Biomarkers Breast

The following list has been reviewed and approved by the European Society of Breast Imaging (EUSOBI)

 

Biomarker

Target

Level of evidence

Evidence

Acquisition requirements

Reading

Issues

Established biomarkers

Mammographic breast density

Breast cancer risk, stratification of mammographic sensitivity

1 [1-4!]

Substantial

Mammography

Qualitative ACR BI-RADS, quantitative computer-assisted (commercial) 

Standardization, reproducibility of visual assessment,

Individual CAD systems reproducible but poor inter reproducibility.

 

ADC

Differential diagnosis of breast MRI lesions, response to NAC

1 [5-13!]

Substantial

MRI with DWI, 2 b-values 0-50 and 800 s/mm2

Visual co-registration of enhancing lesion and ADC map, ROI

Standardization, generally accepted cut-off, pitfalls

 

Pharmacokinetic mapping of DCE data in breast MRI

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

2-3 [14-18!]

Limited (due to heterogeneity)

MRI T1w DCE sequence, temporal resolution <20 sec

Multiple available models, most common free deconvolution, Tofts, modified Tofts

Huge standardization issues

 

Ultrafast DCE-MRI markers

Differential diagnosis of breast MRI lesions

2 [19-23!]

Moderate

MRI T1w DCE acquisition with a temporal resolution around 5 sec

Time-to-Enhancement (TTE)/time-of-arrival (TOA), maximum slope, initial slope,  initial enhancement ratio, initial area under the curve

Standardization, generally accepted cut-off

 

Kaiser score

Differential diagnosis of breast MRI lesions

1 [24-29!]

Moderate

Breast MRI according to international recommendations

Visual assessment of 5 dynamic and morphologic features

Official accreditation by societies

 

Shear Wave Velocity/ Strain elasticity

Differential diagnosis, evaluation of treatment response

1-2 [30-34!]

Moderate

US device with elastography capability

Visual assessment, ROI for quantitative measurements, ratios

Standardization, generally accepted cut-offs

 

Doppler ultrasound: RI/PI

Differential diagnosis, evaluation of treatment response

2 [35, 36!]

Limited

Doppler ultrasound

Acquisition of blood flow spectrum

Standardization, generally accepted cut-offs

 

Contrast-enhanced ultrasound general

Differential diagnosis, evaluation of treatment response

2 [37!]

Moderate

Contrast enhanced ultrasound

Visual assessment, ROI for time-signal intensity curves

Standardization, generally accepted criteria

 

CEUS parameters Time to peak, peak intensity, wash-in & wash-out rate, area under curve

Differential diagnosis, evaluation of treatment response

2-3 [36, 37!]

Limited

Contrast enhanced ultrasound

Visual assessment, ROI for time-signal intensity curves

Standardization, identification of most useful quantitative parameters, generally accepted cut-offs

Biomarkers under investigation /in development

Advanced Diffusion Weighted Imaging

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

2-3 [12!, 38-53!]

Moderate

MRI, acquisition parameters depend on specific technique

Data analysis depends on specific technique

Standardization, accepted cut-offs

 

Volumetric automated assessment of DCE data in breast MRI

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

2 [16!, 54-63!]

Moderate

MRI T1w DCE sequence

Multiple software tools available, detection of hotspot/most suspicious enhancement, volume of enhancement, distribution of curve-types within lesion

Definite use-case, standardization

 

Background parenchymal enhancement (breast MRI)

Breast cancer risk, prediction of response to NAT

1 [64-73!]

Moderate

Dynamic-Contrast-Enhanced MRI

Qualitative ACR BI-RADS, quantitative (experimental)

Standardization, conflicting evidence

 

Total Choline (tCho) from proton MR-spectroscopy

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

1 [74-78!]

Moderate

MR-spectroscopy sequence, PRESS or STEAM, TE30-270ms, best 135 ms

Postprocessing, then visual assessment, SNR determination or quantitation

Standardization, low technical sensitivity

 

FDG-uptake (PET)

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

2 [36!, 79-86!]

Moderate

18F-labeled Fluor-deoxyglucose PET

Visual, ROI for SUV evaluation

Standardization, clinical use-case

 

FEC-uptake (PET)

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

3 [87-89!]

Limited

18F-labeled Fluor-ethylcholine PET

Visual, ROI for SUV evaluation

Standardization, clinical use-case

 

FES-uptake (PET)

Whole body staging and response to therapy

3-4 [90-95!]

Limited

16α-[18F]-fluoro-17β-estradiol PET

Visual, ROI for SUV evaluation

Standardization, clinical use-case

 

FDG Background parenchymal uptake (PET)

Breast cancer risk, cancer phenotyping, response assessment to NAT

3 [86!, 96-98!]

