MRI as a biomarker for breast cancer diagnosis and prognosis

Abstract

Breast cancer (BC) is the most frequently diagnosed female invasive cancer in Western countries and the leading cause of cancer-related death worldwide. Nowadays, tumor heterogeneity is a well-known characteristic of BC, since it includes several nosological entities characterized by different morphologic features, clinical course and response to treatment. Thus, with the spread of molecular biology technologies and the growing knowledge of the biological processes underlying the development of BC, the importance of imaging biomarkers as non-invasive information about tissue hallmarks has progressively grown. To date, breast magnetic resonance imaging (MRI) is considered indispensable in breast imaging practice, with widely recognized indications such as BC screening in females at increased risk, locoregional staging and neoadjuvant therapy (NAT) monitoring. Moreover, breast MRI is increasingly used to assess not only the morphologic features of the pathological process but also to characterize individual phenotypes for targeted therapies, building on developments in genomics and molecular biology features. The aim of this review is to explore the role of breast multiparametric MRI in providing imaging biomarkers, leading to an improved differentiation of benign and malignant breast lesions and to a customized management of BC patients in monitoring and predicting response to treatment. Finally, we discuss how breast MRI biomarkers offer one of the most fertile ground for artificial intelligence (AI) applications. In the era of personalized medicine, with the development of omics-technologies, machine learning and big data, the role of imaging biomarkers is embracing new opportunities for BC diagnosis and treatment.

Figure 1.

31-year-old female with triple-negative breast cancer of the right breast. (a) Axial fat-suppressed T ₂ weighted image shows a slight hyperintense round mass in the upper inner quadrant of the right breast, with mild intratumoral high signal intensity consistent with intralesional necrosis. (b) Axial ADC map shows a corresponding hypointense area of diffusion restriction. (c) Sagittal post-contrast T ₁ weighted image confirms the presence of a round mass lesion with rim enhancement. ADC, apparent diffusion coefficient.

Figure 2.

54-year-old female with bilateral breast cancer, invasive ductal carcinoma on the right breast and ductal carcinoma in situ on the left breast. (a) Axial DWI image (b value = 1000 s/ mm²) shows an hyperintense lesion between the upper quadrants of the right breast, (b) with corresponding 0,8 × 10⁻³ mm²/s ADC values. (c) Axial DWI image (b value = 1000 s/mm²) shows an hyperintense area in the upper outer quadrant of the left breast, (d) with higher ADC values (1,02 × 10⁻³ mm²/s). ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging.

Figure 3.

61-year-old female with triple-negative breast cancer of the right breast, before (a,b,c) and after 3 months (d,e,f) of neoadjuvant chemotherapy. (a) Axial fat-suppressed T ₂ weighted image shows a 27 mm hyperintense oval mass with regular margins between the lower quadrants of the right breast. (b) The mass appears homogeneously hypointense in the ADC map with 0,7 × 10⁻³ mm²/s ADC value. (c) Axial post-contrast T ₁ weighted image shows a corresponding oval mass with rim enhancement. (d) Axial fat-suppressed T ₂ weighted image shows a reduction in size of the lesion, which appears as a round hyperintense mass with blurred margins. (e) The ADC map shows a hypointense lesion with increased ADC values (1,2 × 10⁻³ mm²/s). (f) Axial post-contrast T ₁ weighted image shows a residual 12 mm round mass lesion. ADC, apparent diffusion coefficient.