Abstract
Organised breast cancer screening has been shown to reduce breast cancer mortality, especially if cancers are diagnosed at an earlier stage. The acquisition of high-quality digital mammograms or breast tomosynthesis images is critical for ensuring optimal screening sensitivity and early detection. Breast positioning is one of the most important aspects of image quality and is often cited as the main reason for failure in image quality reviews or accreditation. There is a lack of global consensus as to the most clinically meaningful criteria for evaluating breast positioning quality, the language used, and the interpretation of such criteria. Current methods for the clinical evaluation of breast positioning rely on visual methods, which are both subjective and time-consuming. This has hampered the use of large-scale data to define minimum positioning standards and for comprehensive analysis of image quality for technologists and screening providers.
In this review chapter, we provide an overview of an automated breast positioning evaluation system that leverages image processing and machine learning methods, to provide comprehensive metric-, image- and study-level analysis of breast positioning. Using real clinical data, examples are also provided that demonstrate how this automated evaluation system can integrate into clinical practice to provide technologists and managers with consistent, objective, and continuous feedback on breast positioning. Trend analyses and benchmarking against organisation and global averages can identify target areas for improvement and supports realistic goal setting. From an administrative perspective, automated breast positioning and reporting also facilitates compliance with accreditation standards and continuous quality assurance programs.
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References
Smith RA, Duffy SW, Tabár L. Breast cancer screening: the evolving evidence. Oncology. 2012;26(5):9–81, 85–86, 471–75.
Duffy SW, Tabár L, Yen AM-F, Dean PB, Smith RA, Jonsson H, Tornberg S, Chen SL, Chiu SY, Fann JC, Ku MM. Mammography screening reduces rates of advanced and fatal breast cancers: results in 549,091 women. Cancer. 2020;126(13):2971–9.
Tabár L, Chen TH-H, Yen AM-F, Chen SL-S, Fann JC-Y, Chiu SY-H, Ku MM, Wu WY, Hsu CY, Chen YY, Beckmann K. Effect of mammography screening on mortality by histological grade. Cancer Epidemiol Biomark Prev. 2018;27(2):154–7.
Tabár L, Yen AM-F, Wu WY-Y, Chen SL-S, Chiu SY-H, Fann JC-Y, Ku MM, Smith RA, Duffy SW, Chen TH. Insights from the breast cancer screening trials: how screening affects the natural history of breast cancer and implications for evaluating service screening programs. Breast J. 2015;21(1):13–20.
Taylor K, Parashar D, Bouverat G, Poulos A, Gullien R, Stewart E, Aarre R, Crystal P, Wallis M. Mammographic image quality in relation to positioning of the breast: a multicentre international evaluation of the assessment systems currently used, to provide an evidence base for establishing a standardised method of assessment. Radiography. 2017;23(4):343–9.
Pal S, Ikeda DM, Jesinger RA, Mickelsen LJ, Chen CA, Larson DB. Improving performance of mammographic breast positioning in an academic radiology practice. AJR Am J Roentgenol. 2018;210(4):807–15.
Moreira C, Svoboda K, Poulos A. Comparison of the validity and reliability of two image classification systems for the assessment of mammogram quality. J Med Screen. 2005;12(1):38–42.
Sweeney RI, Lewis SJ, Hogg P, McEntee MF. A review of mammographic positioning image quality criteria for the craniocaudal projection. Br J Radiol. 2017;91(1082):20170611.
Taplin SH, Rutter CM, Finder C, Mandelson MT, Houn F, White E. Screening mammography - clinical image quality and the risk of interval breast cancer. AJR Am J Roentgenol. 2002;178(4):797–803.
Bae MS, Moon WK, Chang JM, Koo HR, Kim WH, Cho N, Yi A, La Yun B, Lee SH, Kim MY, Ryu EB. Breast cancer detected with screening US: reasons for nondetection at mammography. Radiology. 2014;270(2):369–77.
Mercieca N, Portelli JL, Jadva-Patel H. Mammographic image reject rate analysis and cause – a national Maltese study. Radiography. 2017;23(1):25–31.
Yeom YK, Chae EY, Kim HH, Cha JH, Shin HJ, Choi WJ. Screening mammography for second breast cancers in women with history of early-stage breast cancer: factors and causes associated with non-detection. BMC Med Imaging. 2019;19(1):2.
Salkowski LR, Elezaby M, Fowler AM, Burnside E, Woods RW, Strigel RM. Comparison of screening full-field digital mammography and digital breast tomosynthesis technical recalls. J Med Imaging. 2019;6(3):031403.
