pisano¹,

Acharyya²,

Cole³

et al. 2009

Radiology

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Purpose:To determine which factors contributed to the Digital Mammographic Imaging Screening Trial (DMIST) cancer detection results. Materials and Methods:This project was HIPAA compliant and institutional review board approved. Seven radiologist readers reviewed the film hard-copy (screen-film) and digital mammograms in DMIST cancer cases and assessed the factors that contributed to lesion visibility on both types of images. Two multinomial logistic regression models were used to analyze the combined and condensed visibility ratings assigned by the readers to the paired digital and screen-film images. Results:Readers most frequently attributed differences in DMIST cancer visibility to variations in image contrast-not differences in positioning or compression-between digital and screen-film mammography. The odds of a cancer being more visible on a digital mammogram-rather than being equally visible on digital and screen-film mammogramswere significantly greater for women with dense breasts than for women with nondense breasts, even with the data adjusted for patient age, lesion type, and mammography system (odds ratio, 2.28; P Ͻ .0001). The odds of a cancer being more visible at digital mammography-rather than being equally visible at digital and screen-film mammography-were significantly greater for lesions imaged with the General Electric digital mammography system than for lesions imaged with the Fischer (P ϭ .0070) and Fuji (P ϭ .0070) devices. Conclusion:The significantly better diagnostic accuracy of digital mammography, as compared with screen-film mammography, in women with dense breasts demonstrated in the DMIST was most likely attributable to differences in image contrast, which were most likely due to the inherent system performance improvements that are available with digital mammography. The authors conclude that the DMIST results were attributable primarily to differences in the display and acquisition characteristics of the mammography devices rather than to reader variability.

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“…Image processing may vary substantially among different digital mammography vendors, even endangering interoperability between mammography review workstations. Not surprisingly, there is also no agreement on the optimal image processing among radiologists [61], and care has to be taken not to prolong reading times by switching too much between different image processing and window settings.…”

Section: Impact On Workflowmentioning

confidence: 99%