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Hypotheses for the Future

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Breast MRI for High-risk Screening

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Abstract

This chapter presents possible scenarios for breast cancer high-risk screening. Initially, we describe the underutilization of breast MRI for high-risk screening and the best solution, unfortunately as yet adopted in only a few countries, integrating the high-risk screening into the context of general population-based screening programs, as a primer for personalized screening protocols. Thereafter, we outlined four major trends: (1) the general question whether increasingly effective systemic therapies, especially those including both chemotherapeutic drugs and targeted treatments, are expected soon to nullify the advantages of early diagnosis; (2) the potential application of innovative approaches such as liquid biopsy and smart bras; (3) the alternative use of non-MRI imaging methods, including breast tomosynthesis, automated breast ultrasound, contrast-enhanced mammography, breast-dedicated contrast-enhanced computed tomography, and optical imaging; and (4) the possibility of reducing doses of gadolinium chelates and the potential use of non-contrast MRI methods, especially diffusion-weighted sequences. Finally, we outline the perspectives of artificial intelligence applications to breast imaging, in particular to breast MRI, as well as those of a personalized approach to breast cancer screening based on tailored risk stratifications. A last wishful thinking is dedicated to the need to expand research from the mainstream of early detection and therapy to primary prevention, using background parenchymal enhancement on MRI as a possible imaging biomarker of effects of lifestyle modifications.

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Notes

  1. 1.

    PPV1 is the fraction of true positives related to all positive recalls.

  2. 2.

    PPV2 is the fraction of true positives related to the number of recommended biopsies.

  3. 3.

    PPV3 is the fraction of true positives related to number of performed biopsies.

  4. 4.

    Cozzi A, Monti CB, Monaco C et al (2020) Contrast-enhanced mammography (CEM): a systematic review and meta-analysis of diagnostic performance (Abstract accepted as Oral Presentation at the European Congress of Radiology 2020).

  5. 5.

    A couple of interesting papers have been recently published and have to be mentioned. The first one regards the results of the DENSE study – Bakker MF, de Lange SV, Pijnappel RM et al. (2019) Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 38:2091–2102 – a multicenter randomized controlled trial. The authors invited 8,061 women to MRI and 32,312 women to mammography (1:4 ratio). The 2-year interval cancer rate was 2.5 per 1,000 in the MRI group versus 5.0 per 1,000 in the mammography group (p < 0.001), an important result showing that the high MRI detection rate (16.5 per 1,000) allowed to halve the interval cancer rate. However, for MRI, PPV1 (recall rate) was 17%, PPV3 (biopsy) was 26%, and the false positive rate was 8%, while MRI screening was accepted only by 59% of the invited women. In addition, considering that this was a prevalent screening round, the authors noted “the relatively large number of well-differentiated and hormone-positive cancers among the MRI participants” and that an unknown fraction of MRI-detected cancers may represent overdiagnosis. The second paper – Obdeijn IM, Mann RM, Loo CCE et al. (2020) The supplemental value of mammographic screening over breast MRI alone in BRCA2 mutation carriers. Breast Cancer Res Treat 181:581–588 – reported an overall screening sensitivity of 95.2% (81/85), with only 4 interval cancers, with a sensitivity of 86% for MRI and 50% for mammography (p < 0.001). In women below 40, one 6-mm grade 3 DCIS was detected by mammography, being only retrospectively visible on MRI, while other 7 cancers detected only at mammography were diagnosed in women aged 50 years and older, increasing sensitivity in this subgroup from 80% to 96% (p < 0.001). The authors concluded by suggesting to postpone mammographic screening in BRCA2 mutation carriers to at least age 40.

Abbreviations

ABUS:

Automated breast ultrasound

AI:

Artificial intelligence

BC:

Breast cancer

BDCT:

Breast-dedicated computed tomog-raphy

BI-RADS:

Breast Imaging Reporting and Data System

CE-MRI:

Contrast-enhanced magnetic resonance imaging

CEM:

Contrast-enhanced mammography

CI:

Confidence interval

DBT:

Digital breast tomosynthesis

DCIS:

Ductal carcinoma in situ

DWI:

Diffusion-weighted imaging

ER:

Estrogen receptor

HER2:

Human epidermal growth factor receptor 2

HHUS:

Hand-held ultrasound

MRI:

Magnetic resonance imaging

OR:

Odds ratio

PPV:

Positive predictive value

ROC-AUC:

Area under the curve at receiver operating characteristics analysis

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Authors and Affiliations

  1. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy

    Francesco Sardanelli

  2. Unit of Radiology, IRCCS Policlinico San Donato, San Donato Milanese, Italy

    Francesco Sardanelli

  3. Department of Cell Biology and Neurosciences, Molecular and Cellular Imaging Unit, Istituto Superiore di Sanità (ISS), Rome, Italy

    Franca Podo

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Correspondence to Francesco Sardanelli .

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Editors and Affiliations

  1. Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, Italy

    Francesco Sardanelli

  2. Department of Cell Biology and Neurosciences Molecular and Cellular Imaging Unit, Istituto Superiore di Sanità (ISS), Rome, Italy

    Franca Podo

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Sardanelli, F., Podo, F. (2020). Hypotheses for the Future. In: Sardanelli, F., Podo, F. (eds) Breast MRI for High-risk Screening. Springer, Cham. https://doi.org/10.1007/978-3-030-41207-4_23

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