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Diffusion MRI outlined viable tumour volume beats GTV in intra-treatment stratification of outcome

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BACKGROUND AND PURPOSE: In radiotherapy, treatment response is generally evaluated many weeks after end of the treatment course. If the treatment outcome could be predicted during radiotherapy better tumour control could be achieved through timely adaptation of the treatment strategy. In this study intra-treatment change based on the diffusion MRI outlined viable tumour volume (VTV) was assessed and compared to the standard GTV to study their outcome prediction capacity.

MATERIALS AND METHODS: Thirty-eight brain metastases from twenty-one cancer patients were analysed in this prospective trial. Diffusion and structural MRI was acquired on a 1 T machine before, during, and at follow-up 2-3 months after radiotherapy. The VTV was defined as a region with high cellularity using high b-value diffusion MRI scans. Further, the diffusivity of the VTV was derived as the apparent diffusion coefficient (ADC). Treatment outcome was determined using RECIST defined bounds in the T1W MRI follow-up scan. Longitudinal statistical analysis was performed using a linear mixed effect model.

RESULTS: The GTV declined in both responding and non-responding (significantly) tumours with inseparable rates during radiotherapy. The VTV volume fraction reduced significantly in the responding tumours only. The ADC of the VTV increased significantly in responding metastases whereas it decreased in non-responding metastases. Furthermore, no association between baseline tumour size or primary disease and outcome was observed.

CONCLUSION: GTV size change during radiotherapy is not a reliable predictor of outcome in brain metastases. On the other hand, change in the volume fraction of VTV and diffusivity of VTV shows ability to stratify treatment outcome.

TidsskriftRadiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Sider (fra-til)121-126
Antal sider6
StatusUdgivet - mar. 2020

Bibliografisk note

Copyright © 2019 Elsevier B.V. All rights reserved.

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