Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (674 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
In humans, one of the paired regions in the anterior portion of the thorax. The breasts consist of the mammary glands, the SKIN, the muscles, the adipose tissue, and the connective tissues.
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Breasts.
Where it fits in the MeSH classification
- Broader topics
- Body Regions
- Narrower topics
- Mammary Glands, Human; Nipples
- See also
- Lactation; Mammary Glands, Animal; Mammography; Mastectomy; Mammaplasty
Related concepts
- Thorax
- Mammary Glands
- Muscles
- Adipose Tissue
- Connective Tissue
Research landscape
PubMed indexes 57,363 records tagged with the MeSH heading “Breast” (counts retrieved 2026-10-31). 10,920 of them (19.0%) were published from 2020 onward, and the busiest period so far is through 1999 with 21,453 records.
Reviews account for 5,605 records (9.8%) and meta-analyses for 373. Randomized controlled trials number 879 (1.5%) as a share of all records on the topic. Randomized trials make up only a small share of this literature, so much of what is known comes from laboratory, observational or descriptive work.
Show the data
| Period | Records |
|---|---|
| through 1999 | 21,453 |
| 2000-2009 | 9,779 |
| 2010-2019 | 15,211 |
| 2020 onward | 10,920 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 5,605 | 9.8% |
| Meta-analyses | 373 | 0.7% |
| Randomized controlled trials | 879 | 1.5% |
Method: PubMed query "Breast"[MeSH Terms] with publication-type and date filters, retrieved 2026-10-31.
Reading the evidence in anatomy and physiology
Anatomy describes how body structures are built and physiology describes how they normally work, so most findings here are descriptive or mechanistic rather than evidence about treatment. Much of what is known comes from dissection, imaging, tissue studies and experiments in animals, and measurements in healthy volunteers can differ from those in people with disease or at different ages. Normal ranges vary between individuals and populations. Check whether a result was measured in people or in another species, the age and health of the participants, and the method used.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| A Review of Artificial Intelligence in Breast Imaging | 2024 | Review article Design not recorded in structured metadata | 22 | cc by PMID 38787015 · DOI · PMC11125819 |
| Volumetric printed biomimetic scaffolds support in vitro lactation of human milk-derived mammary epithelial cells | 2025 | Research article Design not recorded in structured metadata | 13 | cc by PMID 40465736 · DOI · PMC12136022 |
| Maternal Breast Growth and Body Mass Index Are Associated with Low Milk Production in Women | 2024 | Research article Design not recorded in structured metadata | 12 | cc by PMID 39275171 · DOI · PMC11397153 |
| Single-cell spatial atlas of the aging human breast | 2026 | Research article Design not recorded in structured metadata | 4 | cc by PMID 41917190 · DOI · PMC13099655 |
A Review of Artificial Intelligence in Breast Imaging [1]
Abstract reading summary — source excerpts
- Opening excerpt
- With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis.
- Closing excerpt
- Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (146 words)
With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women's physical and mental health. Early breast cancer screening-through mammography, ultrasound, or magnetic resonance imaging (MRI)-can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 38787015 · DOI · PMC11125819
Volumetric printed biomimetic scaffolds support in vitro lactation of human milk-derived mammary epithelial cells [2]
Abstract reading summary — source excerpts
- Opening excerpt
- The human breast is remarkably plastic and remodels with each birth to produce milk optimally suited for the changing demands of the newborn.
- Closing excerpt
- This model offers exciting avenues to explore hormonal, nutritional, and mechanobiological factors involved in lactation, thereby improving understanding of lactation for the benefit of infants and their mothers.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (159 words)
The human breast is remarkably plastic and remodels with each birth to produce milk optimally suited for the changing demands of the newborn. This dynamic nature of lactation makes it challenging to study under controlled conditions. Given the health benefits of human milk, models of secretory mammary tissue would offer opportunities to study factors that influence this important food source. First, 3D models of the mammary duct/alveoli (D/A) were designed inspired by shapes found in vivo. Photoresins based on mammary decellularized extracellular matrix (dECM) were optimized to match the mechanical properties of native breast tissue. Next, these D/A models were printed with a volumetric printer and seeded with human milk-derived mammary epithelial cells (MECs). MECs formed stable epithelial layers on the printed surfaces and secreted the β-casein and milk fat globules. This model offers exciting avenues to explore hormonal, nutritional, and mechanobiological factors involved in lactation, thereby improving understanding of lactation for the benefit of infants and their mothers.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 40465736 · DOI · PMC12136022
Maternal Breast Growth and Body Mass Index Are Associated with Low Milk Production in Women [3]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundMaternal breast volume is determined by the quantity of glandular and adipose tissue, and it undergoes significant changes during pregnancy.
