Digital Mammography versus Digital Mammography Plus Tomosynthesis in Breast Cancer Screening: The Oslo Tomosynthesis Screening Trial

Skaane, Per; Bandos, Andriy I.; Niklason, Loren T.; Sebuødegård, Sofie; Østerås, Bjørn Helge; Gullien, Randi; Gur, David; Hofvind, Solveig

doi:10.1148/radiol.2019182394

Cited by 125 publications

(76 citation statements)

References 24 publications

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“…Therefore, the sensitivity of DM is likely to be overestimated when cancers that are only detected by DBT are not taken into account (unless there has been adequate follow-up). However, in a recent study using DM and DBT in over 24,000 women where DBT-only cancers were also counted, the modality sensitivity of DM was approximately 80% which is comparable to the overall modality sensitivity of 79% used in our study [36]. Nevertheless, as it is likely that there might still be some overestimation of DM sensitivity, this might lead to a slight underestimation of DBT cost-effectiveness.…”

Section: Discussionsupporting

confidence: 66%

“…Several European countries, and Australia, have recently evaluated DBT in trials performed in population-based screening programs [12,35,36,48]. However, DBT is a relatively new technology, and whether DBT could be an alternative cost-effective replacement for DM in a population screening program is a matter of ongoing investigations.…”

Section: Resultsmentioning

confidence: 99%

“…However, no matter which value of these two DBT costs is used, DBT screening only for women with dense breasts could be cost-effective, since the threshold for dense breasts is within the reported DBT sensitivity range. Another important issue is that previous studies have shown that DBT detects more tumours than DM [36,38]. Therefore, the sensitivity of DM is likely to be overestimated when cancers that are only detected by DBT are not taken into account (unless there has been adequate follow-up).…”

Section: Discussionmentioning

confidence: 99%

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The cost-effectiveness of digital breast tomosynthesis in a population breast cancer screening program

et al. 2020

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Objectives To evaluate at which sensitivity digital breast tomosynthesis (DBT) would become cost-effective compared to digital mammography (DM) in a population breast cancer screening program, given a constant estimate of specificity. Methods In a microsimulation model, the cost-effectiveness of biennial screening for women aged 50–75 was simulated for three scenarios: DBT for women with dense breasts and DM for women with fatty breasts (scenario 1), DBT for the whole population (scenario 2) or maintaining DM screening (reference). For DM, sensitivity was varied depending on breast density from 65 to 87%, and for DBT from 65 to 100%. The specificity was set at 96.5% for both DM and DBT. Direct medical costs were considered, including screening, biopsy and treatment costs. Scenarios were considered to be cost-effective if the incremental cost-effectiveness ratio (ICER) was below €20,000 per life year gain (LYG). Results For both scenarios, the ICER was more favourable at increasing DBT sensitivity. Compared with DM screening, 0.8–10.2% more LYGs were found when DBT sensitivity was at least 75% for scenario 1, and 4.7–18.7% when DBT sensitivity was at least 80% for scenario 2. At €96 per DBT, scenario 1 was cost-effective at a DBT sensitivity of at least 90%, and at least 95% for scenario 2. At €80 per DBT, these values decreased to 80% and 90%, respectively. Conclusion DBT is more likely to be a cost-effective alternative to mammography in women with dense breasts. Whether DBT could be cost-effective in a general population highly depends on DBT costs. Key Points • DBT could be a cost-effective screening modality for women with dense breasts when its sensitivity is at least 90% at a maximum cost per screen of €96. • DBT has the potential to be cost-effective for screening all women when sensitivity is at least 90% at a maximum cost per screen of €80. • Whether DBT could be used as an alternative to mammography for screening all women is highly dependent on the cost of DBT per screen.