Limited

18F-labeled Fluor-deoxyglucose PET

ROI/VOI for SUV evaluation

Standardization, conflicting evidence

 

Radiomics and Radiogenomics signatures, Deep learning, A.I.

Differential diagnosis (benign vs malignant), cancer phenotyping, response assessment to NAT

2-3 [99-102!]

Limited

Imaging data

 

 

 

Oxygenated/ Deoxygenated Haemoglobin

Differential diagnosis (benign vs malignant), response assessment to NAT

3

Limited

Optical imaging

MIPs, parametric maps, visual assessment, quantification of concentration

Standardisation, clinical use

 

Quantitative ultrasound parameters (attenuation/ scatter coefficients, entropy, textural parameters etc.)

Differential diagnosis (benign vs malignant), response assessment to NAT

3

Limited

Quantitative ultrasound techniques

Data analysis depends on specific technique

Standardization, clinical use

 

Peritumoral oedema

Differential diagnosis (benign vs malignant),

cancer phenotyping, response assessment to NAT

2 [103-109!]

moderate

MRI T2w sequence

 

Qualitative, Visual assessment

Standardization, reproducibility of visual assessment, clinical use

 

CESM

Differential diagnosis (benign vs malignant), response assessment to NAT

2-3 [110-113!]

Limited

Contrast-enhanced mammography

Visual assessment of enhancement; semiquantitative contrast measures under investigation

