Digital breast tomosynthesis: computerized detection of microcalcifications in reconstructed breast volume using a 3D approach

Author(s):  
Heang-Ping Chan ◽  
Berkman Sahiner ◽  
Jun Wei ◽  
Lubomir M. Hadjiiski ◽  
Chuan Zhou ◽  
...  
Author(s):  
Åsne S Holen ◽  
Marthe Larsen ◽  
Nataliia Moshina ◽  
Gunvor G Wåade ◽  
Ioannis Sechopoulos ◽  
...  

Abstract Objective To investigate whether having the nipple imaged in profile was associated with breast characteristics or compression parameters, and whether it affected selected outcomes in screening with standard digital mammography or digital breast tomosynthesis. Methods In this IRB-approved retrospective study, results from 87 450 examinations (174 900 breasts) performed as part of BreastScreen Norway, 2016–2019, were compared by nipple in profile status and screening technique using descriptive statistics and generalized estimating equations. Unadjusted and adjusted odds ratios with 95% confidence intervals (95% CIs) were estimated for outcomes of interest, including age, breast volume, volumetric breast density, and compression force as covariates. Results Achieving the nipple in profile versus not in profile was associated with lower breast volume (845.1 cm3 versus 1059.9 cm3, P < 0.01) and higher mammographic density (5.6% versus 4.4%, P < 0.01). Lower compression force and higher compression pressure were applied to breasts with the nipple in profile (106.6 N and 11.5 kPa) compared to the nipple not in profile (110.8 N and 10.5 kPa, P < 0.01 for both). The adjusted odds ratio was 0.95 (95% CI: 0.88–1.02; P = 0.15) for recall and 0.92 (95% CI: 0.77–1.10; P = 0.36) for screen-detected cancer for nipple in profile versus not in profile. Conclusion Breast characteristics and compression parameters might hamper imaging of the nipple in profile. However, whether the nipple was in profile or not on the screening mammograms did not influence the odds of recall or screen-detected cancer, regardless of screening technique.


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

2021 ◽  
Vol 11 (6) ◽  
pp. 2503
Author(s):  
Marco Alì ◽  
Natascha Claudia D’Amico ◽  
Matteo Interlenghi ◽  
Marina Maniglio ◽  
Deborah Fazzini ◽  
...  

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%.


2021 ◽  
Vol 83 ◽  
pp. 184-193
Author(s):  
R. Ricciardi ◽  
G. Mettivier ◽  
M. Staffa ◽  
A. Sarno ◽  
G. Acampora ◽  
...  

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