Skip to main content

Advertisement

Log in

Prediction of glandularity and breast radiation dose from mammography results in Japanese women

  • Original article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

Glandularity has a marked impact on the incidence of breast cancer and the missed lesion rate of mammography. The aim of this study was to develop a novel model for predicting glandularity and patient radiation dose using physical factors that are easily determined prior to mammography. Data regarding glandularity and mean glandular dose were obtained from 331 mammograms. A stepwise multiple regression analysis model was developed to predict glandularity using age, compressed breast thickness and body mass index (BMI), while a model to predict mean glandular dose was created using quantified glandularity, age, compressed breast thickness, height and body weight. The most significant factor for predicting glandularity was age, the influence of which was 1.8 times that of BMI. The most significant factor for predicting mean glandular dose was compressed breast thickness, the influence of which was 1.4 times that of glandularity, 3.5 times that of age and 6.1 times that of height. Both models were statistically significant (both p < 0.0001). Easily determined physical factors were able to explain 42.8% of the total variance in glandularity and 62.4% of the variance in mean glandular dose.

Validation results of the above prediction model made using physical factors in Japanese women. The plotted points of actual vs. prediction glandularity shown in a are distributed in the vicinity of the diagonal line, and the residual plot for predicted glandularity shows an almost random distribution as shown in b. These distributions indicate the appropriateness of the prediction model.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Braithwaite D, Demb J, Henderson LM (2016) Optimal breast cancer screening strategies for older women: current perspectives. Clin Interv Aging 11:111–125

    Article  Google Scholar 

  2. Paul S, Solanki PP, Shahi UP, Srikrishna S (2015) Epidemiological study on breast cancer associated risk factors and screening practices among women in the Holy City of Varanasi, Uttar Pradesh, India. Asian Pac J Cancer Prev 16:8163–8171

    Article  Google Scholar 

  3. Haddad FG, Kourie HR, Adib SM (2015) Trends in mammography utilization for breast cancer screening in a Middle-Eastern country: Lebanon 2005-2013. Cancer Epidemiol 39:819–824

    Article  Google Scholar 

  4. Jayadevan R, Armada MJ, Shaheen R, Mulcahy C, Slanetz PJ (2015) Optimizing digital mammographic image quality for full-field digital detectors: artifacts encountered during the QC process. Radiographics 35:2080–2089

    Article  Google Scholar 

  5. Holland K, van Gils CH, Mann RM, Karssemeijer N (2017) Quantification of masking risk in screening mammography with volumetric breast density maps. Breast Cancer Res Treat 162:541–548

    Article  Google Scholar 

  6. Kamal RM, Abdel Razek NM, Hassan MA, Shaalan MA (2007) Missed breast carcinoma; why and how to avoid? J Egypt Natl Canc Inst 19:178–194

    PubMed  Google Scholar 

  7. McCarthy AM, Keller BM, Pantalone LM, Hsieh MK, Synnestvedt M, Conant EF, Armstrong K, Kontos D (2016) Racial differences in quantitative measures of area and volumetric breast density. J Natl Cancer Inst 108:djw104. https://doi.org/10.1093/jnci/djw104

    Article  PubMed Central  Google Scholar 

  8. Sawada T, Akashi S, Nakamura S, Kuwayama T, Enokido K, Yoshida M, Hashimoto R, Ide T, Masuda H, Taruno K, Oyama H, Takamaru T, Kanada Y, Ikeda M, Kosugi N, Sato H, Nakayama S, Ata A, Tonouchi Y, Sakai H, Matsunaga Y, Matsutani A (2017) Digital volumetric measurement of mammographic density and the risk overlooking cancer in Japanese women. Breast Cancer 24:708–713. https://doi.org/10.1007/s12282-017-0763-2

    Article  PubMed  Google Scholar 

  9. Babić I, Trstenjak VH (2014) Mammography screening—how persistent should we be in the recommendations? Coll Antropol 38:207–209

