Evidence map›Paper›PMID 31900699›Full record

SynthesisEuropean radiology2020

Digital breast tomosynthesis for breast cancer detection: a diagnostic test accuracy systematic review and meta-analysis.

Mostafa Alabousi, Nanxi Zha, Jean-Paul Salameh, Lucy Samoilov, Anahita Dehmoobad Sharifabadi, Alex Pozdnyakov, Behnam Sadeghirad, Vivianne Freitas, Matthew D F McInnes, Abdullah Alabousi

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 9 pooled it
5.6field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

31 citing papers in PubMed, 9 syntheses or guidelines pooled it, 70 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Pooled it
  10. Trial
  11. Trial
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 6 institutions in 1 country.

Mostafa AlabousiDepartment of Radiology, McMaster University, Hamilton, ON, Canada.
Nanxi ZhaDepartment of Radiology, McMaster University, Hamilton, ON, Canada.
Jean-Paul SalamehDepartment of Medicine, University of Ottawa, Ottawa, ON, Canada.
Lucy SamoilovThe Schulich School of Medicine, Western University, London, ON, Canada.
Anahita Dehmoobad SharifabadiDepartment of Medicine, University of Ottawa, Ottawa, ON, Canada.
Alex PozdnyakovThe Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.
Behnam SadeghiradDepartment of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, Ontario, Canada.
Vivianne FreitasJoint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Matthew D F McInnesDepartment of Radiology and Epidemiology, University of Ottawa, Ottawa, Canada.
Abdullah AlabousiDepartment of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, ON, Canada. abdullah.alabousi@medportal.ca.ORCID http://orcid.org/0000-0001-8481-5087
McMaster University · CAUniversity of Ottawa · CAImpact · CASt. Joseph’s Healthcare Hamilton · CAUniversity of Toronto · CAWestern University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesNo consensus exists on digital breast tomosynthesis (DBT) utilization for breast cancer detection. We performed a diagnostic test accuracy systematic review and meta-analysis comparing DBT, combined DBT and digital mammography (DM), and DM alone for breast cancer detection in average-risk women.

methodsMEDLINE and EMBASE were searched until September 2018. Comparative design studies reporting on the diagnostic accuracy of DBT and/or DM for breast cancer detection were included. Demographic, methodologic, and diagnostic accuracy data were extracted. Risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-2 tool. Accuracy metrics were pooled using bivariate random-effects meta-analysis. The impact of multiple covariates was assessed using meta-regression. PROSPERO ID: CRD 42018111287.

resultsThirty-eight studies reporting on 488,099 patients (13,923 with breast cancer) were included. Eleven studies were at low risk of bias. DBT alone, combined DBT and DM, and DM alone demonstrated sensitivities of 88% (95% confidence interval [CI] 83-92), 88% (CI 83-92), and 79% (CI 75-82), as well as specificities of 84% (CI 76-89), 81% (CI 73-88), and 79% (CI 71-85), respectively. The greater sensitivities of DBT alone and combined DBT and DM compared to DM alone were preserved in the combined meta-regression models accounting for other covariates (p = 0.003-0.006). No significant difference in diagnostic accuracy between DBT alone and combined DBT and DM was identified (p = 0.175-0.581).

conclusionsDBT is more sensitive than DM, while the addition of DM to DBT provides no additional diagnostic benefit. Consideration of these findings in breast cancer imaging guidelines is recommended. KEY POINTS: • Digital breast tomosynthesis with or without additional digital mammography is more sensitive in detecting breast cancer than digital mammography alone in women at average risk for breast cancer. • The addition of digital mammography to digital breast tomosynthesis provides no additional diagnostic benefit in detecting breast cancer compared to digital breast tomosynthesis alone. • The specificity of digital breast tomosynthesis with or without additional digital mammography is no different than digital mammography alone in the detection of breast cancer.

Indexed as

Breast NeoplasmsEarly Detection of CancerFemaleHumansMammographyReproducibility of ResultsBreast neoplasmsMammographyMeta-analysisSensitivity and specificitySystematic review

Identifiers

PMID31900699
OpenAlexW2996762549

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.