Gilroy HM, Hill ML, Chan A, Halling-Brown M, Highnam RP. Automated breast positioning evaluation of screening mammograms in the UK. Poster presented at the European Congress of Radiology 2021. Vienna: ESR; 2021.
Guertin M, Theberge I, Dufresne M, Zomahoun HTV, Major D, Tremblay R, Ricard C, Shumak R, Wadden N, Pelletier E, Brisson J. Clinical image quality in daily practice of breast cancer mammography screening. Can Assoc Radiol J. 2014;65(3):199–206.
Rouette J, Elfassy N, Bouganim N, Yin H, Lasry N, Azoulay L. Evaluation of the quality of mammographic breast positioning: a quality improvement study. CMAJ Open. 2021;9(2):E607–E12.
Bassett LW, Hirbawi IA, DeBruhl N, Hayes MK. Mammographic positioning: evaluation from the view box. Radiology. 1993;188(3):803–6.
Huppe AI, Overman KL, Gatewood JB, Hill JD, Miller LC, Inciardi MF. Mammography positioning standards in the digital era: is the status quo acceptable? AJR Am J Roentgenol. 2017;209(6):1419–25.
American College of Radiology. Mammography accreditation clinical image review sheet. Reston, VA: ACR. https://www.acraccreditation.org/-/media/ACRAccreditation/Documents/Mammography/Clinical-Image-Review-Sheet%2D%2D-MAP.pdf?la=en. Accessed 31 Aug 2021.
National Health Services Breast Screening Program. Guidance for breast screening mammographers. 3rd ed. Sheffield: NHS Breast Screening Programme; 2017.
BreastScreen Australia Accredidation Review Commitee. National accreditation standards. Canberra, ACT: BreastScreen Australia National Accreditation Standards (NAS). https://www.health.gov.au/. Accessed 31 Aug 2021.
LRCB. Dutch Expert Centre for Screening. Criteria regarding positioning techniques for mammography. Nijmegen: LRCB; 2009. https://www.lrcb.nl/en/download/criteria-regarding-positioning-techniques-for-mammography/. Accessed 31 Aug 2021.
Rijken H, Caseldine J, Laird O. Radiological guidelines. In: Perry N, Broeders M, de Wolf C, Tornberg S, Holland R, von Karsa L, editors. European guidelines for quality assurance in breast cancer screening and diagnosis. 4th ed. Luxembourg: Office for Official Publications of the Europeam Communities; 2006.
U.S. Food and Drug Adminstration. Mammography Quality Standards Act regulations. Silver Spring, MD: USFDA; 2002. https://www.fda.gov/radiation-emitting-products/regulations-mqsa/mammography-quality-standards-act-regulations#s90012. Accessed 31 Aug 2021.
U.S. Food and Drug Administration. EQUIP: enhancing quality using the inspection program. Silver Spring, MD: USFDA; 2017. https://www.fda.gov/radiation-emitting-products/mqsa-insights/equip-enhancing-quality-using-inspection-program. Accessed 31 Aug 2021.
Rauscher GH, Tossas-Milligan K, Macarol T, Grabler PM, Murphy AM. Trends in attaining mammography quality benchmarks with repeated participation in a quality measurement program: going beyond the mammography quality standards act to address breast cancer disparities. J Am Coll Radiol. 2020;17(11):1420–8.
Royal Australian and New Zealand College of Radiologists. Mammography quality assurance program. Wellington: RANZCR. https://www.ranzcr.com/fellows/clinical-radiology/quality-assurance-and-accreditation/mqap. Accessed 31 Aug 2021.
Canadian Association of Radiologists. Mammography accreditation program (MAP). Ottawa, ON: CAR. https://car.ca/patient-care/map/?__cf_chl_captcha_tk__=pmd_gkAOL23GLnGIbPkvq.nu09KoPLPpkyV6diRec_bSouE-1629925597-0-gqNtZGzNA5CjcnBszQhl. Accessed 31 Aug 2021.
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Acknowedgement The authors would like to thank Dr Jones & Partners Medical Imaging (Attunga Medical Centre, Toorak Gardens, SA, Australia) and their technologists for kindly allowing us to use screenshots from their Volpara Analytics dashboards.
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Chan, A., Howes, J., Hill, C., Highnam, R. (2022). Automated Assessment of Breast Positioning in Mammography Screening. In: Mercer, C., Hogg, P., Kelly, J. (eds) Digital Mammography. Springer, Cham. https://doi.org/10.1007/978-3-031-10898-3_22
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