    PubMed  Google Scholar 

  10. Depypere H, Desreux J, Pérez-López FR, Ceausu I, Erel CT, Lambrinoudaki I, Schenck-Gustafsson K, van der Schouw YT, Simoncini T, Tremollieres F, Rees M, EMAS (2014) EMAS position statement: individualized breast cancer screening versus population-based mammography screening programmes. Maturitas 79:481–486

    Article  Google Scholar 

  11. Sullivan CL, Pandya A, Min RJ, Drotman M, Hentel K (2015) The development and implementation of a patient-centered radiology consultation service: a focus on breast density and additional screening options. Clin Imaging 39:731–734

    Article  Google Scholar 

  12. Chau SL, Alabaster A, Luikart K, Brenman LM, Habel LA (2017) The effect of California’s breast density notification legislation on breast cancer screening. J Prim Care Community Health 8:55–62

    Article  Google Scholar 

  13. Ekpo EU, Mello-Thoms C, Rickard M, Brennan PC, McEntee MF (2016) Breast density (BD) assessment with digital breast tomosynthesis (DBT): agreement between Quantra™ and 5th edition BI-RADS®. Breast 30:185–190

    Article  Google Scholar 

  14. Brandt KR, Scott CG, Ma L, Mahmoudzadeh AP, Jensen MR, Whaley DH, Wu FF, Malkov S, Hruska CB, Norman AD, Heine J, Shepherd J, Pankratz VS, Kerlikowske K, Vachon CM (2016) Comparison of clinical and automated breast density measurements: implications for risk prediction and supplemental screening. Radiology 279:710–719

    Article  Google Scholar 

  15. Damases CN, Mello-Thoms C, McEntee MF (2016) Inter-observer variability in mammographic density assessment using Royal Australian and New Zealand College of Radiologists (RANZCR) synoptic scales. J Med Imaging Radiat Oncol 60:329–336

    Article  Google Scholar 

  16. Machida Y, Saita A, Namba H, Fukuma E (2016) Automated volumetric breast density estimation out of digital breast tomosynthesis data: feasibility study of a new software version. Springerplus 5:780

    Article  Google Scholar 

  17. Shiina N, Sakakibara M, Fujisaki K, Iwase T, Nagashima T, Sangai T, Kubota Y, Akita S, Takishima H, Miyazaki M (2016) Volumetric breast density is essential for predicting cosmetic outcome at the late stage after breast-conserving surgery. Eur J Surg Oncol 42:481–488

    Article  CAS  Google Scholar 

  18. Youk JH, Gweon HM, Son EJ, Kim JA (2016) Automated volumetric breast density measurements in the era of the BI-RADS fifth edition: a comparison with visual assessment. AJR Am J Roentgenol 206:1056–1062

    Article  Google Scholar 

  19. Youn I, Choi S, Kook SH, Choi YJ (2016) Mammographic breast density evaluation in Korean women using fully automated volumetric assessment. J Korean Med Sci 31:457–462

    Article  CAS  Google Scholar 

  20. Gilbert FJ, Tucker L, Gillan MGC et al (2015) Health technology assessment: breast density assessment. NHS 19:29–44

    Google Scholar 

  21. Yamamuro Y, Yamada K, Asai Y et al (2016) Accurate quantification of glandularity and its applications with regard to breast radiation doses and missed lesion rates during individualized screening mammography. In: Tingberg A, Lång K, Timberg P (eds) Proceedings of the 13th International Workshop on Breast Imaging, vol 9699. Springer-Verlag, New York, Inc., New York, pp 377–384

    Google Scholar 

  22. van Engeland S, Snoeren PR, Huisman H, Boetes C, Karssemeijer N (2006) Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging 25:273–282

    Article  Google Scholar 

  23. Maeda K, Matsumoto M, Taniguchi A (2005) Compton-scattering measurement of diagnostic x-ray spectrum using high-resolution Schottky CdTe detector. Med Phys 32:1542–1547

    Article  CAS  Google Scholar 

  24. Hubbell JH, Seltzer SM (1995) Tables of X-ray mass attenuation coefficients and mass energy absorption coefficients 1 keV to 20 MeV for elements Z1 to 92 and 48 additional substances of dosimetric interest. Technology Administration USGPO Washington DC

