scholarly journals Does lateral arm technique decrease the rate of clip migration in stereotactic and tomosynthesis-guided biopsies?

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Olena Weaver ◽  
Ethan O. Cohen ◽  
Rachel E. Perry ◽  
Hilda H. Tso ◽  
Kanchan Phalak ◽  
...  
Keyword(s):  
Statistical Difference ◽  
Digital Breast Tomosynthesis ◽  
Sampling Techniques ◽  
Technical Failure ◽  
Tissue Sampling ◽  
Success Rates ◽  
Single Institution ◽  
Breast Tomosynthesis ◽  
Hematoma Formation ◽  
Stereotactic Guidance

Abstract Background Mammography-guided vacuum-assisted biopsies (MGVAB) can be done with stereotaxis or digital breast tomosynthesis guidance. Both methods can be performed with a conventional (CBA) or a lateral arm biopsy approach (LABA). Marker clip migration is relatively frequent in MGVAB (up to 44%), which in cases requiring surgery carries a risk of positive margins and re-excision. We aimed to compare the rates of clip migration and hematoma formation between the CBA and LABA techniques of prone MGVAB. Our HIPAA compliant retrospective study included all consecutive prone MGVAB performed in a single institution over a 20-month period. The LABA approach was used with DBT guidance; CBA utilized DBT or stereotactic guidance. The tissue sampling techniques were otherwise identical. Results After exclusion, 389 biopsies on 356 patients were analyzed. LABA was done in 97 (25%), and CBA in 292 (75%) cases. There was no statistical difference in clip migration rate with either 1 cm or 2 cm distance cut-off [15% for CBA and 10% for LABA for 1 cm threshold (p = 0.31); 5.8% or CBA and 3.1% or LABA for 2 cm threshold (p = 0.43)]. There was no difference in the rate of hematoma formation (57.5% in CDB and 50.5% in LABA, p = 0.24). The rates of technical failure were similar for both techniques (1.7% for CBA and 3% for LABA) with a combined failure rate of 1%. Conclusions LABA and CBA had no statistical difference in clip migration or hematoma formation rates. Both techniques had similar success rates and may be helpful in different clinical situations.

scholarly journals Positive Predictive Value of Tomosynthesis-guided Biopsies of Architectural Distortions Seen on Digital Breast Tomosynthesis and without an Ultrasound Correlate

2019 ◽  
Vol 9 ◽  
pp. 53 ◽  
Author(s):  
Gopal R. Vijayaraghavan ◽  
Adrienne Newburg ◽  
Srinivasan Vedantham
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Inclusion Criteria ◽  
Single Institution ◽  
Breast Tomosynthesis ◽  
Diagnostic Categories ◽  
High Risk Lesion ◽  
Prior Literature

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.

scholarly journals Comparison of radiation dose between 2D digital stereotactic versus digital breast tomosynthesis-guided breast biopsies

2021 ◽  
Vol 134 ◽  
pp. 109407
Author(s):  
T. Amir ◽  
S.P Zuckerman ◽  
B. Barufaldi ◽  
A.D Maidment ◽  
E.F Conant
Keyword(s):  
Radiation Dose ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals A Decision Support System Based on BI-RADS and Radiomic Classifiers to Reduce False Positive Breast Calcifications at Digital Breast Tomosynthesis: A Preliminary Study

2021 ◽  
Vol 11 (6) ◽  
pp. 2503
Author(s):  
Marco Alì ◽  
Natascha Claudia D’Amico ◽  
Matteo Interlenghi ◽  
Marina Maniglio ◽  
Deborah Fazzini ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Breast Imaging ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Preliminary Study ◽  
Personalized Risk ◽  
Fold Cross Validation ◽  
Malignant Diagnosis

Digital breast tomosynthesis (DBT) studies were introduced as a successful help for the detection of calcification, which can be a primary sign of cancer. Expert radiologists are able to detect suspicious calcifications in DBT, but a high number of calcifications with non-malignant diagnosis at biopsy have been reported (false positives, FP). In this study, a radiomic approach was developed and applied on DBT images with the aim to reduce the number of benign calcifications addressed to biopsy and to give the radiologists a helpful decision support system during their diagnostic activity. This allows personalizing patient management on the basis of personalized risk. For this purpose, 49 patients showing microcalcifications on DBT images were retrospectively included, classified by BI-RADS (Breast Imaging-Reporting and Data System) and analyzed. After segmentation of microcalcifications from DBT images, radiomic features were extracted. Features were then selected with respect to their stability within different segmentations and their repeatability in test–retest studies. Stable radiomic features were used to train, validate and test (nested 10-fold cross-validation) a preliminary machine learning radiomic classifier that, combined with BI-RADS classification, allowed a reduction in FP of a factor of 2 and an improvement in positive predictive value of 50%.

scholarly journals A deep learning classifier for digital breast tomosynthesis

2021 ◽  
Vol 83 ◽  
pp. 184-193
Author(s):  
R. Ricciardi ◽  
G. Mettivier ◽  
M. Staffa ◽  
A. Sarno ◽  
G. Acampora ◽  
...  
Keyword(s):  
Deep Learning ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
Learning Classifier
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