- Closing excerpt
- ConclusionsWomen who are older, have an obese BMI and who have minimal pre-pregnancy breast volume and breast growth should be provided with antenatal screening and breastfeeding support as they are more likely to experience low milk production.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (219 words)
BackgroundMaternal breast volume is determined by the quantity of glandular and adipose tissue, and it undergoes significant changes during pregnancy. These changes are intricately linked to the development of glandular tissue, which most likely reflects lactation capacity. Evidence indicates that women with overweight or obesity exhibit larger breast volume compared to those with a normal body mass index (BMI), emphasizing the close relationship between breast volume and maternal adiposity. Hence, we aim to investigate breast volume growth and maternal BMI as potential risk factors for low milk production.MethodsLactating women (n = 609) from the Perth metropolitan area in Western Australia between 2011 and 2023 were included in the analysis. Twenty-four-hour milk production measurements were conducted using the test weighing method, and milk removal frequencies were recorded. Mothers completed questionnaires regarding demographic, obstetric and infant details. Linear and logistic regression models were used to determine maternal and infant factors associated with milk production.ResultsHere we show that increasing maternal age and BMI are associated with low milk production. Moreover, larger pre-pregnancy breast volume and breast growth are associated with both higher BMI and milk production.ConclusionsWomen who are older, have an obese BMI and who have minimal pre-pregnancy breast volume and breast growth should be provided with antenatal screening and breastfeeding support as they are more likely to experience low milk production.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39275171 · DOI · PMC11397153
Single-cell spatial atlas of the aging human breast [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Breast cancer can develop over a wide age range and tumors in younger women differ from those in older women.
- Closing excerpt
- Our multiscale atlas extensively details an unexpected general decline of breast tissue with age and reveals its changing spatial context.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (150 words)
Breast cancer can develop over a wide age range and tumors in younger women differ from those in older women. Aging alters the spatial context of early tumors and may explain these differences, but breast tissue aging remains poorly characterized. Here, using imaging mass cytometry to profile the spatial expression of 40 proteins, we explore age-related remodeling of normal breast tissues in over 3 million cells from 527 reduction mammoplasties. Aged breast tissue was less cellular and less proliferative for all cell types (epithelial, stromal and immune). Tissue architecture was restructured with fewer heterotypic epithelial cell-cell interactions, far fewer lobules and increased fat. Older tissues had a more inflammatory microenvironment with increased M2 macrophages and granzyme B+ T cells, contrasted by younger tissues in which B cells were most enriched. Our multiscale atlas extensively details an unexpected general decline of breast tissue with age and reveals its changing spatial context.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 41917190 · DOI · PMC13099655
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Worldwide Secular Trends in Age at Pubertal Onset Assessed by Breast Development Among Girls: A Systematic Review and Meta-analysis [5]
- Research article Mammographic density phenotypes and risk of breast cancer: a meta-analysis [6]
- Research article Magnetic resonance imaging screening of the contralateral breast in women with newly diagnosed breast cancer: systematic review and meta-analysis of incremental cancer detection and impact on surgical management [7]
- Research article Meta-analysis of non-sentinel node metastases associated with micrometastatic sentinel nodes in breast cancer [8]
- Research article Magnetic resonance imaging in the preoperative assessment of patients with primary breast cancer: systematic review of diagnostic accuracy and meta-analysis [9]
- Research article The Importance of Breast Adipose Tissue in Breast Cancer [10]
References
- Al-Karawi D, Al-Zaidi S, Helael KA, Obeidat N, Mouhsen AM, Ajam T, et al.. A Review of Artificial Intelligence in Breast Imaging. . 2024. PMID 38787015 · DOI · PMC11125819
- Hasenauer A, Bevc K, McCabe MC, Chansoria P, Saviola AJ, Hansen KC, et al.. Volumetric printed biomimetic scaffolds support in vitro lactation of human milk-derived mammary epithelial cells. . 2025. PMID 40465736 · DOI · PMC12136022
- Jin X, Lai CT, Perrella SL, McEachran JL, Gridneva Z, Geddes DT. Maternal Breast Growth and Body Mass Index Are Associated with Low Milk Production in Women. . 2024. PMID 39275171 · DOI · PMC11397153
- Gupta P, Lee E, Masqué Soler N, Schrader E, Wang XQ, Mayer S, et al.. Single-cell spatial atlas of the aging human breast. . 2026. PMID 41917190 · DOI · PMC13099655
- Eckert-Lind C, Busch AS, Petersen JH, Biro FM, Butler G, Bräuner EV, et al.. Worldwide Secular Trends in Age at Pubertal Onset Assessed by Breast Development Among Girls: A Systematic Review and Meta-analysis. JAMA Pediatr. 2020. PMID 32040143 · DOI · PMC7042934
- Pettersson A, Graff RE, Ursin G, Santos Silva ID, McCormack V, Baglietto L, et al.. Mammographic density phenotypes and risk of breast cancer: a meta-analysis. J Natl Cancer Inst. 2014. PMID 24816206 · DOI · PMC4568991
- Brennan ME, Houssami N, Lord S, Macaskill P, Irwig L, Dixon JM, et al.. Magnetic resonance imaging screening of the contralateral breast in women with newly diagnosed breast cancer: systematic review and meta-analysis of incremental cancer detection and impact on surgical management. J Clin Oncol. 2009. PMID 19805685 · DOI
- Cserni G, Gregori D, Merletti F, Sapino A, Mano MP, Ponti A, et al.. Meta-analysis of non-sentinel node metastases associated with micrometastatic sentinel nodes in breast cancer. Br J Surg. 2004. PMID 15376203 · DOI
- Plana MN, Carreira C, Muriel A, Chiva M, Abraira V, Emparanza JI, et al.. Magnetic resonance imaging in the preoperative assessment of patients with primary breast cancer: systematic review of diagnostic accuracy and meta-analysis. Eur Radiol. 2012. PMID 21847541 · DOI
- Kothari C, Diorio C, Durocher F. The Importance of Breast Adipose Tissue in Breast Cancer. Int J Mol Sci. 2020. PMID 32796696 · DOI · PMC7460846
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-10-31; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-10-31.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.