UMMS Affiliation

Department of Radiology

Publication Date


Document Type



Diagnosis | Neoplasms | Radiology


Objective: The objective of the study was to determine the positive predictive value (PPV) of architectural distortions (AD) observed on digital breast tomosynthesis (DBT) and without an ultrasound (US) correlate.

Materials and Methods: In this single-institution, retrospective study, patients who underwent DBT-guided biopsies of AD without any associated findings on digital mammography (DM) or DBT, and without a correlate on targeted US exam, over a 14-month period were included in this study. All patients had DM and DBT and targeted US exams. The PPV was computed along with the exact 95% confidence limits (CL) using simple binomial proportions, with histopathology as the reference standard.

Results: A total of 45 ADs in 45 patients met the inclusion criteria. Histopathology indicated 6/45 (PPV: 13.3%, CL: 5.1-26.8%), ADs were malignant, including one high-risk lesion that was upgraded at surgery. ADs were appreciated only on DBT in 12/45 (26.7%) patients, and on both DBT and DM in 33/45 (73.3%) patients, and the corresponding PPV was 25% (3/12, CL: 5.5-57.2%) and 9.1% (3/33, CL: 1.9-24.3%), respectively. In all analyses, the observed PPV significantly exceeded the 2% probability of malignancy for Breast Imaging Reporting and Data System-3 diagnostic categories (P < 0.004).

Conclusions: The PPV of malignancy in DBT detected AD without an US correlate in our series of 45 cases was 6/45 (13.3%). In the absence of an US correlate, the PPV of AD is lower than that mentioned in prior literature but exceeds the 2% threshold to justify DBT-guided biopsy.


Architectural distortion, Breast, Cancer, Digital breast tomosynthesis, Positive predictive value

Rights and Permissions

© 2019 Published by Scientific Scholar on behalf of Journal of Clinical Imaging Science. This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

DOI of Published Version



J Clin Imaging Sci. 2019 Nov 18;9:53. doi: 10.25259/JCIS_134_2019. eCollection 2019. Link to article on publisher's site

Journal/Book/Conference Title

Journal of clinical imaging science

Related Resources

Link to Article in PubMed

PubMed ID


Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.