Needs representative data & benchmarking against standard of care

  1. Destounis SV, Santacroce A, Arieno A (2019) Update on Breast Density, Risk Estimation, and Supplemental Screening. Am J Roentgenol 214:296–305. https://doi.org/10.2214/AJR.19.21994
  2. McCormack VA, dos Santos Silva I (2006) Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol 15:1159–1169. https://doi.org/10.1158/1055-9965.EPI-06-0034
  3. Tice JA, O’Meara ES, Weaver DL, et al (2013) Benign breast disease, mammographic breast density, and the risk of breast cancer. J Natl Cancer Inst 105:1043–1049. https://doi.org/10.1093/jnci/djt124
  4. Le Boulc’h M, Bekhouche A, Kermarrec E, et al (2020) Comparison of breast density assessment between human eye and automated software on digital and synthetic mammography: Impact on breast cancer risk. Diagn Interv Imaging. https://doi.org/10.1016/j.diii.2020.07.004
  5. Newitt DC, Zhang Z, Gibbs JE, et al (2019) Test-retest repeatability and reproducibility of ADC measures by breast DWI: Results from the ACRIN 6698 trial. J Magn Reson Imaging JMRI 49:1617–1628. https://doi.org/10.1002/jmri.26539
  6. Woodhams R, Matsunaga K, Kan S, et al (2005) ADC mapping of benign and malignant breast tumors. Magn Reson Med Sci MRMS Off J Jpn Soc Magn Reson Med 4:35–42
  7. Guo Y, Cai Y-Q, Cai Z-L, et al (2002) Differentiation of clinically benign and malignant breast lesions using diffusion-weighted imaging. J Magn Reson Imaging 16:172–178. https://doi.org/10.1002/jmri.10140
  8. Baltzer PAT, Bickel H, Spick C, et al (2018) Potential of Noncontrast Magnetic Resonance Imaging With Diffusion-Weighted Imaging in Characterization of Breast Lesions: Intraindividual Comparison With Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Invest Radiol 53:229–235. https://doi.org/10.1097/RLI.0000000000000433
  9. Baltzer A, Dietzel M, Kaiser CG, Baltzer PA (2016) Combined reading of Contrast Enhanced and Diffusion Weighted Magnetic Resonance Imaging by using a simple sum score. Eur Radiol 26:884–891. https://doi.org/10.1007/s00330-015-3886-x
  10. Pinker K, Bickel H, Helbich TH, et al (2013) Combined contrast-enhanced magnetic resonance and diffusion-weighted imaging reading adapted to the “Breast Imaging Reporting and Data System” for multiparametric 3-T imaging of breast lesions. Eur Radiol 23:1791–1802. https://doi.org/10.1007/s00330-013-2771-8
  11. Partridge SC, Rahbar H, Murthy R, et al (2011) Improved diagnostic accuracy of breast MRI through combined apparent diffusion coefficients and dynamic contrast-enhanced kinetics. Magn Reson Med 65:1759–1767. https://doi.org/10.1002/mrm.22762
  12. Baxter GC, Graves MJ, Gilbert FJ, Patterson AJ (2019) A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization. Radiology 291:632–641. https://doi.org/10.1148/radiol.2019182510
  13. Baltzer P, Mann RM, Iima M, et al (2020) Diffusion-weighted imaging of the breast-a consensus and mission statement from the EUSOBI International Breast Diffusion-Weighted Imaging working group. Eur Radiol 30:1436–1450. https://doi.org/10.1007/s00330-019-06510-3
  14. Yankeelov TE, Lepage M, Chakravarthy A, et al (2007) Integration of quantitative DCE-MRI and ADC mapping to monitor treatment response in human breast cancer: initial results. Magn Reson Imaging 25:1–13. https://doi.org/10.1016/j.mri.2006.09.006
  15. Marinovich ML, Sardanelli F, Ciatto S, et al (2012) Early prediction of pathologic response to neoadjuvant therapy in breast cancer: systematic review of the accuracy of MRI. Breast Edinb Scotl 21:669–677. https://doi.org/10.1016/j.breast.2012.07.006
  16. Hauth E a. M, Jaeger H, Maderwald S, et al (2006) Evaluation of quantitative parametric analysis for characterization of breast lesions in contrast-enhanced MR mammography. Eur Radiol 16:2834–2841. https://doi.org/10.1007/s00330-006-0348-5
  17. Di Giovanni P, Azlan CA, Ahearn TS, et al (2010) The accuracy of pharmacokinetic parameter measurement in DCE-MRI of the breast at 3 T. Phys Med Biol 55:121–132. https://doi.org/10.1088/0031-9155/55/1/008
  18. Li W, Newitt DC, Gibbs J, et al (2020) Predicting breast cancer response to neoadjuvant treatment using multi-feature MRI: results from the I-SPY 2 TRIAL. NPJ Breast Cancer 6:63. https://doi.org/10.1038/s41523-020-00203-7
  19. Mann RM, Mus RD, van Zelst J, et al (2014) A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging. Invest Radiol 49:579–585. https://doi.org/10.1097/RLI.0000000000000057
  20. Oldrini G, Fedida B, Poujol J, et al (2017) Abbreviated breast magnetic resonance protocol: Value of high-resolution temporal dynamic sequence to improve lesion characterization. Eur J Radiol 95:177–185. https://doi.org/10.1016/j.ejrad.2017.07.025
