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Lesion Detectability and Masking Disparity Assessment in Breast Tomosynthesis Across Diverse Populations Using In-Silico Imaging Trials

Barufaldi, Bruno ; Vimieiro, Rodrigo B. ; Dong, Vincent ; Tomic, Hanna LU ; Cao, Quy ; da Costa Vieira, Marcelo Andrade ; Bakic, Predrag R. LU ; Eby, Peter R. ; Zackrisson, Sophia LU and McCarthy, Anne Marie , et al. (2026) In IEEE Transactions on Medical Imaging 45(8). p.4433-4443
Abstract

Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same... (More)

Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same manner to avoid proprietary processing in lesion detection. Lesion detectability was assessed with Channelized Hotelling Observers. Regression and causal mediation analyses examined the relationships between race, density, and detectability. As result, detectability decreased with increasing density; for masses, the area under the receiver operating characteristic curve (ROC AUC) reduced significantly from 0.93 to 0.85 (BIRADS A to D), whereas for microcalcifications AUC decreased from 0.85 to 0.78 across the same density range. Discrimination remained higher for masses than calcifications (AUC = 0.89 vs. AUC = 0.82). Stratified analyses showed slightly higher detectability in Non-Hispanic Black women compared with Non-Hispanic White and Asian American women, largely reflecting differences in density. Mediation analysis revealed that breast density accounted for 38–55% of the observed race-associated detectability differences. Mediation analyses have shown that density is the dominant factor in detectability. These findings support the development of calibrated detection models and personalized screening strategies that account for breast density.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
breast cancer, Digital breast tomosynthesis, lesion detection, virtual imaging trials
in
IEEE Transactions on Medical Imaging
volume
45
issue
8
pages
11 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • pmid:42262937
  • scopus:105041498972
ISSN
0278-0062
DOI
10.1109/TMI.2026.3701599
language
English
LU publication?
yes
id
377c2ff6-c442-4a4e-af2b-ef3ad4a46af3
date added to LUP
2026-09-22 15:48:52
date last changed
2026-09-22 15:50:05
@article{377c2ff6-c442-4a4e-af2b-ef3ad4a46af3,
  abstract     = {{<p>Breast density impacts cancer detection by masking tumors within fibroglandular tissue and there are disparities in screening outcomes across racial groups. However, it remains unclear whether these differences reflect inherent tissue characteristics or systemic bias. To isolate the effect of breast density on lesion detectability across racial subgroups, we conducted a retrospective case-control study using raw tomosynthesis projections from 902 women (453 cases, 451 matched controls) across BI-RADS density categories and self-reported race. Identical in-silico spiculated masses (8–15 mm) and microcalcification clusters (10–14 mm) were inserted into the projections using a calibrated lesion model. Images were reconstructed in the same manner to avoid proprietary processing in lesion detection. Lesion detectability was assessed with Channelized Hotelling Observers. Regression and causal mediation analyses examined the relationships between race, density, and detectability. As result, detectability decreased with increasing density; for masses, the area under the receiver operating characteristic curve (ROC AUC) reduced significantly from 0.93 to 0.85 (BIRADS A to D), whereas for microcalcifications AUC decreased from 0.85 to 0.78 across the same density range. Discrimination remained higher for masses than calcifications (AUC = 0.89 vs. AUC = 0.82). Stratified analyses showed slightly higher detectability in Non-Hispanic Black women compared with Non-Hispanic White and Asian American women, largely reflecting differences in density. Mediation analysis revealed that breast density accounted for 38–55% of the observed race-associated detectability differences. Mediation analyses have shown that density is the dominant factor in detectability. These findings support the development of calibrated detection models and personalized screening strategies that account for breast density.</p>}},
  author       = {{Barufaldi, Bruno and Vimieiro, Rodrigo B. and Dong, Vincent and Tomic, Hanna and Cao, Quy and da Costa Vieira, Marcelo Andrade and Bakic, Predrag R. and Eby, Peter R. and Zackrisson, Sophia and McCarthy, Anne Marie and Maidment, Andrew D.A.}},
  issn         = {{0278-0062}},
  keywords     = {{breast cancer; Digital breast tomosynthesis; lesion detection; virtual imaging trials}},
  language     = {{eng}},
  number       = {{8}},
  pages        = {{4433--4443}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{IEEE Transactions on Medical Imaging}},
  title        = {{Lesion Detectability and Masking Disparity Assessment in Breast Tomosynthesis Across Diverse Populations Using In-Silico Imaging Trials}},
  url          = {{http://dx.doi.org/10.1109/TMI.2026.3701599}},
  doi          = {{10.1109/TMI.2026.3701599}},
  volume       = {{45}},
  year         = {{2026}},
}