BREAST

Validated biomarkers

Level of evidence

Single centre/ Multicentre/ metaanalysis

Indication

Patient prep

Data acquisition requirements

Image processing algorithm

 

Morphology (shape, margins, calcification type)

1a1!, 1b2!

Population screening studies

Differentiate benign from malignant

No

High resolution T1W contrast enhanced images with fat saturation

Manual documentation by trained observer

 

Tumour volume

1b3!

Multicentre-I-SPY

Response to NACT

No

High resolution T1W contrast enhanced images with fat saturation

Manual delineation by trained observer

 

Dynamic contrast uptake pattern 

(BI-RADS)

14,5,6!

Multicentre

Differentiate benign from malignant

No

Temporal resolution of less than 1 minute

Image subtraction

Recommended for clinical trials

ADC

1b7!

Single centre

Detection/ Differentiate malignancy

No

At least 2 b-values, excluding 0 value. b=50 and 1000s/mm2 preferred

Monoexponential fit of data

 

ADC parameter maps

2b8!

Multicentre

Response to NACT

No

As for ADC

Colour display of ADC values on a voxel-by-voxel basis

 

Enhancement fraction/ Functional tumour volume

1b9!

Multicentre-I-SPY

Response to NACT

No

Temporal resolution of less than 1 minute

Need to set threshold for abnormal enhancement

               
 

Ktrans, kep, Ve

2b10-15!

Single centre

Differentiate benign from malignant

Prognostic for tumour subtype

Response to NACT

No

Temporal resolution of less than 1 minute

Method of defining arterial input function needs to be set (individual vs population based). Latter preferred

Multiple models available16! Tofts most commonly used

 

MRS

1b17!

Multicentre-I-SPY

Response to NACT

No

Resolution critical. 2D vs 3D techniques. Size of single voxel acquisition needs to address local SNR issues

jMRUI or similar, peak height or area under peak in relation to reference value from test object or normal tissue

Biomarkers in development/ not recommended

US-SWE

2b18!

Single centre

Response to NACT

No

None

Qualitative or quantitative display of shear wave speed or Young’s modulus

 

Radiomic signatures (morphology and enhancement)

3b19!

Database search

Response to NACT

No

As for high-resolution morphological, DCE and DWI imaging

No consensus, variable data20!

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  2. Riedl CC1, Ponhold L, Flöry D, Weber M, Kroiss R, Wagner T, Fuchsjäger M, Helbich TH. Magnetic resonance imaging of the breast improves detection of invasive cancer, preinvasive cancer, and premalignant lesions during surveillance of women at high risk for breast cancer. Clin Cancer Res. 2007 Oct 15;13(20):6144-52.

  3. Hylton NM.Locally advanced breast cancer: MR imaging for prediction of response to neoadjuvant chemotherapy--results from ACRIN 6657/I-SPY TRIAL Radiology. 2012 PMID: 22623692PMCID: PMC3359517

  4. Sohns C, Scherrer M, Staab W, Obenauer S. Value of the BI-RADS classification in MR-Mammography for diagnosis of benign and malignant breast tumors. Eur Radiol. 2011 Dec;21(12):2475-83. doi: 10.1007/s00330-011-2210-7. Epub 2011 Jul 31.

  5. Fujiwara K, Yamada T, Kanemaki Y, Okamoto S, Kojima Y, Tsugawa K, Nakajima Y. Grading System to Categorize Breast MRI in BI-RADS 5th Edition: A Multivariate Study of Breast Mass Descriptors in Terms of Probability of Malignancy. AJR Am J Roentgenol. 2018 Mar;210(3):W118-W127. doi: 10.2214/AJR.17.17926. Epub 2018 Jan 30. PMID:29381382

  6. Hamy AS, Giacchetti S, Albiter M, de Bazelaire C, Cuvier C, Perret F, Bonfils S, Charvériat P, Hocini H, de Roquancourt A, Espie M. BI-RADS categorisation of 2,708 consecutive nonpalpable breast lesions in patients referred to a dedicated breast care unit. Eur Radiol. 2012 Jan;22(1):9-17. doi: 10.1007/s00330-011-2201-8. Epub 2011 Jul 16. PMID: 21769528

  7. Spick C, Bickel H, Pinker K, Bernathova M, Kapetas P, Woitek R, Clauser P, Polanec SH, Rudas M, Bartsch R, Helbich TH, Baltzer PA. Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy. NMR Biomed. 2016 Oct;29(10):1445-53. doi: 10.1002/nbm.3596. Epub 2016 Aug 24.

  8. Galbán CJ, Ma B, Malyarenko D, Pickles MD, Heist K, Henry NL, Schott AF, Neal CH, Hylton NM, Rehemtulla A, Johnson TD, Meyer CR, Chenevert TL, Turnbull LW, Ross BD. Multi-site clinical evaluation of DW-MRI as a treatment response metric for breast cancer patients undergoing neoadjuvant chemotherapy. PLoS One. 2015 Mar 27;10(3):e0122151. doi: 10.1371/journal.pone.0122151. eCollection 2015

  9. Hylton NM, Gatsonis CA, Rosen MA, Lehman CD, Newitt DC, Partridge SC, Bernreuter WK, Pisano ED, Morris EA, Weatherall PT, Polin SM, Newstead GM, Marques HS, Esserman LJ, Schnall MD; ACRIN 6657 Trial Team and I-SPY 1 TRIAL Investigators. Neoadjuvant Chemotherapy for Breast Cancer: Functional Tumor Volume by MR Imaging Predicts Recurrence-free Survival-Results from the ACRIN 6657/CALGB 150007 I-SPY 1 TRIAL. Radiology. 2016 Apr;279(1):44-55. doi: 10.1148/radiol.2015150013. Epub 2015 Dec 1.

