Improving delineation of true tumour volume with multimodal MRI in a rat model of brain metastasis

Purpose: Brain metastases are almost universally lethal with short median survival times. Despite this they are often potentially curable, with therapy failing only because of local relapse. One key reason relapse occurs is because treatment planning did not delineate metastasis margins sufficiently accurately, allowing residual tumour to regrow. The aim of this study was to determine the extent to which multimodal MRI, with a simple and automated analysis pipeline, could improve upon current clinical practice of single modality independent observer tumour delineation. Methods and Materials: We used a single rat model of brain metastasis (ENU1564 breast carcinoma cells in BD-IX rats), with and without radiotherapy. Multimodal MRI data were acquired using sequences either in current clinical use or in clinical trial, and included post gadolinium T1-weighted images and maps of blood flow, blood volume, T1 and T2 relaxation times, and apparent diffusion coefficient. Results: In all cases, independent observers underestimated the true size of metastases from single modality gadolinium-enhanced MRI (85±36µL vs. 131±40µL histological measurement), whilst multimodal MRI more accurately delineated tumour volume (132±41µL). Multimodal MRI offered increased sensitivity compared to independent observer for detecting metastasis (0.82 vs. 0.61, respectively), with only a slight decrease in specificity (0.86 vs. 0.98). Blood flow maps conferred the greatest improvements in margin detection for late-stage metastases after radiotherapy. Gadolinium-enhanced T1 weighted images conferred the greatest increase in accuracy of detection for smaller metastases. Conclusions: These findings suggest that multimodal MRI of brain metastases could significantly improve the visualisation of brain metastasis margins, beyond current clinical practice, with the potential to decrease relapse rates and increase patient survival. This finding now needs validation in additional tumour models or clinical cohorts.