  21. Pineda FD, Medved M, Wang S, et al (2016) Ultrafast Bilateral DCE-MRI of the Breast with Conventional Fourier Sampling: Preliminary Evaluation of Semi-quantitative Analysis. Acad Radiol 23:1137–1144. https://doi.org/10.1016/j.acra.2016.04.008
  22. Abe H, Mori N, Tsuchiya K, et al (2016) Kinetic Analysis of Benign and Malignant Breast Lesions With Ultrafast Dynamic Contrast-Enhanced MRI: Comparison With Standard Kinetic Assessment. AJR Am J Roentgenol 207:1159–1166. https://doi.org/10.2214/AJR.15.15957
  23. Mann RM, Cho N, Moy L (2019) Breast MRI: State of the Art. Radiology 292:520–536. https://doi.org/10.1148/radiol.2019182947
  24. Marino MA, Clauser P, Woitek R, et al (2016) A simple scoring system for breast MRI interpretation: does it compensate for reader experience? Eur Radiol 26:2529–2537. https://doi.org/10.1007/s00330-015-4075-7
  25. Woitek R, Spick C, Schernthaner M, et al (2017) A simple classification system (the Tree flowchart) for breast MRI can reduce the number of unnecessary biopsies in MRI-only lesions. Eur Radiol 27:3799–3809. https://doi.org/10.1007/s00330-017-4755-6
  26. Wengert GJ, Pipan F, Almohanna J, et al (2020) Impact of the Kaiser score on clinical decision-making in BI-RADS 4 mammographic calcifications examined with breast MRI. Eur Radiol 30:1451–1459. https://doi.org/10.1007/s00330-019-06444-w
  27. Milos RI, Pipan F, Kalovidouri A, et al (2020) The Kaiser score reliably excludes malignancy in benign contrast-enhancing lesions classified as BI-RADS 4 on breast MRI high-risk screening exams. Eur Radiol 30:6052–6061. https://doi.org/10.1007/s00330-020-06945-z
  28. Baltzer PAT, Dietzel M, Kaiser WA (2013) A simple and robust classification tree for differentiation between benign and malignant lesions in MR-mammography. Eur Radiol 23:2051–2060. https://doi.org/10.1007/s00330-013-2804-3
  29. Zhang B, Feng L, Wang L, et al (2020) [Kaiser score for diagnosis of breast lesions presenting as non-mass enhancement on MRI]. Nan Fang Yi Ke Da Xue Xue Bao 40:562–566. https://doi.org/10.12122/j.issn.1673-4254.2020.04.18
  30. Huang R, Jiang L, Xu Y, et al (2019) Comparative Diagnostic Accuracy of Contrast-Enhanced Ultrasound and Shear Wave Elastography in Differentiating Benign and Malignant Lesions: A Network Meta-Analysis. Front Oncol 9:102. https://doi.org/10.3389/fonc.2019.00102
  31. Barr RG, De Silvestri A, Scotti V, et al (2019) Diagnostic Performance and Accuracy of the 3 Interpreting Methods of Breast Strain Elastography: A Systematic Review and Meta-analysis. J Ultrasound Med Off J Am Inst Ultrasound Med 38:1397–1404. https://doi.org/10.1002/jum.14849
  32. Luo J, Cao Y, Nian W, et al (2018) Benefit of Shear-wave Elastography in the differential diagnosis of breast lesion: a diagnostic meta-analysis. Med Ultrason 1:43–49. https://doi.org/10.11152/mu-1209
  33. Blank MAB, Antaki JF (2017) Breast Lesion Elastography Region of Interest Selection and Quantitative Heterogeneity: A Systematic Review and Meta-Analysis. Ultrasound Med Biol 43:387–397. https://doi.org/10.1016/j.ultrasmedbio.2016.09.002
  34. Li D-D, Guo L-H, Xu H-X, et al (2015) Acoustic radiation force impulse elastography for differentiation of malignant and benign breast lesions: a meta-analysis. Int J Clin Exp Med 8:4753–4761
  35. Zhang X-H, Xiao C (2018) Diagnostic Value of Nineteen Different Imaging Methods for Patients with Breast Cancer: a Network Meta-Analysis. Cell Physiol Biochem 46:2041–2055. https://doi.org/10.1159/000489443
  36. Bruening W, Uhl S, Fontanarosa J, et al (2012) Noninvasive Diagnostic Tests for Breast Abnormalities: Update of a 2006 Review. Agency for Healthcare Research and Quality (US), Rockville (MD)
  37. Zhou S-C, Le J, Zhou J, et al (2020) The Role of Contrast-Enhanced Ultrasound in the Diagnosis and Pathologic Response Prediction in Breast Cancer: A Meta-analysis and Systematic Review. Clin Breast Cancer 20:e490–e509. https://doi.org/10.1016/j.clbc.2020.03.002
  38. Baltzer PAT, Schäfer A, Dietzel M, et al (2011) Diffusion tensor magnetic resonance imaging of the breast: a pilot study. Eur Radiol 21:1–10. https://doi.org/10.1007/s00330-010-1901-9
  39. Iima M, Yano K, Kataoka M, et al (2015) Quantitative non-Gaussian diffusion and intravoxel incoherent motion magnetic resonance imaging: differentiation of malignant and benign breast lesions. Invest Radiol 50:205–211. https://doi.org/10.1097/RLI.0000000000000094
