Characterization of Tumors with DCE-MRI
Wei Huang1

1Oregon Health & Science University

Synopsis

This lecture discusses the common methods for DCE-MRI data analysis, the clinical applications of DCE-MRI in cancer imaging, the major factors that cause variabilities in estimated DCE-MRI metrics and affect utility in cancer imaging, and the potential of the Shutter-Speed DCE-MRI method for imaging tumor metabolic activity.

Highlights

  • There are three approaches for analysis of DCE-MRI time-course data: qualitative, semi-quantitative, and quantitative. Quantitative pharmacokinetic analysis of DCE-MRI data estimates parameters that are directly related to tissue biology.

  • Clinical applications of DCE-MRI in cancer imaging include cancer detection and evaluation of therapeutic response.

  • Major factors that cause variations in estimated quantitative DCE-MRI parameters and consequently affect the utility of quantitative DCE-MRI in clinical practice include model selection, quantifications of native tissue T1 and AIF, temporal resolution, and acquisition time.

  • The Shutter-Speed model takes into account the effects of water exchange and may offer the potential for imaging metabolic activities.

Target Audience

Investigators who have been using or plan to use dynamic contrast-enhanced (DCE) MRI for cancer imaging.

Outcome/Objectives

Besides the basics of DCE-MRI and examples of clinical applications, this lecture will discuss major steps in data acquisition and analysis that cause variations in estimated DCE-MRI parameters, which may affect the utility of DCE-MRI for cancer imaging. The Shutter-Speed DCE-MRI method will be introduced as an imaging method that, in addition to imaging tumor microvasculature, may also provide high resolution imaging of tumor metabolic activities.

Introduction

DCE-MRI involves serial acquisition of T1-weighted MR images of a tissue of interest (e.g., a tumor) before, during, and after an intravenous injection of paramagnetic contrast agent (CA). As the CA enters into the tissue from the blood, it changes the tissue’s T1 value and therefore the measured signal intensity. Most tumors have elevated angiogenesis and the neo-vasculature is generally quite permeable to the gadolinium-based small CA molecules. This makes DCE-MRI an attractive, noninvasive, functional imaging method for characterizing tumor microvasculature. There are commonly three approaches for analyzing the DCE-MRI signal intensity time-course data. Qualitative description of the curve pattern of the time-course (such as washout, plateau, and persistence) is often used in clinical practice. Semi-quantitative analysis of the time-course data (such as uptake slope, washout slope, and percent signal change) has been adopted by some commercial CAD (Computer Aided Diagnosis) systems. By fitting the DCE time-course data with a pharmacokinetic (PK) model, quantitative analysis allows extraction of quantitative parameters of tissue biology. The estimated quantitative PK parameters are usually variants of: Ktrans, a rate constant for passive CA transfer between plasma and interstitium - a measure of microvascular flow and/or permeability; ve, the extravascular and extracellular space volume fraction (the putative CA distribution volume fraction); and vp, the plasma volume fraction. kep, the CA intravasation rate constant, can be calculated as Ktrans/ve. Both semi-quantitative and quantitative DCE-MRI methods are increasingly used in research and early phase clinical trial settings to assessment cancer response to therapy. Though quantitative PK analysis has the advantage of directly quantifying tissue biological properties and the estimated parameters are in principle independent of data acquisition details, the precision and accuracy of the PK parameters can be affected by several factors in data acquisition and analysis. On the other hand, semi-quantitative analysis is simpler and more straightforward without the need for sophisticated mathematical modeling, and is readily implementable in clinical settings. However, semi-quantitative parameters are directly related to MRI signal change, not tissue biology, and the values are generally dependent on data acquisition details, which often vary from one institution to another in clinical practice, leading to high variability and low reproducibility.

Clinical Applications in Cancer Imaging

For cancer imaging, DCE-MRI can be used for screening/detection, diagnosis, staging, therapy response evaluation, and assessment of residual disease and recurrence. There has been substantial literature evidence over the last decade showing that quantitative DCE-MRI can improve accuracy in cancer diagnosis, and provide earlier and better evaluation of cancer response to therapy than the standard-of-care approach of measuring imaging tumor size. Texture analysis of DCE-MRI parameter spatial distribution has recently gained significant interest for its utility in characterizing tumor heterogeneity and assessing therapy response. Because MRI is significantly more expensive than other imaging modalities for cancer screening/detection, prediction and evaluation of cancer therapy response may be the most valuable application of DCE-MRI in cancer imaging, especially in the emerging era of personalized medicine.

Variability of Quantitative DCE-MRI Parameters

The accuracy and precision of quantitative PK parameters can be affected by several major factors in data acquisition and analysis, including temporal resolution, acquisition time, quantifications of arterial input function (AIF) and pre-CA native tissue T1, and selection of PK model and software package for data analysis. The variations in estimated PK parameters caused by variations of these factors are, however, often systematic. Therefore, the utility of quantitative DCE-MRI for certain cancer imaging applications may not be diminished despite errors in estimated PK parameter values. For example, a recent longitudinal breast DCE-MRI study shows that various PK models and software packages performed comparably well in early prediction of breast cancer response to neoadjuvant chemotherapy whether absolute or percent change of PK parameter values were used as predictors of response. Nonetheless, standardization of data acquisition protocol and central data analysis with a consistent method is probably the best practice for quantitative DCE-MRI in a multicenter setting.

Shutter-Speed Analysis of DCE-MRI Data

The Shutter-Speed PK model incudes an additional parameter, τi (mean intracellular water lifetime), to account for the effects of transcytolemmal water exchange in quantitative analysis of DCE-MRI data. Recent studies have shown that τi may be an imaging biomarker of cellular energetic metabolism. Therefore, improving data sensitivity to the water exchange effects (through acquisition parameter adjustment) and using the Shutter-Speed PK model for data analysis may add a new dimension to DCE-MRI of cancer: assessing tumor metabolic activity. τi has been shown to be an effective DCE-MRI parameter for prediction of tumor response to treatment.

Conclusion

DCE-MRI is a valuable noninvasive functional imaging method for tumor characterization. The important clinical applications include cancer detection and evaluation of therapeutic response. Variations in data acquisition and analysis can lead to variabilities in estimated DCE-MRI metrics. Therefore, standardization in acquisition and analysis is preferred in multicenter studies to improve reproducibility. Shutter-Speed analysis of DCE-MRI data potentially allows measurement of tumor metabolic activity.

Acknowledgements

NIH grant U01CA154602.

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Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)