  10. Huang W, Li X, Chen Y, Li X, Chang MC, Oborski MJ, Malyarenko DI, Muzi M, Jajamovich GH, Fedorov A, Tudorica A, Gupta SN, Laymon CM, Marro KI, Dyvorne HA, Miller JV, Barbodiak DP, Chenevert TL, Yankeelov TE, Mountz JM, Kinahan PE, Kikinis R, Taouli B, Fennessy F, Kalpathy-Cramer J. Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge. Transl Oncol. 2014 Feb 1;7(1):153-66. eCollection 2014 Feb. PMID:24772219

  11. Sorace AG, Partridge SC, Li X, Virostko J, Barnes SL, Hippe DS, Huang W, Yankeelov TE. Distinguishing benign and malignant breast tumors: preliminary comparison of kinetic modeling approaches using multi-institutional dynamic contrast-enhanced MRI data from the International Breast MR Consortium 6883 trial. J Med Imaging (Bellingham). 2018 Jan;5(1):011019. doi: 10.1117/1.JMI.5.1.011019. Epub 2018 Jan 22. PMID: 29392160

  12. Cheng Z, Wu Z, Shi G, Yi Z, Xie M, Zeng W, Song C, Zheng C, Shen J. Discrimination between benign and malignant breast lesions using volumetric quantitative dynamic contrast-enhanced MR imaging. Eur Radiol. 2018 Mar;28(3):982-991. doi: 10.1007/s00330-017-5050-2. Epub 2017 Sep 19. PMID: 28929243

  13. Wang C, Wei W, Santiago L, Whitman G, Dogan B. Can imaging kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging be valuable in predicting clinicopathological prognostic factors of invasive breast cancer? Acta Radiol. 2017 Jan 1:284185117740746. doi: 10.1177/0284185117740746. [Epub ahead of print] PMID: 29105486

  14. Li X, Abramson RG, Arlinghaus LR, Kang H, Chakravarthy AB, Abramson VG, Farley J, Mayer IA, Kelley MC, Meszoely IM, Means-Powell J, Grau AM, Sanders M, Yankeelov TE. Multiparametric magnetic resonance imaging for predicting pathological response after the first cycle of neoadjuvant chemotherapy in breast cancer. Invest Radiol. 2015 Apr;50(4):195-204. doi: 10.1097/RLI.0000000000000100 PMID: 25360603

  15. Pinker K, Bogner W, Baltzer P, Gruber S, Bickel H, Brueck B, Trattnig S, Weber M, Dubsky P, Bago-Horvath Z, Bartsch R, Helbich TH. Improved diagnostic accuracy with multiparametric magnetic resonance imaging of the breast using dynamic contrast-enhanced magnetic resonance imaging, diffusion-weighted imaging, and 3-dimensional proton magnetic resonance spectroscopic imaging. Invest Radiol. 2014 Jun;49(6):421-30. doi: 10.1097/RLI.0000000000000029. PMID: 24566292

  16. Ewing JR, Bagher-Ebadian H. Model selection in measures of vascular parameters using dynamic contrast-enhanced MRI: experimental and clinical applications. NMR Biomed. 2013 Aug;26(8):1028-41. doi: 10.1002/nbm.2996. Review. PMID: 23881857

  17. Bolan PJ, Kim E, Herman BA, Newstead GM, Rosen MA, Schnall MD, Pisano ED, Weatherall PT, Morris EA, Lehman CD, Garwood M, Nelson MT, Yee D, Polin SM, Esserman LJ, Gatsonis CA, Metzger GJ, Newitt DC, Partridge SC, Hylton NM; ACRIN Trial team ISPY-1 Investigators. MR spectroscopy of breast cancer for assessing early treatment response: Results from the ACRIN 6657 MRS trial. J Magn Reson Imaging. 2017 Jul;46(1):290-302. doi: 10.1002/jmri.25560. Epub 2016 Dec 16.

  18. Ma Y, Zhang S, Zang L, Li J, Li J, Kang Y, Ren W. Combination of shear wave elastography and Ki-67 index as a novel predictive modality for the pathological response to neoadjuvant chemotherapy in patients with invasive breast cancer. Eur J Cancer. 2016 Dec;69:86-101. doi: 10.1016/j.ejca.2016.09.031. Epub 2016 Nov 4.

  19. Li H, Zhu Y, Burnside ES, Drukker K, Hoadley KA, Fan C, Conzen SD, Whitman GJ, Sutton EJ, Net JM, Ganott M, Huang E, Morris EA, Perou CM, Ji Y, Giger ML. MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology. 2016 Nov;281(2):382-391. Epub 2016 May 5.

  20. Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, Aerts HJ, Gillies RJ, Lambin P. The effect of SUV discretization in quantitative FDG-PET Radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015 Aug 5;5:11075. doi: 10.1038/srep11075.PMID:26242464