  40. Scaranelo AM, Degani H, Grobgeld D, et al (2020) Effect of IV Administration of a Gadolinium-Based Contrast Agent on Breast Diffusion-Tensor Imaging. AJR Am J Roentgenol 215:1030–1036. https://doi.org/10.2214/AJR.19.22085
  41. Wang K, Li Z, Wu Z, et al (2019) Diagnostic Performance of Diffusion Tensor Imaging for Characterizing Breast Tumors: A Comprehensive Meta-Analysis. Front Oncol 9:1229. https://doi.org/10.3389/fonc.2019.01229
  42. Park VY, Kim SG, Kim E-K, et al (2019) Diffusional kurtosis imaging for differentiation of additional suspicious lesions on preoperative breast MRI of patients with known breast cancer. Magn Reson Imaging 62:199–208. https://doi.org/10.1016/j.mri.2019.07.011
  43. Luo J, Hippe DS, Rahbar H, et al (2019) Diffusion tensor imaging for characterizing tumor microstructure and improving diagnostic performance on breast MRI: a prospective observational study. Breast Cancer Res BCR 21:102. https://doi.org/10.1186/s13058-019-1183-3
  44. Huang Y, Lin Y, Hu W, et al (2019) Diffusion Kurtosis at 3.0T as an in vivo Imaging Marker for Breast Cancer Characterization: Correlation With Prognostic Factors. J Magn Reson Imaging JMRI 49:845–856. https://doi.org/10.1002/jmri.26249
  45. Nissan N, Furman-Haran E, Allweis T, et al (2019) Noncontrast Breast MRI During Pregnancy Using Diffusion Tensor Imaging: A Feasibility Study. J Magn Reson Imaging JMRI 49:508–517. https://doi.org/10.1002/jmri.26228
  46. Kim JY, Kim JJ, Kim S, et al (2018) Diffusion tensor magnetic resonance imaging of breast cancer: associations between diffusion metrics and histological prognostic factors. Eur Radiol 28:3185–3193. https://doi.org/10.1007/s00330-018-5429-8
  47. Furman-Haran E, Nissan N, Ricart-Selma V, et al (2018) Quantitative evaluation of breast cancer response to neoadjuvant chemotherapy by diffusion tensor imaging: Initial results. J Magn Reson Imaging JMRI 47:1080–1090. https://doi.org/10.1002/jmri.25855
  48. Yamaguchi K, Nakazono T, Egashira R, et al (2017) Diagnostic Performance of Diffusion Tensor Imaging with Readout-segmented Echo-planar Imaging for Invasive Breast Cancer: Correlation of ADC and FA with Pathological Prognostic Markers. Magn Reson Med Sci MRMS Off J Jpn Soc Magn Reson Med 16:245–252. https://doi.org/10.2463/mrms.mp.2016-0037
  49. Onaygil C, Kaya H, Ugurlu MU, Aribal E (2017) Diagnostic performance of diffusion tensor imaging parameters in breast cancer and correlation with the prognostic factors. J Magn Reson Imaging JMRI 45:660–672. https://doi.org/10.1002/jmri.25481
  50. Liang J, Zeng S, Li Z, et al (2020) Intravoxel Incoherent Motion Diffusion-Weighted Imaging for Quantitative Differentiation of Breast Tumors: A Meta-Analysis. Front Oncol 10:585486. https://doi.org/10.3389/fonc.2020.585486
  51. Sung JS, Malak SF, Bajaj P, et al (2011) Screening breast MR imaging in women with a history of lobular carcinoma in situ. Radiology 261:414–420. https://doi.org/10.1148/radiol.11110091
  52. Iima M, Kataoka M, Kanao S, et al (2018) Intravoxel Incoherent Motion and Quantitative Non-Gaussian Diffusion MR Imaging: Evaluation of the Diagnostic and Prognostic Value of Several Markers of Malignant and Benign Breast Lesions. Radiology 287:432–441. https://doi.org/10.1148/radiol.2017162853
  53. Li W, Newitt DC, Wilmes LJ, et al (2019) Additive value of diffusion-weighted MRI in the I-SPY 2 TRIAL. J Magn Reson Imaging JMRI 50:1742–1753. https://doi.org/10.1002/jmri.26770
  54. Baltzer PAT, Renz DM, Kullnig PE, et al (2009) Application of computer-aided diagnosis (CAD) in MR-mammography (MRM): do we really need whole lesion time curve distribution analysis? Acad Radiol 16:435–442. https://doi.org/10.1016/j.acra.2008.10.007
  55. Baltzer PAT, Freiberg C, Beger S, et al (2009) Clinical MR-mammography: are computer-assisted methods superior to visual or manual measurements for curve type analysis? A systematic approach. Acad Radiol 16:1070–1076. https://doi.org/10.1016/j.acra.2009.03.017
  56. Williams TC, DeMartini WB, Partridge SC, et al (2007) Breast MR imaging: computer-aided evaluation program for discriminating benign from malignant lesions. Radiology 244:94–103. https://doi.org/10.1148/radiol.2441060634
  57. Dietzel M, Zoubi R, Vag T, et al (2013) Association between survival in patients with primary invasive breast cancer and computer aided MRI. J Magn Reson Imaging JMRI 37:146–155. https://doi.org/10.1002/jmri.23812
  58. Dietzel M, Schulz-Wendtland R, Ellmann S, et al (2020) Automated volumetric radiomic analysis of breast cancer vascularization improves survival prediction in primary breast cancer. Sci Rep 10:3664. https://doi.org/10.1038/s41598-020-60393-9