  25. Dance DR, Skinner CL, Young KC, Beckett JR, Kotre CJ (2000) Additional factors for the estimation of mean glandular breast dose using the UK mammography dosimetry protocol. Phys Med Biol 45:3225–3240

    Article  CAS  Google Scholar 

  26. Dance DR, Hunt RA, Bakic PR, Maidment AD, Sandborg M, Ullman G, Alm CG (2005) Breast dosimetry using high-resolution voxel phantoms. Radiat Prot Dosi 114:359–363

    Article  CAS  Google Scholar 

  27. Dance DR, Young KC, van Engen RE (2009) Further factors for the estimation of mean glandular dose using the United Kingdom, European and IAEA breast dosimetry protocols. Phys Med Biol 54:4361–4372

    Article  CAS  Google Scholar 

  28. Halinski RS, Feldt LS (1970) The selection of variables in multiple regression analysis. J Educat Measur 7:151–157

    Article  Google Scholar 

  29. Sickles EA, D’Orsi CJ, Bassett LW, et al. (2013) ACR BI-RADS® mammography. In: ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology, Reston, VA

  30. Gosch D, Jendrass S, Scholz M, Kahn T (2006) Radiation exposure in full-field digital mammography with a selenium flat-panel detector. Rofo 178:693–697 [Article in German]

    Article  CAS  Google Scholar 

  31. Ruschin M, Timberg P, Bath M, Hemdal B, Svahn T, Saunders RS, Samei E, Andersson I, Mattsson S, Chakrabort DP, Tingber A (2007) Dose dependence of mass and microcalcification detection in digital mammography: free response human observer studies. Med Phys 34:400–407

    Article  Google Scholar 

  32. Mehnati P, Alizadeh H, Hoda H (2016) Relation between mammographic parenchymal patterns and breast cancer risk: considering BMI, compressed breast thickness and age of women in Tabriz, Iran. Asian Pac J Cancer Prev 17:2259–2263

    Article  Google Scholar 

  33. Hanna M, Dumas I, Orain M, Jacob S, Têtu B, Sanschagrin F, Bureau A, Poirier B, Diorio C (2017) Association between expression of inflammatory markers in normal breast tissue and mammographic density among premenopausal and postmenopausal women. Menopause 24:524–535

    Article  Google Scholar 

  34. Lewis MC, Irshad A, Ackerman S, Cluver A, Pavic D, Spruill L, Ralston J, Leddy RJ (2016) Assessing the relationship of mammographic breast density and proliferative breast disease. Breast J 22:541–546

    Article  Google Scholar 

  35. Lundberg FE, Johansson AL, Rodriguez-Wallberg K, Brand JS, Czene K, Hall P, Iliadou AN (2016) Association of infertility and fertility treatment with mammographic density in a large screening-based cohort of women: a cross-sectional study. Breast Cancer Res 18:36–41

    Article  Google Scholar 

  36. Nyante SJ, Sherman ME, Pfeiffer RM, Berrington de Gonzalez A, Brinton LA, Bowles EJ, Hoover RN, Glass A, Gierach GL (2016) Longitudinal change in mammographic density among ER-positive breast cancer patients using Tamoxifen. Cancer Epidemiol Biomark Prev 25:212–216

    Article  CAS  Google Scholar 

Download references

Funding

This work was supported by JSPS Grants-in-Aid Scientific Research, Grant Number JP18K07736.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yoshiyuki Asai.

Ethics declarations

This study was approved by the ethics committee of Kindai University, Japan, and all work was conducted in accordance with the World Medical Association Declaration of Helsinki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yamamuro, M., Asai, Y., Yamada, K. et al. Prediction of glandularity and breast radiation dose from mammography results in Japanese women. Med Biol Eng Comput 57, 289–298 (2019). https://doi.org/10.1007/s11517-018-1882-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1882-4

Keywords

Navigation