Current and future trends in magnetic resonance imaging assessments of the response of breast tumors to neoadjuvant chemotherapy

Abstract

The current state-of-the-art assessment of treatment response in breast cancer is based on the response evaluation criteria in solid tumors (RECIST). RECIST reports on changes in gross morphology and divides response into one of four categories. In this paper we highlight how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) may be able to offer earlier, and more precise, information on treatment response in the neoadjuvant setting than RECIST. We then describe how longitudinal registration of breast images and the incorporation of intelligent bioinformatics approaches with imaging data have the potential to increase the sensitivity of assessing treatment response. We conclude with a discussion of the potential benefits of breast MRI at the higher field strength of 3T. For each of these areas, we provide a review, illustrative examples from clinical trials, and offer insights into future research directions.

Figure 1

(a) and (b) display pre-contrast and postcontrast T ₁-weighted sagittal images of a breast tumor. By considering the time course from each voxel, a parametric map can be generated that reports on, for example, the volume transfer constant (K ^trans) as displayed in (c).

Figure 2

ADC values are reduced in breast cancer tumors compared to the unaffected tissue. Shown here is a postcontrast T ₁-weighted image (a) for reference and an overlay of the corresponding ADC map (b). The tumor, located above and between the biopsy clips (white arrows), exhibits increased signal in the T ₁-weighted image and decreased ADC values, compared to the surrounding tissue.

Figure 3

The T ₁-weighted MR image before neoadjuvant chemotherapy (a) is registered to the image posttreatment (d) using the constrained ABA algorithm and the original ABA algorithm, respectively. The registered image using the constrained algorithm (b) shows that the tumor is preserved successfully, while the original ABA algorithm compresses the tumor substantially (c).

Figure 4

Biomedical informatics system architecture to support cancer treatment response assessment. Integrated systems are needed to support end-to-end management of response assessment data including data acquisition, analysis, and visualization. Data acquisition includes systems to acquire and store clinical images for research as well as systems to create image annotation data whether manual image annotation or automated image processing algorithms. Response calculation methods also need to be integrated with structured image annotation databases to perform data analysis to generate response interpretations. The images, image annotations, and response interpretations must be integrated with Clinical Trial Management Systems (CTMS) to enable visualization of response interpretations for treatment decision-making and auditing of clinical trial image response data.