  59. Baltzer PAT, Zoubi R, Burmeister HP, et al (2012) Computer Assisted Analysis of MR-Mammography Reveals Association between Contrast Enhancement and Occurrence of Distant Metastasis. Technol Cancer Res Treat 11:553–560. https://doi.org/10.7785/tcrt.2012.500266
  60. Vag T, Baltzer PAT, Dietzel M, et al (2011) Kinetic analysis of lesions without mass effect on breast MRI using manual and computer-assisted methods. Eur Radiol 21:893–898. https://doi.org/10.1007/s00330-010-2001-6
  61. Dietzel M, Kaiser C, Pinker K, et al (2017) Automated Semi-Quantitative Analysis of Breast MRI: Potential Imaging Biomarker for the Prediction of Tissue Response to Neoadjuvant Chemotherapy. Breast Care Basel Switz 12:231–236. https://doi.org/10.1159/000480226
  62. Pediconi F, Catalano C, Venditti F, et al (2005) Color-coded automated signal intensity curves for detection and characterization of breast lesions: preliminary evaluation of a new software package for integrated magnetic resonance-based breast imaging. Invest Radiol 40:448–457
  63. Baltzer PAT, Vag T, Dietzel M, et al (2010) Computer-aided interpretation of dynamic magnetic resonance imaging reflects histopathology of invasive breast cancer. Eur Radiol 20:1563–1571. https://doi.org/10.1007/s00330-010-1722-x
  64. Hansen NL, Kuhl CK, Barabasch A, et al (2014) Does MRI breast “density” (degree of background enhancement) correlate with mammographic breast density? J Magn Reson Imaging JMRI 40:483–489. https://doi.org/10.1002/jmri.24495
  65. Niell BL, Abdalah M, Stringfield O, et al (2020) Quantitative measures of background parenchymal enhancement predict breast cancer risk. AJR Am J Roentgenol. https://doi.org/10.2214/AJR.20.23804
  66. Elmi A, Conant EF, Kozlov A, et al (2020) Preoperative breast MR imaging in newly diagnosed breast cancer: Comparison of outcomes based on mammographic modality, breast density and breast parenchymal enhancement. Clin Imaging 70:18–24. https://doi.org/10.1016/j.clinimag.2020.10.021
  67. Lo Gullo R, Daimiel I, Rossi Saccarelli C, et al (2020) MRI background parenchymal enhancement, fibroglandular tissue, and mammographic breast density in patients with invasive lobular breast cancer on adjuvant endocrine hormonal treatment: associations with survival. Breast Cancer Res BCR 22:93. https://doi.org/10.1186/s13058-020-01329-z
  68. Arasu VA, Kim P, Li W, et al (2020) Predictive Value of Breast MRI Background Parenchymal Enhancement for Neoadjuvant Treatment Response among HER2- Patients. J Breast Imaging 2:352–360. https://doi.org/10.1093/jbi/wbaa028
  69. Rella R, Bufi E, Belli P, et al (2020) Association between background parenchymal enhancement and tumor response in patients with breast cancer receiving neoadjuvant chemotherapy. Diagn Interv Imaging 101:649–655. https://doi.org/10.1016/j.diii.2020.05.010
  70. Hellgren R, Saracco A, Strand F, et al (2020) The association between breast cancer risk factors and background parenchymal enhancement at dynamic contrast-enhanced breast MRI. Acta Radiol Stockh Swed 1987 284185120911583. https://doi.org/10.1177/0284185120911583
  71. Moliere S, Oddou I, Noblet V, et al (2019) Quantitative background parenchymal enhancement to predict recurrence after neoadjuvant chemotherapy for breast cancer. Sci Rep 9:19185. https://doi.org/10.1038/s41598-019-55820-5
  72. Thompson CM, Mallawaarachchi I, Dwivedi DK, et al (2019) The Association of Background Parenchymal Enhancement at Breast MRI with Breast Cancer: A Systematic Review and Meta-Analysis. Radiology 292:552–561. https://doi.org/10.1148/radiol.2019182441
  73. Arasu VA, Miglioretti DL, Sprague BL, et al (2019) Population-Based Assessment of the Association Between Magnetic Resonance Imaging Background Parenchymal Enhancement and Future Primary Breast Cancer Risk. J Clin Oncol Off J Am Soc Clin Oncol 37:954–963. https://doi.org/10.1200/JCO.18.00378
  74. Baltzer PAT, Dietzel M (2013) Breast lesions: diagnosis by using proton MR spectroscopy at 1.5 and 3.0 T–systematic review and meta-analysis. Radiology 267:735–746. https://doi.org/10.1148/radiol.13121856
  75. Chen J-H, Mehta RS, Baek H-M, et al (2011) Clinical characteristics and biomarkers of breast cancer associated with choline concentration measured by 1H MRS. NMR Biomed 24:316–324. https://doi.org/10.1002/nbm.1595
  76. Sharma U, Agarwal K, Hari S, et al (2019) Role of diffusion weighted imaging and magnetic resonance spectroscopy in breast cancer patients with indeterminate dynamic contrast enhanced magnetic resonance imaging findings. Magn Reson Imaging 61:66–72. https://doi.org/10.1016/j.mri.2019.05.032