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Section: Discussionsupporting

confidence: 66%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The cost-effectiveness of digital breast tomosynthesis in a population breast cancer screening program

et al. 2020

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“…Tomosynthesis certainly increases cancer detection, but the additional cancers are predominantly low grade [15,41] and to date, there has been no impact on interval cancer rates [12,42]. The additional detection of small grade 3 cancers is unclear, as grade size distribution has not been reported, but current evidence does not suggest that this is the answer to increasing the detection of small fast growing cancers destined to become interval cancers.…”

Section: Is There a Role For New Technology?mentioning

confidence: 97%

“…None of the studies, to date, has been powered to measure interval cancer rates but initial reports have shown minimal if any reduction [14]. The information on grade distribution of the cancers detected suggests a preferential detection for lower grade disease [14,15]. Both screen detection rates and interval cancer rates therefore indicate a high sensitivity for lower grade invasive cancers.…”

Section: Introductionmentioning

confidence: 99%

An analysis of screen-detected invasive cancers by grade in the English breast cancer screening programme: are we failing to detect sufficient small grade 3 cancers?

et al. 2020

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Objective Randomised controlled trials have shown a reduction in breast cancer mortality from mammography screening and it is the detection of high-grade invasive cancers that is responsible for much of this effect. We determined the detection rates of invasive cancers by grade, size and type of screen and estimated relative sensitivities with emphasis on grade 3 detection. Methods This observational study analysed data from over 11 million screening episodes (67,681 invasive cancers) from the English NHS breast screening programme over seven screening years 2009/2010 to 2015/2016 for women aged 45–70. Results At prevalent (first) screens (which are unaffected by screening interval), the detection rate of small (< 15 mm) invasive cancers was 0.95 per 1000 for grade 1, but for grade 3 only 0.30 per 1000. The ratio of small (< 15 mm) to large (≥ 15 mm) cancers was 1.8:1 for grade 1 but reversed to 0.5:1 for grade 3. We estimated that the relative sensitivity for grade 3 invasive cancers was 52% of that for grade 1 and the relative sensitivity for small (< 15 mm) grade 3 only 26% of that for small (< 15 mm) grade 1 invasive cancers. Conclusions Sensitivity for small grade 3 invasive cancers is poor compared with that for grade 1 and 2 invasive cancers and larger grade 3 malignancies. This observation is likely a limitation of the current technology related to the absence of identifiable mammographic features for small high-grade cancers. Future work should focus on technologies and strategies to improve detection of these clinically most significant cancers. Key Points • The detection of small high-grade invasive cancers is vital to reduce breast cancer mortality. • We estimate the sensitivity for small grade 3 invasive cancers may be only 26% of that of small grade 1 invasive cancers. This is likely to be associated with the non-specific mammographic features for these cancers. • New technologies and appropriate strategies using current technology are required to maximise the detection of small grade 3 invasive cancers.

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Breast cancer diagnosis based on a new improved Elman neural network optimized by meta‐heuristics

Zheng

Wang

Zhang

et al. 2019

Int J Imaging Syst Tech

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In this article, a new optimized method for diagnosing and analyzing breast cancer from the mammography images is presented. In this regard, preprocessing is used to remove the Gaussian noises that are used to happen in the mammography images and also to remove the additional areas. Then, image segmentation is performed on the images to determine the areas where the contrast material is perceptible. Afterward, combined feature extraction based on a discrete wavelet transform and gray‐level co‐occurrence matrix is proposed for extracting the important information fromthe images. Finally, a new classification model based on an improved Elman neural network (ENN) has been proposed. The ENN is optimized by an improvedversion of the collective animal behavior algorithm. Simulation results areimplemented to the Mammographic Image Analysis Society database and the resultsare compared with three different methods.

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Digital Mammography versus Digital Mammography Plus Tomosynthesis in Breast Cancer Screening: The Oslo Tomosynthesis Screening Trial

Cited by 125 publications

References 24 publications

The cost-effectiveness of digital breast tomosynthesis in a population breast cancer screening program

The cost-effectiveness of digital breast tomosynthesis in a population breast cancer screening program

An analysis of screen-detected invasive cancers by grade in the English breast cancer screening programme: are we failing to detect sufficient small grade 3 cancers?

Breast cancer diagnosis based on a new improved Elman neural network optimized by meta‐heuristics

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