Skip to main content

Advertisement

SpringerLink
Log in

Mammographic density to predict response to neoadjuvant systemic breast cancer therapy

  • Original Article – Cancer Research
  • Published:
Journal of Cancer Research and Clinical Oncology Aims and scope Submit manuscript

Abstract

Background

Mammographic density (MD) is a risk factor for breast cancer (BC) development, and recurrence. However, its predictive value has been less studied. Herein, we challenged MD as a biomarker associated with response in patients treated with neoadjuvant therapy (NAT).

Methods

Data on all NAT treated BC patients prospectively collected in the registry of Fondazione IRCCS Istituto Nazionale dei Tumori, Milano, Italy (2009–2019) were identified. Diagnostic mammograms were used to evaluate and score MD as categorized by the Breast Imaging-Reporting and Data System (BI-RADS), which identifies 4 levels of MD in keeping with relative increase of fibro-glandular over fat tissue. Each case was classified according to the following categories a (MD < 25%), b (26–50%), c (51–75%), and d (> 75%). The association between MD and pathological complete response (pCR), i.e., absence of BC cells in surgical specimens, was analyzed in multivariable setting used logistic regression models with adjustment for clinical and pathological variables.

Results

A total of 442 patients were analyzed, 120 of which (27.1%) attained a pCR. BI-RADS categories a, b, c, and d accounted for 10.0%, 37.8%, 37.1% and 15.2% of cases. Corresponding pCR were 20.5%, 26.9%, 30.5%, 23.9%, respectively. At multivariable analysis, when compared to cases classified as BI-RADS a, those with denser breast showed an increased likelihood of pCR with odds ratio (OR) of 1.70, 2.79, and 1.47 for b, c and d categories, respectively (p = 0.0996), independently of age, BMI [OR underweight versus (vs) normal = 3.76], clinical nodal and tumor status (OR T1/Tx vs T4 = 3.87), molecular subtype (HER2-positive vs luminal = 10.74; triple-negative vs luminal = 8.19). In subgroup analyses, the association of MD with pCR was remarkable in triple-negative (ORs of b, c and d versus a: 1.85, 2.49 and 1.55, respectively) and HER2-positive BC cases (ORs 2.70, 3.23, and 1.16).

Conclusion

Patients with dense breast are more likely to attain a pCR at net of other predictive factors. The potential of MD to assist decisions on BC management and as a stratification factor in neoadjuvant clinical trials should be considered.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Baili P, Torresani M, Agresti R, Rosito G, Daidone MG, Veneroni S, Cavallo I, Funaro F, Giunco M, Turco A, Amash H, Scavo A, Minicozzi P, Bella F, Meneghini E, Sant M. A breast cancer clinical registry in an Italian comprehensive cancer center: an instrument for descriptive, clinical, and experimental research. Tumori. 2015 Jul-Aug;101(4):440–6. doi: https://doi.org/10.5301/tj.5000341.

  • Cain H, Macpherson IR, Beresford M, Pinder SE, Pong J, Dixon JM (2017) Neoadjuvant therapy in early breast cancer: treatment considerations and common debates in practice. Clin Oncol (r Coll Radiol) 29(10):642–652

    Article  CAS  Google Scholar 

  • Castaneda CA, Flores R, Rojas K, Flores C, Castillo M, Milla E (2014) Association between mammographic features and response to neoadjuvant chemotherapy in locally advanced breast carcinoma. Hematol Oncol Stem Cell Ther 7(4):149–156

    Article  Google Scholar 

  • Cortazar P, Zhang L, Untch M, Mehta K, Costantino JP, Wolmark N, Bonnefoi H, Cameron D, Gianni L, Valagussa P (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164–172

    Article  Google Scholar 

  • Curigliano G, Burstein HJ, Winer EP, Gnant M, Dubsky P, Loibl S, et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017 . Ann Oncol. 2017 Aug 1;28(8):1700–1712. doi: https://doi.org/10.1093/annonc/mdx308.

  • Elfgen C, Varga Z, Reeve K, Moskovszky L, Bjelic-Radisic V, Tausch C, Güth U (2019) The impact of distinct triple-negative breast cancer subtypes on misdiagnosis and diagnostic delay. Breast Cancer Res Treat 177(1):67–75

    Article  CAS  Google Scholar 

  • Elsamany S, Alzahrani A, Abozeed WN, Rasmy A, Farooq MU, Elbiomy MA, Rawah E, Alsaleh K, Abdel-Aziz NM (2015) Mammographic breast density: predictive value for pathological response to neoadjuvant chemotherapy in breast cancer patients. Breast 24(5):576–581

    Article  CAS  Google Scholar 

  • Freeman GH, Halton JH (1951) Note on exact treatment of contingency, goodness of fit and other problems of significance. Biometrika 38:141–149

    Article  CAS  Google Scholar 

  • Hammond ME, Hayes DF, Dowsett M, Allred DC, Hagerty KL, Badve S et al (2010) American Society of Clinical Oncology/College of American pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer. J Clin Oncol 28:2784–2795

    Article  Google Scholar 

  • Huo CW, Chew G, Hill P, Huang D, Ingman W, Hodson L et al (2015) High mammographic density is associated with an increase in stromal collagen and immune cells within the mammary epithelium. Breast Cancer Res 17(1):79

    Article  Google Scholar 

  • Loibl S, Denkert C, von Minckwitz G (2015) Neoadjuvant treatment of breast cancer–Clinical and research perspective. Breast 24(Suppl 2):S73–S77

