Mostafa Alabousi1, Nanxi Zha1, Jean-Paul Salameh2,3, Lucy Samoilov4, Anahita Dehmoobad Sharifabadi2, Alex Pozdnyakov5, Behnam Sadeghirad6,7, Vivianne Freitas8, Matthew D F McInnes9,10, Abdullah Alabousi11. 1. Department of Radiology, McMaster University, Hamilton, ON, Canada. 2. Department of Medicine, University of Ottawa, Ottawa, ON, Canada. 3. Department of Clinical Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada. 4. The Schulich School of Medicine, Western University, London, ON, Canada. 5. The Michael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada. 6. Department of Health Research Methods, Evidence, and Impact (HEI), McMaster University, Hamilton, Ontario, Canada. 7. The Michael G. DeGroote Institute for Pain Research and Care, McMaster University, Hamilton, Ontario, Canada. 8. Joint Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada. 9. Department of Radiology and Epidemiology, University of Ottawa, Ottawa, Canada. 10. Ottawa Hospital Research Institute, Clinical Epidemiology Program, Ottawa, Canada. 11. Department of Radiology, McMaster University, St. Joseph's Healthcare, Hamilton, ON, Canada. abdullah.alabousi@medportal.ca.
Abstract
OBJECTIVES: No 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. METHODS: MEDLINE 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. RESULTS: Thirty-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). CONCLUSIONS: DBT 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.
OBJECTIVES: No 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. METHODS: MEDLINE 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. RESULTS: Thirty-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). CONCLUSIONS: DBT 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.
Entities:
Keywords:
Breast neoplasms; Mammography; Meta-analysis; Sensitivity and specificity; Systematic review
Authors: Gerald Gui; Effrosyni Panopoulou; Sarah Tang; Dominique Twelves; Mohammed Kabir; Ann Ward; Catherine Montgomery; Ashutosh Nerurkar; Peter Osin; Clare M Isacke Journal: Breast Cancer Res Treat Date: 2021-01-04 Impact factor: 4.872
Authors: Nina Ditsch; Achim Wöcke; Michael Untch; Christian Jackisch; Ute-Susann Albert; Maggie Banys-Paluchowski; Ingo Bauerfeind; Jens-Uwe Blohmer; Wilfried Budach; Peter Dall; Eva Maria Fallenberg; Peter A Fasching; Tanja N Fehm; Michael Friedrich; Bernd Gerber; Oleg Gluz; Nadia Harbeck; Jörg Heil; Jens Huober; Hans H Kreipe; David Krug; Thorsten Kühn; Sherko Kümmel; Cornelia Kolberg-Liedtke; Sibylle Loibl; Diana Lüftner; Michael Patrick Lux; Nicolai Maass; Christoph Mundhenke; Ulrike Nitz; Tjoung-Won Park-Simon; Toralf Reimer; Kerstin Rhiem; Achim Rody; Marcus Schmidt; Andreas Schneeweiss; Florian Schütz; Hans-Peter Sinn; Christine Solbach; Erich-Franz Solomayer; Elmar Stickeler; Christoph Thomssen; Isabell Witzel; Volkmar Müller; Wolfgang Janni; Marc Thill Journal: Breast Care (Basel) Date: 2022-05-05 Impact factor: 2.268
Authors: Carlos Canelo-Aybar; Lourdes Carrera; Jessica Beltrán; Margarita Posso; David Rigau; Annette Lebeau; Axel Gräwingholt; Xavier Castells; Miranda Langendam; Elsa Pérez; Paolo Giorgi Rossi; Ruben Van Engen; Elena Parmelli; Zuleika Saz-Parkinson; Pablo Alonso-Coello Journal: Cancer Med Date: 2021-03-05 Impact factor: 4.452