Skip to main content
Top
Published in: Breast Cancer Research 1/2017

Open Access 01-12-2017 | Research article

Non-invasive optical spectroscopic monitoring of breast development during puberty

Authors: Lothar Lilge, Mary Beth Terry, Jane Walter, Dushanthi Pinnaduwage, Gord Glendon, Danielle Hanna, Mai-Liis Tammemagi, Angela Bradbury, Saundra Buys, Mary Daly, Esther M. John, Julia A. Knight, Irene L. Andrulis

Published in: Breast Cancer Research | Issue 1/2017

Login to get access

Abstract

Background

Tanner staging (TS), a five-stage classification indicating no breast tissue (TS1) to full breast development (TS5), is used both in health research and clinical care to assess the onset of breast development (TS2) and duration in each stage. Currently, TS is measured both visually and through palpation but non-invasive methods will improve comparisons across settings.

Methods

We used optical spectroscopy (OS) measures from 102 girls at the Ontario site of the LEGACY girls study (average age 12 years, range 10.0–15.4 years) to determine whether breast tissue optical properties map to each TS. We further examined whether these properties differed by age, body mass index (BMI), and breast cancer risk score (BCRS) by examining the major principal components (PC).

Results

Age and BMI increased linearly with increasing TS. Eight PCs explained 99.9% of the variation in OS data. Unlike the linear increase with age and BMI, OS components had distinct patterns by TS: the onset of breast development (TS1 to TS2) was marked by elevation of PC3 scores indicating an increase in adipose tissue and decrease in signal from the pectoral muscle; transition to TS3 was marked by elevation of PC6 and PC7 and decline of PC2 scores indicating an increase in glandular or dense tissue; and transition to TS4+ by decline of PC2 scores representing a further increase in glandular tissue relative to adipose tissue. Of the eight PCs, three component scores (PC4, PC5, and PC8) remained in the best-fitting model of BCRS, suggesting different levels of collagen in the breast tissue by BCRS.