    Article  Google Scholar 

  • Maisonneuve P, Disalvatore D, Rotmensz N, Curigliano G, Colleoni M, Dellapasqua S et al (2014) Proposed new clinicopathological surrogate definitions of luminal A and luminal B (HER2-negative) intrinsic breast cancer subtypes. Breast Cancer Res 16(3):R65

    Article  Google Scholar 

  • Martin LJ, Boyd NF (2008) Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence. Breast Cance Res 10(1):201

    Article  Google Scholar 

  • Maskarinec G, Takata Y, Chen Z, Gram IT, Nagata C, Pagano I, Hayashi K, Arendell L, Skeie G, Rinaldi S, Kaaks R (2007) IGF-I and mammographic density in four geographic locations: a pooled analysis. Int J Cancer 121(8):1786–1792

    Article  CAS  Google Scholar 

  • Maskarinec G, Ju D, Fong J, Horio D, Chan O, Loo LWM, Hernandez BY (2018) Mammographic density and breast tissue expression of inflammatory markers, growth factors, and vimentin. BMC Cancer 18:1191

    Article  CAS  Google Scholar 

  • McCormack VA, dos Santos SI (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev 15(6):1159–1169

    Article  Google Scholar 

  • Miglietta F, Dieci MV, Griguolo G, Guarneri V. Neoadjuvant approach as a platform for treatment personalization: focus on HER2-positive and triple-negative breast cancer. Cancer Treat Rev. 2021 Jul;98:102222.

  • Pike MC, Krailo MD, Henderson BE, Casagrande JT, Hoel DG (1983) Hormonal risk factors, breast tissue age and the age-incidence of breast cancer. Nature 303:767–770

    Article  CAS  Google Scholar 

  • Pinsky RW, Helvie MA (2010) Mammographic breast density: effect on imaging and breast cancer risk. J Natl Compr Canc Netw 8(10):1157–1164

    Article  Google Scholar 

  • Shawky MS, Huo CW, Henderson MA, Redfern A, Britt K, Thompson EW (2019) A review of the influence of mammographic density on breast cancer clinical and pathological phenotype. Breast Cancer Res Treat 177(2):251–276

    Article  Google Scholar 

  • Skarping I, Fornvik D, Sartor H, Heide-Jorgensen U, Zackrisson S, Borgquist S (2019) Mammographic density is a potential predictive marker of pathological response after neoadjuvant chemotherapy in breast cancer. BMC Canc 19(1):1272

    Article  Google Scholar 

  • Skarping I, Förnvik D, Heide-Jørgensen U, Sartor H, Hall P, Zackrisson S, Borgquist S (2021) Mammographic density as an image-based biomarker of therapy response in neoadjuvant-treated breast cancer patients. Cancer Causes Control 32(3):251–260

    Article  Google Scholar 

  • Sobin LH, Gospodarowicz MK, Wittekind CH (2010) TNM classification of malignant tumours, 7th edn. Wiley, Chichester

    Google Scholar 

  • von Minckwitz G., Untch M., Ju Blohmer, Costa S.D., Eidtmann H., Fasching P.A., Gerber B., Eiermann W., Hilfrich J., Huober J. Definition and impact of pathologic complete response on prognosis after neoadjuvant chemotherapy in various intrinsic breast cancer subtypes. J Clin Oncol. 2012;30(15):1796–1804.

  • World Health Organization—Nutrition—Body mass index classification. 2020. Available from: https://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi.

Download references

Author information

Author notes

  1. S. Di Cosimo and C. Depretto have equal contribution SDC and CD are co-first authors.

Authors and Affiliations

  1. Biomarkers Unit, Department of Applied Research and Technological Development, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy

    S. Di Cosimo & V. Cappelletti

  2. Radiology Unit, Fondazione IRCCS, Istituto Nazionale Tumori, Milano, Italy

    C. Depretto, A. Marchianò & G. P. Scaperrotta

  3. Department of Clinical Epidemiology and Trial Organisation, Fondazione IRCCS Istituto Nazionale Tumori, Milano, Italy

    R. Miceli & S. Ljevar

  4. Analytical Epidemiology and Health Impact Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy

    P. Baili & M. Sant

  5. Breast Surgery Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy

    S. Folli & M. Gennaro

  6. Division of Medical Oncology 1, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy

    F. G. De Braud & G. Bianchi

  7. Department of Pathology, Fondazione IRCCS Istituto Nazionale Dei Tumori, Milano, Italy

    A. Vingiani & G. Pruneri

  8. Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale Dei Tumori, Via Venezian 1, 20100, Milano, Italy

    E. La Rocca & M. C. De Santis

Authors
  1. S. Di Cosimo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. Depretto
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. R. Miceli
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Baili
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. S. Ljevar
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. Sant
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. V. Cappelletti
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. S. Folli
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. M. Gennaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. F. G. De Braud
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. G. Bianchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. A. Vingiani
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. G. Pruneri
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. A. Marchianò
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. E. La Rocca
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. M. C. De Santis
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. G. P. Scaperrotta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. C. De Santis.

Ethics declarations

Conflict of interest

We declare that we do not have any conflict of interest.

Ethical approval

The present study has been reviewed and approved.

Research involving in human and animal rights

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Di Cosimo, S., Depretto, C., Miceli, R. et al. Mammographic density to predict response to neoadjuvant systemic breast cancer therapy. J Cancer Res Clin Oncol 148, 775–781 (2022). https://doi.org/10.1007/s00432-021-03881-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00432-021-03881-3

Keywords

Search

Navigation