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Improving Delineation of True Tumor Volume With Multimodal MRI in a Rat Model of Brain Metastasis.
Larkin, James R; Simard, Manon A; de Bernardi, Axel; Johanssen, Vanessa A; Perez-Balderas, Francisco; Sibson, Nicola R.
Afiliação
  • Larkin JR; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
  • Simard MA; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
  • de Bernardi A; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
  • Johanssen VA; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
  • Perez-Balderas F; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford.
  • Sibson NR; Department of Oncology, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, University of Oxford. Electronic address: nicola.sibson@oncology.ox.ac.uk.
Int J Radiat Oncol Biol Phys ; 106(5): 1028-1038, 2020 04 01.
Article em En | MEDLINE | ID: mdl-31959544
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 µL vs 131 ± 40 µL histologic measurement), although multimodal MRI more accurately delineated tumor volume (132 ± 41 µL). 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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Imageamento por Ressonância Magnética / Carga Tumoral / Imagem Multimodal Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: Int J Radiat Oncol Biol Phys Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Neoplasias Encefálicas / Imageamento por Ressonância Magnética / Carga Tumoral / Imagem Multimodal Tipo de estudo: Prognostic_studies Limite: Animals Idioma: En Revista: Int J Radiat Oncol Biol Phys Ano de publicação: 2020 Tipo de documento: Article