  77. Sodano C, Clauser P, Dietzel M, et al (2020) Clinical relevance of total choline (tCho) quantification in suspicious lesions on multiparametric breast MRI. Eur Radiol 30:3371–3382. https://doi.org/10.1007/s00330-020-06678-z
  78. Sah RG, Sharma U, Parshad R, et al (2012) Association of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 status with total choline concentration and tumor volume in breast cancer patients: an MRI and in vivo proton MRS study. Magn Reson Med Off J Soc Magn Reson Med Soc Magn Reson Med 68:1039–1047. https://doi.org/10.1002/mrm.24117
  79. Botsikas D, Kalovidouri A, Becker M, et al (2016) Clinical utility of 18F-FDG-PET/MR for preoperative breast cancer staging. Eur Radiol 26:2297–2307. https://doi.org/10.1007/s00330-015-4054-z
  80. Dong A, Wang Y, Lu J, Zuo C (2016) Spectrum of the Breast Lesions With Increased 18F-FDG Uptake on PET/CT. Clin Nucl Med 41:543–557. https://doi.org/10.1097/RLU.0000000000001203
  81. Kitajima K, Yamano T, Fukushima K, et al (2016) Correlation of the SUVmax of FDG-PET and ADC values of diffusion-weighted MR imaging with pathologic prognostic factors in breast carcinoma. Eur J Radiol 85:943–949. https://doi.org/10.1016/j.ejrad.2016.02.015
  82. Li P, Wang X, Xu C, et al (2020) 18F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients. Eur J Nucl Med Mol Imaging 47:1116–1126. https://doi.org/10.1007/s00259-020-04684-3
  83. Magometschnigg HF, Baltzer PA, Fueger B, et al (2015) Diagnostic accuracy of (18)F-FDG PET/CT compared with that of contrast-enhanced MRI of the breast at 3 T. Eur J Nucl Med Mol Imaging 42:1656–1665. https://doi.org/10.1007/s00259-015-3099-1
  84. Pinker K, Bogner W, Baltzer P, et al (2014) Improved differentiation of benign and malignant breast tumors with multiparametric 18fluorodeoxyglucose positron emission tomography magnetic resonance imaging: a feasibility study. Clin Cancer Res Off J Am Assoc Cancer Res 20:3540–3549. https://doi.org/10.1158/1078-0432.CCR-13-2810
  85. Grueneisen J, Nagarajah J, Buchbender C, et al (2015) Positron Emission Tomography/Magnetic Resonance Imaging for Local Tumor Staging in Patients With Primary Breast Cancer: A Comparison With Positron Emission Tomography/Computed Tomography and Magnetic Resonance Imaging. Invest Radiol 50:505–513. https://doi.org/10.1097/RLI.0000000000000197
  86. Humbert O, Lasserre M, Bertaut A, et al (2018) Breast Cancer Blood Flow and Metabolism on Dual-Acquisition 18F-FDG PET: Correlation with Tumor Phenotype and Neoadjuvant Chemotherapy Response. J Nucl Med Off Publ Soc Nucl Med 59:1035–1041. https://doi.org/10.2967/jnumed.117.203075
  87. Contractor KB, Kenny LM, Stebbing J, et al (2009) [11C]choline positron emission tomography in estrogen receptor-positive breast cancer. Clin Cancer Res Off J Am Assoc Cancer Res 15:5503–5510. https://doi.org/10.1158/1078-0432.CCR-09-0666
  88. Kenny LM, Contractor KB, Hinz R, et al (2010) Reproducibility of [11C]choline-positron emission tomography and effect of trastuzumab. Clin Cancer Res Off J Am Assoc Cancer Res 16:4236–4245. https://doi.org/10.1158/1078-0432.CCR-10-0468
  89. Contractor KB, Kenny LM, Stebbing J, et al (2011) Biological basis of [11C]choline-positron emission tomography in patients with breast cancer: comparison with [18F]fluorothymidine positron emission tomography. Nucl Med Commun 32:997–1004. https://doi.org/10.1097/MNM.0b013e328349567b
  90. Dehdashti F, Mortimer JE, Trinkaus K, et al (2009) PET-based estradiol challenge as a predictive biomarker of response to endocrine therapy in women with estrogen-receptor-positive breast cancer. Breast Cancer Res Treat 113:509–517. https://doi.org/10.1007/s10549-008-9953-0
  91. Linden HM, Kurland BF, Peterson LM, et al (2011) Fluoroestradiol positron emission tomography reveals differences in pharmacodynamics of aromatase inhibitors, tamoxifen, and fulvestrant in patients with metastatic breast cancer. Clin Cancer Res Off J Am Assoc Cancer Res 17:4799–4805. https://doi.org/10.1158/1078-0432.CCR-10-3321
  92. Peterson LM, Kurland BF, Schubert EK, et al (2014) A phase 2 study of 16α-[18F]-fluoro-17β-estradiol positron emission tomography (FES-PET) as a marker of hormone sensitivity in metastatic breast cancer (MBC). Mol Imaging Biol 16:431–440. https://doi.org/10.1007/s11307-013-0699-7
  93. Kurland BF, Peterson LM, Lee JH, et al (2017) Estrogen Receptor Binding (18F-FES PET) and Glycolytic Activity (18F-FDG PET) Predict Progression-Free Survival on Endocrine Therapy in Patients with ER+ Breast Cancer. Clin Cancer Res Off J Am Assoc Cancer Res 23:407–415. https://doi.org/10.1158/1078-0432.CCR-16-0362