Conclusions

Our results suggest that serial measures of OS, a non-invasive assessment of breast tissue characteristics, can be used as an objective outcome that does not rely on visual inspection or palpation, for studying drivers of breast development.
Appendix
Available only for authorised users
Literature
1.
go back to reference Johnson RH, Chien FL, Bleyer A. Incidence of breast cancer with distant involvement among women in the United States, 1976 to 2009. JAMA. 2013;309:800–5.CrossRefPubMed Johnson RH, Chien FL, Bleyer A. Incidence of breast cancer with distant involvement among women in the United States, 1976 to 2009. JAMA. 2013;309:800–5.CrossRefPubMed
2.
go back to reference Torre LA, et al. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomarkers Prev. 2016;25(1):16–27.CrossRefPubMed Torre LA, et al. Global cancer incidence and mortality rates and trends–an update. Cancer Epidemiol Biomarkers Prev. 2016;25(1):16–27.CrossRefPubMed
3.
go back to reference Colditz GA, Rosner BA, Speizer FE. Risk factors for breast cancer according to family history of breast cancer. For the Nurses’ Health Study Research Group. J Natl Cancer Inst. 1996;88(6):365–71.CrossRefPubMed Colditz GA, Rosner BA, Speizer FE. Risk factors for breast cancer according to family history of breast cancer. For the Nurses’ Health Study Research Group. J Natl Cancer Inst. 1996;88(6):365–71.CrossRefPubMed
5.
go back to reference Euling SY, et al. Examination of US puberty-timing data from 1940 to 1994 for secular trends: panel finding. Pediatrics. 2008;121 Suppl 3:S172–91.CrossRefPubMed Euling SY, et al. Examination of US puberty-timing data from 1940 to 1994 for secular trends: panel finding. Pediatrics. 2008;121 Suppl 3:S172–91.CrossRefPubMed
6.
go back to reference de Muinich Keizer SM, Mul D. Trends in pubertal development in Europe. Hum Reprod Update. 2001;7(3):287–91.CrossRefPubMed de Muinich Keizer SM, Mul D. Trends in pubertal development in Europe. Hum Reprod Update. 2001;7(3):287–91.CrossRefPubMed
8.
go back to reference Morris NM, Udry JR. Validation of a self-administered instrument to assess stage of adolescent development. J Youth Adolesc. 1980;9(3):271–80.CrossRefPubMed Morris NM, Udry JR. Validation of a self-administered instrument to assess stage of adolescent development. J Youth Adolesc. 1980;9(3):271–80.CrossRefPubMed
9.
go back to reference Terry MB, et al. Comparison of clinical, maternal, and self pubertal assessments: implications for health studies. Pediatrics. 2016;138(1):e20154571. doi:10.1542/peds.2015-4571. Terry MB, et al. Comparison of clinical, maternal, and self pubertal assessments: implications for health studies. Pediatrics. 2016;138(1):e20154571. doi:10.​1542/​peds.​2015-4571.
10.
go back to reference Boyd NF, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–36.CrossRefPubMed Boyd NF, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–36.CrossRefPubMed
11.
go back to reference Byrne C, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87(21):1622–9.CrossRefPubMed Byrne C, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status. J Natl Cancer Inst. 1995;87(21):1622–9.CrossRefPubMed
12.
go back to reference Nelson HD, et al. Risk factors for breast cancer for women aged 40 to 49 years: a systematic review and meta-analysis. Ann Intern Med. 2012;156(9):635–48.CrossRefPubMedPubMedCentral Nelson HD, et al. Risk factors for breast cancer for women aged 40 to 49 years: a systematic review and meta-analysis. Ann Intern Med. 2012;156(9):635–48.CrossRefPubMedPubMedCentral
13.
go back to reference Blackmore KM, Knight JA, Lilge L. Association between transillumination breast spectroscopy and quantitative mammographic features of the breast. Cancer Epidemiol Biomarkers Prev. 2008;17(5):1043–50.CrossRefPubMed Blackmore KM, Knight JA, Lilge L. Association between transillumination breast spectroscopy and quantitative mammographic features of the breast. Cancer Epidemiol Biomarkers Prev. 2008;17(5):1043–50.CrossRefPubMed
14.
go back to reference Blyschak KSM, Jong R, Lilge L. Classification of breast tissue density by optical transillumination spectroscopy: optical and physiological effects governing predictive value. Med Phys. 2004;31(6):1398–414.CrossRefPubMed Blyschak KSM, Jong R, Lilge L. Classification of breast tissue density by optical transillumination spectroscopy: optical and physiological effects governing predictive value. Med Phys. 2004;31(6):1398–414.CrossRefPubMed
15.
go back to reference Blackmore KM, Knight JA, Walter J, Lilge L. The association between breast tissue optical content and mammographic density in pre- and post-menopausal women. PLoS One. 2015;10(1):e0115851.CrossRefPubMedPubMedCentral Blackmore KM, Knight JA, Walter J, Lilge L. The association between breast tissue optical content and mammographic density in pre- and post-menopausal women. PLoS One. 2015;10(1):e0115851.CrossRefPubMedPubMedCentral
16.
go back to reference Knight JA, et al. Optical spectroscopy of the breast in premenopausal women reveals tissue variation with changes in age and parity. Med Phys. 2010;37(2):419–26.CrossRefPubMed Knight JA, et al. Optical spectroscopy of the breast in premenopausal women reveals tissue variation with changes in age and parity. Med Phys. 2010;37(2):419–26.CrossRefPubMed
17.
go back to reference John EM, et al. The LEGACY girls study: growth and development in the context of breast cancer family history. Epidemiology. 2016;27(3):438–48.CrossRefPubMed John EM, et al. The LEGACY girls study: growth and development in the context of breast cancer family history. Epidemiology. 2016;27(3):438–48.CrossRefPubMed
18.
go back to reference Cerussi A, et al. In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. J Biomed Opt. 2006;11(4):044005.CrossRefPubMed Cerussi A, et al. In vivo absorption, scattering, and physiologic properties of 58 malignant breast tumors determined by broadband diffuse optical spectroscopy. J Biomed Opt. 2006;11(4):044005.CrossRefPubMed
20.
go back to reference Dick SL, Lilge L. Optical reflectance spectroscopy for prospective studies on breast cancer risk in adolescent girls. Am J Epidemiol. 2006;163(11):S97–7. Dick SL, Lilge L. Optical reflectance spectroscopy for prospective studies on breast cancer risk in adolescent girls. Am J Epidemiol. 2006;163(11):S97–7.
21.
go back to reference Simick MK, et al. Non-ionizing near-infrared radiation transillumination spectroscopy for breast tissue density and assessment of breast cancer risk. J Biomed Opt. 2004;9(4):794–803.CrossRefPubMed Simick MK, et al. Non-ionizing near-infrared radiation transillumination spectroscopy for breast tissue density and assessment of breast cancer risk. J Biomed Opt. 2004;9(4):794–803.CrossRefPubMed
22.
go back to reference Antoniou AC, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91(8):1580–90.PubMedPubMedCentral Antoniou AC, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancer. Br J Cancer. 2004;91(8):1580–90.PubMedPubMedCentral
23.
go back to reference Antoniou AC, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer. 2008;98(8):1457–66.CrossRefPubMedPubMedCentral Antoniou AC, et al. The BOADICEA model of genetic susceptibility to breast and ovarian cancers: updates and extensions. Br J Cancer. 2008;98(8):1457–66.CrossRefPubMedPubMedCentral
24.
go back to reference Lee AJ, et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer. 2014;110(2):535–45.CrossRefPubMed Lee AJ, et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br J Cancer. 2014;110(2):535–45.CrossRefPubMed
25.
go back to reference Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;AC-19(6):716–23.CrossRef Akaike H. A new look at the statistical model identification. IEEE Trans Autom Control. 1974;AC-19(6):716–23.CrossRef
26.
go back to reference Taroni P, et al. Seven-wavelength time-resolved optical mammography extending beyond 1000 nm for breast collagen quantification. Opt Express. 2009;17(18):15932–46.CrossRefPubMed Taroni P, et al. Seven-wavelength time-resolved optical mammography extending beyond 1000 nm for breast collagen quantification. Opt Express. 2009;17(18):15932–46.CrossRefPubMed
Metadata
Title
Non-invasive optical spectroscopic monitoring of breast development during puberty
Authors
Lothar Lilge
Mary Beth Terry
Jane Walter
Dushanthi Pinnaduwage
Gord Glendon
Danielle Hanna
Mai-Liis Tammemagi
Angela Bradbury
Saundra Buys
Mary Daly
Esther M. John
Julia A. Knight
Irene L. Andrulis
Publication date
01-12-2017
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2017
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-017-0805-x

Other articles of this Issue 1/2017

Breast Cancer Research 1/2017 Go to the issue
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine