Predicting mammographic density with linear ultrasound transducers.
Breast cancer risk
Percent mammographic density
Ultrasound
Journal
European journal of medical research
ISSN: 2047-783X
Titre abrégé: Eur J Med Res
Pays: England
ID NLM: 9517857
Informations de publication
Date de publication:
28 Sep 2023
28 Sep 2023
Historique:
received:
05
04
2022
accepted:
28
08
2023
medline:
27
11
2023
pubmed:
29
9
2023
entrez:
28
9
2023
Statut:
epublish
Résumé
High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).
Sections du résumé
BACKGROUND
BACKGROUND
High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments.
METHODS
METHODS
We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON
RESULTS
RESULTS
Gray level bins and PMD correlated to a certain extent. Spearman's ρ ranged from - 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R
CONCLUSIONS
CONCLUSIONS
In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).
Identifiants
pubmed: 37770952
doi: 10.1186/s40001-023-01327-9
pii: 10.1186/s40001-023-01327-9
pmc: PMC10537934
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
384Informations de copyright
© 2023. The Author(s).
Références
Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356(3):227–36.
doi: 10.1056/NEJMoa062790
pubmed: 17229950
Boyd NF, Byng JW, Jong RA, Fishell EK, Little LE, Miller AB, et al. Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study. NCI J Natl Cancer Inst. 1995;87(9):670–5.
McCormack VA, dos Santos SI. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev. 2006;15(6):1159–69.
Heusinger K, Loehberg CR, Haeberle L, Jud SM, Klingsiek P, Hein A, et al. Mammographic density as a risk factor for breast cancer in a German case-control study. Eur J Cancer Prev. 2011;20(1):1–8.
pubmed: 21102341
Vachon CM, van Gils CH, Sellers TA, Ghosh K, Pruthi S, Brandt KR, et al. Mammographic density, breast cancer risk and risk prediction. Breast Cancer Res. 2007;9(6):217.
pubmed: 18190724
pmcid: 2246184
Jud SM, Haberle L, Fasching PA, Heusinger K, Hack C, Faschingbauer F, et al. Correlates of mammographic density in B-mode ultrasound and real time elastography. Eur J Cancer Prev. 2012;21(4):343–9.
pubmed: 22123663
Rauh C, Hack CC, Haberle L, Hein A, Engel A, Schrauder MG, et al. Percent mammographic density and dense area as risk factors for breast cancer. Geburtshilfe Frauenheilkd. 2012;72(8):727–33.
pubmed: 25258465
pmcid: 4168400
Huober J, Schneeweiss A, Hartkopf AD, Muller V, Lux MP, Janni W, et al. Update breast cancer 2020 part 3-early breast cancer. Geburtshilfe Frauenheilkd. 2020;80(11):1105–14.
pubmed: 33173238
pmcid: 7647721
Lokate M, Peeters PH, Peelen LM, Haars G, Veldhuis WB, van Gils CH. Mammographic density and breast cancer risk: the role of the fat surrounding the fibroglandular tissue. Breast Cancer Res. 2011;13(5):R103.
pubmed: 22030015
pmcid: 3262216
Haberle L, Fasching PA, Brehm B, Heusinger K, Jud SM, Loehberg CR, et al. Mammographic density is the main correlate of tumors detected on ultrasound but not on mammography. Int J Cancer. 2016;139(9):1967–74.
pubmed: 27389655
Eriksson L, Czene K, Rosenberg LU, Tornberg S, Humphreys K, Hall P. Mammographic density and survival in interval breast cancers. Breast Cancer Res. 2013;15(3):R48.
pubmed: 23786804
pmcid: 4053151
Gierach GL, Ichikawa L, Kerlikowske K, Brinton LA, Farhat GN, Vacek PM, et al. Relationship between mammographic density and breast cancer death in the Breast Cancer Surveillance Consortium. J Natl Cancer Inst. 2012;104(16):1218–27.
pubmed: 22911616
pmcid: 3611814
Heindl F, Fasching PA, Hein A, Hack CC, Heusinger K, Gass P, et al. Mammographic density and prognosis in primary breast cancer patients. Breast. 2021;59:51–7.
pubmed: 34157655
pmcid: 8237359
Burton A, Maskarinec G, Perez-Gomez B, Vachon C, Miao H, Lajous M, et al. Mammographic density and ageing: A collaborative pooled analysis of cross-sectional data from 22 countries worldwide. PLoS Med. 2017;14(6):e1002335.
pubmed: 28666001
pmcid: 5493289
Byrne C, Schairer C, Wolfe J, Parekh N, Salane M, Brinton LA, et al. Mammographic features and breast cancer risk: effects with time, age, and menopause status. JNCI J Natl Cancer Inst. 1995;87(21):1622–9.
Boyd NF, Lockwood GA, Byng JW, Little LE, Yaffe MJ, Tritchler DL. The relationship of anthropometric measures to radiological features of the breast in premenopausal women. Br J Cancer. 1998;78(9):1233–8.
pubmed: 9820186
pmcid: 2063010
Brisson J, Morrison AS, Kopans DB, Sadowsky NL, Kalisher L, Twaddle JA, et al. Height and weight, mammographic features of breast tissue, and breast cancer risk. Am J Epidemiol. 1984;119(3):371–81.
pubmed: 6702813
Gram IT, Bremnes Y, Ursin G, Maskarinec G, Bjurstam N, Lund E. Percentage density, Wolfe’s and Tabar’s mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 2005;7(5):R854–61.
pubmed: 16168132
pmcid: 1242160
Boyd NF, Martin LJ, Sun L, Guo H, Chiarelli A, Hislop G, et al. Body size, mammographic density, and breast cancer risk. Cancer Epidemiol Biomark Prev. 2006;15(11):2086–92.
Azam S, Lange T, Huynh S, Aro AR, von Euler-Chelpin M, Vejborg I, et al. Hormone replacement therapy, mammographic density, and breast cancer risk: a cohort study. Cancer Causes Control. 2018;29(6):495–505.
pubmed: 29671181
pmcid: 5938298
van Duijnhoven FJ, Peeters PH, Warren RM, Bingham SA, van Noord PA, Monninkhof EM, et al. Postmenopausal hormone therapy and changes in mammographic density. J Clin Oncol. 2007;25(11):1323–8.
pubmed: 17312333
McTiernan A, Martin CF, Peck JD, Aragaki AK, Chlebowski RT, Pisano ED, et al. Estrogen-plus-progestin use and mammographic density in postmenopausal women: Women’s Health Initiative randomized trial. J Natl Cancer Inst. 2005;97(18):1366–76.
pubmed: 16174858
Lee E, Luo J, Su YC, Lewinger JP, Schumacher FR, Van Den Berg D, et al. Hormone metabolism pathway genes and mammographic density change after quitting estrogen and progestin combined hormone therapy in the California Teachers Study. Breast Cancer Res. 2014;16(6):477.
pubmed: 25499601
pmcid: 4318222
Greendale GA, Reboussin BA, Sie A, Singh HR, Olson LK, Gatewood O, et al. Effects of estrogen and estrogen-progestin on mammographic parenchymal density. Postmenopausal Estrogen/Progestin Interventions (PEPI) Investigators. Ann Intern Med. 1999;130(4 Pt 1):262–9.
Engmann NJ, Scott CG, Jensen MR, Ma L, Brandt KR, Mahmoudzadeh AP, et al. Longitudinal changes in volumetric breast density with tamoxifen and aromatase inhibitors. Cancer Epidemiol Biomark Prev. 2017;26(6):930–7.
