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Published in: Breast Cancer Research 1/2022

Open Access 01-12-2022 | Breast Cancer | Research

Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence

Authors: Joshua Ogony, Thomas de Bel, Derek C. Radisky, Jennifer Kachergus, E. Aubrey Thompson, Amy C. Degnim, Kathryn J. Ruddy, Tracy Hilton, Melody Stallings-Mann, Celine Vachon, Tanya L. Hoskin, Michael G. Heckman, Robert A. Vierkant, Launia J. White, Raymond M. Moore, Jodi Carter, Matthew Jensen, Laura Pacheco-Spann, Jill E. Henry, Anna Maria Storniolo, Stacey J. Winham, Jeroen van der Laak, Mark E. Sherman

Published in: Breast Cancer Research | Issue 1/2022

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Abstract

Background

Breast terminal duct lobular units (TDLUs), the source of most breast cancer (BC) precursors, are shaped by age-related involution, a gradual process, and postpartum involution (PPI), a dramatic inflammatory process that restores baseline microanatomy after weaning. Dysregulated PPI is implicated in the pathogenesis of postpartum BCs. We propose that assessment of TDLUs in the postpartum period may have value in risk estimation, but characteristics of these tissues in relation to epidemiological factors are incompletely described.

Methods

Using validated Artificial Intelligence and morphometric methods, we analyzed digitized images of tissue sections of normal breast tissues stained with hematoxylin and eosin from donors ≤ 45 years from the Komen Tissue Bank (180 parous and 545 nulliparous). Metrics assessed by AI, included: TDLU count; adipose tissue fraction; mean acini count/TDLU; mean dilated acini; mean average acini area; mean “capillary” area; mean epithelial area; mean ratio of epithelial area versus intralobular stroma; mean mononuclear cell count (surrogate of immune cells); mean fat area proximate to TDLUs and TDLU area. We compared epidemiologic characteristics collected via questionnaire by parity status and race, using a Wilcoxon rank sum test or Fisher’s exact test. Histologic features were compared between nulliparous and parous women (overall and by time between last birth and donation [recent birth: ≤ 5 years versus remote birth: > 5 years]) using multivariable regression models.

Results

Normal breast tissues of parous women contained significantly higher TDLU counts and acini counts, more frequent dilated acini, higher mononuclear cell counts in TDLUs and smaller acini area per TDLU than nulliparas (all multivariable analyses p < 0.001). Differences in TDLU counts and average acini size persisted for > 5 years postpartum, whereas increases in immune cells were most marked ≤ 5 years of a birth. Relationships were suggestively modified by several other factors, including demographic and reproductive characteristics, ethanol consumption and breastfeeding duration.

Conclusions

Our study identified sustained expansion of TDLU numbers and reduced average acini area among parous versus nulliparous women and notable increases in immune responses within five years following childbirth. Further, we show that quantitative characteristics of normal breast samples vary with demographic features and BC risk factors.
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Metadata
Title
Towards defining morphologic parameters of normal parous and nulliparous breast tissues by artificial intelligence
Authors
Joshua Ogony
Thomas de Bel
Derek C. Radisky
Jennifer Kachergus
E. Aubrey Thompson
Amy C. Degnim
Kathryn J. Ruddy
Tracy Hilton
Melody Stallings-Mann
Celine Vachon
Tanya L. Hoskin
Michael G. Heckman
Robert A. Vierkant
Launia J. White
Raymond M. Moore
Jodi Carter
Matthew Jensen
Laura Pacheco-Spann
Jill E. Henry
Anna Maria Storniolo
Stacey J. Winham
Jeroen van der Laak
Mark E. Sherman
Publication date
01-12-2022
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2022
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-022-01541-z

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