Diagnostic value of the stand-alone synthetic image in digital breast tomosynthesis examinations

Garayoa, Julia; Chevalier, M.; Castillo, María del Pilar; Mahíllo‐Fernández, Ignacio; Ouahabi, Najim Amallal El; Estrada, Carmen; Tejerina, A.; Benitez, Olivia; Valverde, Julio

doi:10.1007/s00330-017-4991-9

Cited by 18 publications

(12 citation statements)

References 23 publications

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“…Specificity was shown to be lower for microcalcifications and higher for distortions 45. In more recent studies, a similar specificity between SM and DM, with or without DBT, was found 44,97,98. In conclusion, specificity was shown to be at least equivalent for Hologic DM and SM, and higher for DBT&SM when compared to DM alone.…”

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosysupporting

confidence: 55%

“…The TOMMY trial, with 7060 participants, has shown an overall sensitivity of 87% for DM alone, and 88% for DBT&SM, ie not a significant difference between the two 45,51. Similar results were found specifically for microcalcifications 97,98. In two other studies a different there was an increased sensitivity by SM and DBT&SM compared to DM alone 43,44…”

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 59%

“…Similarly, for microcalcifications, the comparison of DBT&SM versus DM&DBT versus SM versus DM concluded in a non-significant difference in AUC amongst the above methods 97. The direct comparison between DM and SM has shown a non-statistically significant difference in the AUC of the two systems 98…”

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 99%

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<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Hadjipanteli

Kontos

Constantinidou

2019

CMAR

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Aim: Digital Breast Tomosynthesis (DBT), with or without Digital Mammography (DM) or Synthetic Mammography (SM), has been introduced or is under consideration for its introduction in breast cancer screening in several countries, as it has been shown that it has advantages over DM. Despite this there is no agreement on how to implement DBT in screening, and in many cases there is a lack of official guidance on the optimum usage of each commercially available system. The aim of this review is to carry out a manufacturerspecific summary of studies on the implementation of DBT in breast cancer screening. Methods: An exhaustive literature review was undertaken to identify clinical observer studies that evaluated at least one of five common metrics: sensitivity, specificity, area under the curve (AUC) of the receiver-operating characteristics (ROC) analysis, recall rate and cancer detection rate. Four common DBT implementation methods were discussed in this review: (1) DBT, (2) DM with DBT, (3) 1-view DBT with or without 1-view DM or 2-view DM and (4) DBT with SM. Results: A summary of 89 studies, selected from a database of 677 studies, on the assessment of the implementation of DBT in breast cancer screening is presented in tables and discussed in a manufacturer-and metric-specific approach. Much more studies were carried out using some DBT systems than others. For one implementation method of DBT by one manufacturer there is a shortage of studies, for another implementation there are conflicting results. In some cases, there is a strong agreement between studies, making the advantages and disadvantages of each system clear. Conclusion: The optimum implementation method of DBT in breast screening, in terms of diagnostic benefit and patient radiation dose, for one manufacturer does not necessarily apply to other manufacturers.

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Section: Results: Studies On the Optimal Use Of Digital Breast Tomosysupporting

confidence: 55%

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 59%

Section: Results: Studies On the Optimal Use Of Digital Breast Tomosymentioning

confidence: 99%

See 1 more Smart Citation

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Hadjipanteli

Kontos

Constantinidou

2019

CMAR

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“…We collected sets of DM examinations that were read by multiple radiologists during other unrelated, and previously completed, retrospective multi-reader multi-case (MRMC) observer studies (25)(26)(27)(28)(29)(30)(31)(32). In those studies, DM was compared to another modality (eg, digital breast tomosynthesis) for breast cancer detection in cancer-enriched datasets.…”

Section: Digital Mammogramsmentioning

confidence: 99%

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

Rodríguez‐Ruiz¹,

Lång²,

Gubern-Mérida³

et al. 2019

JNCI: Journal of the National Cancer Institute

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443

293

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Background Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an AI system to that of radiologists in detecting breast cancer in DM. Methods Nine multi-reader, multi-case study datasets previously used for different research purposes in seven countries were collected. Each dataset consisted of DM exams acquired with systems from four different vendors, multiple radiologists’ assessments per exam, and ground truth verified by histopathological analysis or follow-up, yielding a total of 2652 exams (653 malignant) and interpretations by 101 radiologists (28 296 independent interpretations). An AI system analyzed these exams yielding a level of suspicion of cancer present between 1 and 10. The detection performance between the radiologists and the AI system was compared using a noninferiority null hypothesis at a margin of 0.05. Results The performance of the AI system was statistically noninferior to that of the average of the 101 radiologists. The AI system had a 0.840 (95% confidence interval [CI] = 0.820 to 0.860) area under the ROC curve and the average of the radiologists was 0.814 (95% CI = 0.787 to 0.841) (difference 95% CI = −0.003 to 0.055). The AI system had an AUC higher than 61.4% of the radiologists. Conclusions The evaluated AI system achieved a cancer detection accuracy comparable to an average breast radiologist in this retrospective setting. Although promising, the performance and impact of such a system in a screening setting needs further investigation.

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“…According to a recent Spanish observational, retrospective, single-centre, multireader blinded study, the performance of stand-alone synthetic image mammography was not inferior for lesion visibility and for lesion BIRADS categorisation as compared with full-field DM. 18 …”

Section: Introductionmentioning

confidence: 99%

Digital breast tomosynthesis plus synthesised images versus standard full-field digital mammography in population-based screening (TOSYMA): protocol of a randomised controlled trial

et al. 2018

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IntroductionDevelopment of digital breast tomosynthesis (DBT) provides a technology that generates three-dimensional data sets, thus reducing the pitfalls of overlapping breast tissue. Observational studies suggest that the combination of two-dimensional (2D) digital mammography and DBT increases diagnostic accuracy. However, because of duplicate exposure, this comes at the cost of an augmented radiation dose. This undesired adverse impact can be avoided by using synthesised 2D images reconstructed from the DBT data (s2D).We designed a diagnostic superiority trial on a high level of evidence with the aim of providing a comparison of screening efficacy parameters resulting from DBT+s2D versus the current screening standard 2D full-field digital mammography (FFDM) in a multicentre and multivendor setting on the basis of the quality-controlled, population-based, biennial mammography screening programme in Germany.Methods and analysis80 000 women in the eligible age 50–69 years attending the routine mammography screening programme and willing to participate in the TOSYMA trial will be assigned by 1:1 randomisation to either the intervention arm (DBT+s2D) or the control arm (FFDM) during a 12-month recruitment period in screening units of North Rhine-Westphalia and Lower Saxony. State cancer registries will provide the follow-up of interval cancers.Primary endpoints are the detection rate of invasive breast cancers at screening examination and the cumulative incidence of interval cancers in the 2 years after a negative examination. Secondary endpoints are the detection rate of ductal carcinoma in situ and of tumour size T1, the recall rate for assessment, the positive predictive value of recall and the cumulative 12-month incidence of interval cancers. An adaptive statistical design with one interim analysis provides the option to modify the design.Ethics and disseminationThis protocol has been approved by the local medical ethical committee (2016-132-f-S). Results will be submitted to international peer-reviewed journals.Trial registrationNCT03377036; Pre-results.

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Diagnostic value of the stand-alone synthetic image in digital breast tomosynthesis examinations

Cited by 18 publications

References 23 publications

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

<p>The role of digital breast tomosynthesis in breast cancer screening: a manufacturer- and metrics-specific analysis</p>

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists

Digital breast tomosynthesis plus synthesised images versus standard full-field digital mammography in population-based screening (TOSYMA): protocol of a randomised controlled trial

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