Standardization of Quantitative DCE-MRI Parameters Measurement: An Urgent Need for Breast Cancer Imaging

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With recent technological advances, the contribution that imaging makes to the care of cancer patients goes well beyond tumor morphology. Advanced MRI sequences and sophisticated image analysis software have enabled the detection of specific biological processes, which reflect cancer behavior, including tumor aggressiveness and response to treatment. In breast imaging, dynamic contrast enhanced (DCE) sequences play key roles in cancer detection and characterization ( 1 Pinker K Helbich TH Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol. 2017; https://doi.org/10.1259/bjr.20160715 Crossref PubMed Scopus (72) Google Scholar ). Thus, advanced DCE sequences have the potential to revolutionize breast cancer imaging. Indeed, such MRI sequences are characterized by high temporal resolution, enabling the acquisition of multiple repeated images in a short time interval and, through the application of a pharmacokinetic model, to analyze the transfer of contrast agent through the capillary vessel wall, a process strongly related to tissue permeability and neoangiogenesis. Such biological phenomena are described by quantitative values, such as the contrast transfer from the vessel to the extravascular extracellular space (Ktrans) and vice-versa (Kep), the amount of contrast agent within the vessel (plasma volume) and in the EES (Ve) ( 2 Bernstein JM Kershaw LE Withey SB et al. Tumor plasma flow determined by dynamic contrast-enhanced MRI predicts response to induction chemotherapy in head and neck cancer. Oral Oncol. 2015; https://doi.org/10.1016/j.oraloncology.2015.01.013 Crossref PubMed Scopus (15) Google Scholar ). Several studies explored the role of such parameters in different imaging tasks, including differentiating benign from malignant breast lesions, characterizing breast cancer aggressiveness, predicting patients’ prognosis and the early assessment of the response to neoadjuvant chemotherapy ( 3 Kim Y Kim SH Song BJ et al. Early prediction of response to neoadjuvant chemotherapy using dynamic contrast-enhanced MRI and ultrasound in breast cancer. Korean J Radiol. 2018; https://doi.org/10.3348/kjr.2018.19.4.682 Crossref Scopus (33) Google Scholar , 4 Margolis NE Moy L Sigmund EE et al. Assessment of aggressiveness of breast cancer using simultaneous 18F-FDG-PET and DCE-MRI. Clin Nucl Med. 2016; https://doi.org/10.1097/RLU.0000000000001254 Crossref PubMed Scopus (18) Google Scholar , 5 Liu F Wang M Li H. Role of perfusion parameters on DCE-MRI and ADC values on DWMRI for invasive ductal carcinoma at 3.0 Tesla. World J Surg Oncol. 2018; https://doi.org/10.1186/s12957-018-1538-8 Crossref Scopus (15) Google Scholar , 6 Lee J Kim SH Kang BJ. Pretreatment prediction of pathologic complete response to neoadjuvant chemotherapy in breast cancer: perfusion metrics of dynamic contrast enhanced MRI. Sci Rep. 2018; https://doi.org/10.1038/s41598-018-27764-9 Crossref Scopus (15) Google Scholar ). This last possible application represents one of the most fascinating uses of quantitative DCE-MRI parameters, if we consider that changes in tumor biology are supposed to occur earlier than the morphologic ones ( 7 Romeo V Accardo G Perillo T et al. Assessment and prediction of response to neoadjuvant chemotherapy in breast cancer: A comparison of imaging modalities and future perspectives. Cancers (Basel). 2021; https://doi.org/10.3390/cancers13143521 Crossref Scopus (5) Google Scholar ). Thus, changes of perfusion parameters after the first chemotherapy cycles may early reflect tumor response or resistance. Moreover, pre-treatment values might reflect the tumor vascular asset, providing important information related to drug delivery and, therefore, treatment response.