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Improving Delineation of True Tumor Volume With Multimodal MRI in a Rat Model of Brain Metastasis
James R Larkin1, Manon A Simard1, Axel de Bernardi1
1Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
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 or accurately, allowing residual tumor to regrow. The aim of this study was to determine the extent to which multimodal magnetic resonance imaging (MRI), with a simple and automated analysis pipeline, could improve upon current clinical practice of single-modality, independent-observer tumor delineation.
Methods And Materials:
We used a single rat model of brain metastasis (ENU1564 breast carcinoma cells in BD-IX rats), with and without radiation therapy. Multimodal MRI data were acquired using sequences either in current clinical use or in clinical trial and included postgadolinium 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 ± 36 microL vs 131 ± 40 microL histologic measurement), although multimodal MRI more accurately delineated tumor volume (132 ± 41 microL). Multimodal MRI offered increased sensitivity compared with 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 radiation therapy. 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 visualization 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 tumor models or clinical cohorts.
Insights
Multimodal magnetic resonance imaging (MRI) improves brain metastasis margin delineation over single-modality MRI, potentially reducing relapse and increasing survival. Further validation is needed.
Area of Science:
- Oncology
- Radiology
- Medical Imaging
Background:
- Brain metastases are a leading cause of cancer-related death.
- Local relapse after treatment is common due to inaccurate tumor delineation.
- Current clinical practice relies on single-modality MRI for treatment planning.
Purpose of the Study:
- To evaluate if multimodal MRI can improve brain metastasis margin delineation compared to single-modality MRI.
- To assess an automated analysis pipeline for multimodal MRI data.
- To determine the potential impact on reducing local relapse rates.
Main Methods:
- A rat model of brain metastasis (ENU1564 breast carcinoma) was used, with and without radiation therapy.
- Multimodal MRI sequences were acquired, including T1-weighted, blood flow, blood volume, T1/T2 relaxation, and apparent diffusion coefficient maps.
- Data were analyzed using a simple, automated pipeline.
Main Results:
- Multimodal MRI (132 ± 41 microL) more accurately delineated tumor volume than single-modality MRI (85 ± 36 microL).
- Multimodal MRI showed higher sensitivity (0.82 vs. 0.61) for metastasis detection, with slightly lower specificity (0.86 vs. 0.98).
- Blood flow maps improved margin detection for late-stage metastases; T1-weighted images improved detection of smaller metastases.
Conclusions:
- Multimodal MRI significantly enhances brain metastasis margin visualization beyond current clinical standards.
- Improved delineation may decrease relapse rates and improve patient survival.
- Further validation in additional models and clinical cohorts is warranted.
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