The ability of digital breast tomosynthesis to reduce additional examinations in older women

Maha Gharaibeh; Ahmad Abu Alfwares; Eyhab Elobeid; Ruba Khasawneh; Liqa Rousan; Mwaffaq El-Heis; Mooath Al-Jarrah; Ahmed A Haj Hussein; Maryam Altalhi; Laith Abualigah

doi:10.3389/fmed.2023.1276434

The ability of digital breast tomosynthesis to reduce additional examinations in older women

Front Med (Lausanne). 2023 Nov 21:10:1276434. doi: 10.3389/fmed.2023.1276434. eCollection 2023.

Authors

Maha Gharaibeh¹, Ahmad Abu Alfwares², Eyhab Elobeid^{1

3}, Ruba Khasawneh¹, Liqa Rousan¹, Mwaffaq El-Heis¹, Mooath Al-Jarrah¹, Ahmed A Haj Hussein¹, Maryam Altalhi⁴, Laith Abualigah^{5

6

7

8

9

10

11

12}

Affiliations

¹ Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan.
² Department of Radiation Technology, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, Jordan.
³ Department of Emergency, Faculty of Medicine, Omdurman Islamic University, Omdurman, Sudan.
⁴ Department of Management Information Systems, College of Business Administration, Taif University, Taif, Saudi Arabia.
⁵ Computer Science Department, Al al-Bayt University, Mafraq, Jordan.
⁶ Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon.
⁷ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman, Jordan.
⁸ MEU Research Unit, Middle East University, Amman, Jordan.
⁹ College of Engineering, Yuan Ze University, Taoyuan, Taiwan.
¹⁰ School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang, Malaysia.
¹¹ School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya, Malaysia.
¹² Applied Science Research Center, Applied Science Private University, Amman, Jordan.

Abstract

Aims: To assess the diagnostic performance of digital breast tomosynthesis (DBT) in older women across varying breast densities and to compare its effectiveness for cancer detection with 2D mammography and ultrasound (U/S) for different breast density categories. Furthermore, our study aimed to predict the potential reduction in unnecessary additional examinations among older women due to DBT.

Methods: This study encompassed a cohort of 224 older women. Each participant underwent both 2D mammography and digital breast tomosynthesis examinations. Supplementary views were conducted when necessary, including spot compression and magnification, ultrasound, and recommended biopsies. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC) were calculated for 2D mammography, DBT, and ultrasound. The impact of DBT on diminishing the need for supplementary imaging procedures was predicted through binary logistic regression.

Results: In dense breast tissue, DBT exhibited notably heightened sensitivity and NPV for lesion detection compared to non-dense breasts (61.9% vs. 49.3%, p < 0.001) and (72.9% vs. 67.9%, p < 0.001), respectively. However, the AUC value of DBT in dense breasts was lower compared with non-dense breasts (0.425 vs. 0.670). Regarding the ability to detect calcifications, DBT demonstrated significantly improved sensitivity and NPV in dense breasts compared to non-dense breasts (100% vs. 99.2%, p < 0.001) and (100% vs. 94.7%, p < 0.001), respectively. On the other hand, the AUC value of DBT was slightly lower in dense breasts compared with non-dense (0.682 vs. 0.711). Regarding lesion detection for all cases between imaging examinations, the highest sensitivity was observed in 2D mammography (91.7%, p < 0.001), followed by DBT (83.7%, p < 0.001), and then ultrasound (60.6%, p < 0.001). In dense breasts, sensitivity for lesion detection was highest in 2D mammography (92.9%, p < 0.001), followed by ultrasound (76.2%, p < 0.001), and the last one was DBT. In non-dense breasts, sensitivities were 91% (p < 0.001) for 2D mammography, 50.7% (p < 0.001) for ultrasound, and 49.3% (p < 0.001) for DBT. In terms of calcification detection, DBT displayed significantly superior sensitivity compared to 2D mammography in both dense and non-dense breasts (100% vs. 91.4%, p < 0.001) and (99.2% vs. 78.5%, p < 0.001), respectively. However, the logistic regression model did not identify any statistically significant relationship (p > 0.05) between DBT and the four dependent variables.

Conclusion: Our findings indicate that among older women, DBT does not significantly decrease the requirement for further medical examinations.

Keywords: breast density; diagnostic performance; digital breast tomosynthesis (DBT); sensitivity; unnecessary additional examinations.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was financially supported via a funding grant by the Deanship of Scientific Research, Jordan University of Science and Technology, Irbid, Jordan. The researchers wish to thank the Deanship of Scientific Research at Taif University for funding this research.