Cuzick J, Warwick J, Pinney E, Warren RM, Duffy SW. Tamoxifen and breast density in women at increased risk of breast cancer. J Natl Cancer Inst. 2004;96(8):621–8.
pubmed: 15100340
Gram IT, Funkhouser E, Tabar L. Reproductive and menstrual factors in relation to mammographic parenchymal patterns among perimenopausal women. Br J Cancer. 1995;71(3):647–50.
pubmed: 7880753
pmcid: 2033639
Kaufman Z, Garstin WI, Hayes R, Michell MJ, Baum M. The mammographic parenchymal patterns of nulliparous women and women with a family history of breast cancer. Clin Radiol. 1991;43(6):385–8.
pubmed: 2070577
Vachon CM, Kuni CC, Anderson K, Anderson VE, Sellers TA. Association of mammographically defined percent breast density with epidemiologic risk factors for breast cancer (United States). Cancer Causes Control. 2000;11(7):653–62.
pubmed: 10977110
Loehberg CR, Heusinger K, Jud SM, Haeberle L, Hein A, Rauh C, et al. Assessment of mammographic density before and after first full-term pregnancy. Eur J Cancer Prev. 2010;19(6):405–12.
pubmed: 20700056
Hack CC, Emons J, Jud SM, Heusinger K, Adler W, Gass P, et al. Association between mammographic density and pregnancies relative to age and BMI: a breast cancer case-only analysis. Breast Cancer Res Treat. 2017;166(3):701–8.
pubmed: 28828694
Yaghjyan L, Colditz GA, Rosner B, Bertrand KA, Tamimi RM. Reproductive factors related to childbearing and mammographic breast density. Breast Cancer Res Treat. 2016;158(2):351–9.
pubmed: 27351801
pmcid: 5527672
Anon. Collaborative Group on Hormonal Factors in Breast Cancer. Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet. 2002;360(9328):187–95.
Morris GJ. Breastfeeding, parity, and reduction of breast cancer risk. Breast J. 2009;15(5):562–3.
pubmed: 19671107
Heywang-Kobrunner SH, Hacker A, Sedlacek S. Advantages and Disadvantages of Mammography Screening. Breast Care. 2011;6(3):199–207.
pubmed: 21779225
pmcid: 3132967
Hubner J, Katalinic A, Waldmann A, Kraywinkel K. Long-term incidence and mortality trends for breast cancer in Germany. Geburtshilfe Frauenheilkd. 2020;80(6):611–8.
pubmed: 32565551
pmcid: 7299687
Miglioretti DL, Lange J, van den Broek JJ, Lee CI, van Ravesteyn NT, Ritley D, et al. Radiation-induced breast cancer incidence and mortality from digital mammography screening: a modeling study. Ann Intern Med. 2016;164(4):205–14.
pubmed: 26756460
pmcid: 4878445
Khazen M, Warren RM, Boggis CR, Bryant EC, Reed S, Warsi I, et al. A pilot study of compositional analysis of the breast and estimation of breast mammographic density using three-dimensional T1-weighted magnetic resonance imaging. Cancer Epidemiol Biomark Prev. 2008;17(9):2268–74.
Wei J, Chan HP, Helvie MA, Roubidoux MA, Sahiner B, Hadjiiski LM, et al. Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. Med Phys. 2004;31(4):933–42.
pubmed: 15125012
Tagliafico A, Bignotti B, Tagliafico G, Astengo D, Martino L, Airaldi S, et al. Breast density assessment using a 3T MRI system: comparison among different sequences. PLoS ONE. 2014;9(6):e99027.
pubmed: 24892933
pmcid: 4044003
Klifa C, Carballido-Gamio J, Wilmes L, Laprie A, Shepherd J, Gibbs J, et al. Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort. Magn Reson Imaging. 2010;28(1):8–15.
pubmed: 19631485
Nie K, Chen JH, Chan S, Chau MK, Yu HJ, Bahri S, et al. Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med Phys. 2008;35(12):5253–62.
pubmed: 19175084
pmcid: 2673600
Merkel D, Stahlheber H, Chupina V, Schneider C. Comparison of the quality of B-scan ultrasound in modern high-end devices. Zeitschrift für Gastroenterologie. 2018;56(12):1491–8.