  94. Chae SY, Kim S-B, Ahn SH, et al (2017) A Randomized Feasibility Study of 18F-Fluoroestradiol PET to Predict Pathologic Response to Neoadjuvant Therapy in Estrogen Receptor-Rich Postmenopausal Breast Cancer. J Nucl Med Off Publ Soc Nucl Med 58:563–568. https://doi.org/10.2967/jnumed.116.178368
  95. Chae SY, Ahn SH, Kim S-B, et al (2019) Diagnostic accuracy and safety of 16α-[18F]fluoro-17β-oestradiol PET-CT for the assessment of oestrogen receptor status in recurrent or metastatic lesions in patients with breast cancer: a prospective cohort study. Lancet Oncol 20:546–555. https://doi.org/10.1016/S1470-2045(18)30936-7
  96. Leithner D, Horvat JV, Bernard-Davila B, et al (2019) A multiparametric [18F]FDG PET/MRI diagnostic model including imaging biomarkers of the tumor and contralateral healthy breast tissue aids breast cancer diagnosis. Eur J Nucl Med Mol Imaging 46:1878–1888. https://doi.org/10.1007/s00259-019-04331-6
  97. Mema E, Mango VL, Guo X, et al (2018) Does breast MRI background parenchymal enhancement indicate metabolic activity? Qualitative and 3D quantitative computer imaging analysis. J Magn Reson Imaging JMRI 47:753–759. https://doi.org/10.1002/jmri.25798
  98. Choi BB, Kim SH, Park CS, Jung NY (2017) Correlation of Prognostic Factors of Invasive Lobular Carcinoma with ADC Value of DWI and SUVMax of FDG-PET. Chonnam Med J 53:133–139. https://doi.org/10.4068/cmj.2017.53.2.133
  99. Lo Gullo R, Daimiel I, Morris EA, Pinker K (2020) Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging 11:1. https://doi.org/10.1186/s13244-019-0795-6
  100. Le EPV, Wang Y, Huang Y, et al (2019) Artificial intelligence in breast imaging. Clin Radiol 74:357–366. https://doi.org/10.1016/j.crad.2019.02.006
  101. O’Connor JPB, Aboagye EO, Adams JE, et al (2017) Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 14:169–186. https://doi.org/10.1038/nrclinonc.2016.162
  102. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169
  103. Moffa G, Galati F, Collalunga E, et al (2020) Can MRI Biomarkers Predict Triple-Negative Breast Cancer? Diagn Basel Switz 10:. https://doi.org/10.3390/diagnostics10121090
  104. Panzironi G, Moffa G, Galati F, et al (2020) Peritumoral edema as a biomarker of the aggressiveness of breast cancer: results of a retrospective study on a 3 T scanner. Breast Cancer Res Treat 181:53–60. https://doi.org/10.1007/s10549-020-05592-8
  105. Baltzer PAT, Yang F, Dietzel M, et al (2010) Sensitivity and specificity of unilateral edema on T2w-TSE sequences in MR-Mammography considering 974 histologically verified lesions. Breast J 16:233–239. https://doi.org/10.1111/j.1524-4741.2010.00915.x
  106. Cheon H, Kim HJ, Kim TH, et al (2018) Invasive Breast Cancer: Prognostic Value of Peritumoral Edema Identified at Preoperative MR Imaging. Radiology 287:68–75. https://doi.org/10.1148/radiol.2017171157
  107. Kaiser CG, Herold M, Krammer J, et al (2017) Prognostic Value of “Prepectoral Edema” in MR-mammography. Anticancer Res 37:1989–1995. https://doi.org/10.21873/anticanres.11542
  108. Dietzel M, Baltzer PA, Vag T, et al (2011) Potential of MR mammography to predict tumor grading of invasive breast cancer. RöFo Fortschritte Auf Dem Geb Röntgenstrahlen Nukl 183:826–833. https://doi.org/10.1055/s-0031-1273244
  109. Baltzer PAT, Dietzel M, Gajda, et al (2012) A systematic comparison of two pulse sequences for edema assessment in MR-mammography. Eur J Radiol 81:1500–1503. https://doi.org/10.1016/j.ejrad.2011.03.001
  110. Zanardo M, Cozzi A, Trimboli RM, et al (2019) Technique, protocols and adverse reactions for contrast-enhanced spectral mammography (CESM): a systematic review. Insights Imaging 10:76. https://doi.org/10.1186/s13244-019-0756-0
  111. Suter MB, Pesapane F, Agazzi GM, et al (2020) Diagnostic accuracy of contrast-enhanced spectral mammography for breast lesions: A systematic review and meta-analysis. Breast Edinb Scotl 53:8–17. https://doi.org/10.1016/j.breast.2020.06.005
  112. Tang S, Xiang C, Yang Q (2020) The diagnostic performance of CESM and CE-MRI in evaluating the pathological response to neoadjuvant therapy in breast cancer: a systematic review and meta-analysis. Br J Radiol 93:20200301. https://doi.org/10.1259/bjr.20200301
  113. Zamora K, Allen E, Hermecz B (2021) Contrast mammography in clinical practice: Current uses and potential diagnostic dilemmas. Clin Imaging 71:126–135. https://doi.org/10.1016/j.clinimag.2020.11.002

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