Beckmann MW, Brucker C, Hanf V, Rauh C, Bani MR, Knob S, et al. Quality assured health care in certified breast centers and improvement of the prognosis of breast cancer patients. Oncol Res Treat. 2011;34(7):362–7.
Haberle L, Wagner F, Fasching PA, Jud SM, Heusinger K, Loehberg CR, et al. Characterizing mammographic images by using generic texture features. Breast Cancer Res. 2012;14(2):R59.
pubmed: 22490545
pmcid: 3446394
Manduca A, Carston MJ, Heine JJ, Scott CG, Pankratz VS, Brandt KR, et al. Texture features from mammographic images and risk of breast cancer. Cancer Epidemiol Biomark Prev. 2009;18(3):837–45.
Haberle L, Hack CC, Heusinger K, Wagner F, Jud SM, Uder M, et al. Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound. Eur J Med Res. 2017;22(1):30.
pubmed: 28854966
pmcid: 5577694
Heusinger K, Jud SM, Haberle L, Hack CC, Adamietz BR, Meier-Meitinger M, et al. Association of mammographic density with hormone receptors in invasive breast cancers: results from a case-only study. Int J Cancer. 2012;131(11):2643–9.
pubmed: 22392346
Heusinger K, Jud SM, Haberle L, Hack CC, Fasching PA, Meier-Meitinger M, et al. Association of mammographic density with the proliferation marker Ki-67 in a cohort of patients with invasive breast cancer. Breast Cancer Res Treat. 2012;135(3):885–92.
pubmed: 22936391
Hack CC, Haberle L, Geisler K, Schulz-Wendtland R, Hartmann A, Fasching PA, et al. Mammographic density and prediction of nodal status in breast cancer patients. Geburtshilfe Frauenheilkd. 2013;73(2):136–41.
pubmed: 24771910
pmcid: 3858989
Haberle L, Hein A, Rubner M, Schneider M, Ekici AB, Gass P, et al. Predicting triple-negative breast cancer subtype using multiple single nucleotide polymorphisms for breast cancer risk and several variable selection methods. Geburtshilfe Frauenheilkd. 2017;77(6):667–78.
pubmed: 28757654
pmcid: 5489407
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1-73.
pubmed: 25560730
Fasching PA, Ekici AB, Adamietz BR, Wachter DL, Hein A, Bayer CM, et al. Breast cancer risk-genes, environment and clinics. Geburtshilfe Frauenheilkd. 2011;71(12):1056–66.
pubmed: 25253900
pmcid: 4166916
Bayer CM, Bani MR, Schneider M, Dammer U, Raabe E, Haeberle L, et al. Assessment of breast volume changes during human pregnancy using a three-dimensional surface assessment technique in the prospective CGATE study. Eur J Cancer Prev. 2014;23(3):151–7.
pubmed: 24100511
Heijblom M, Piras D, Brinkhuis M, van Hespen JC, van den Engh FM, van der Schaaf M, et al. Photoacoustic image patterns of breast carcinoma and comparisons with Magnetic Resonance Imaging and vascular stained histopathology. Sci Rep. 2015;5:11778.
pubmed: 26159440
pmcid: 4498178
McDonald ES, Schopp JG, Peacock S, DeMartini WB, Rahbar H, Lehman CD, et al. Diffusion-weighted MRI: association between patient characteristics and apparent diffusion coefficients of normal breast fibroglandular tissue at 3 T. Am J Roentgenol. 2014;202(5):W496-502.
Glide C, Duric N, Littrup P. Novel approach to evaluating breast density utilizing ultrasound tomography. Med Phys. 2007;34(2):744–53.
pubmed: 17388192
Glide-Hurst CK, Duric N, Littrup P. Volumetric breast density evaluation from ultrasound tomography images. Med Phys. 2008;35(9):3988–97.
pubmed: 18841850