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Volumes and growth rates of untreated adult low-grade gliomas indicate risk of early malignant transformation

Research output: Contribution to journalArticle

  • Jeremy Rees
  • Hilary Watt
  • H Rolf Jäger
  • Chris Benton
  • Daniel Tozer
  • Paul Tofts
  • Adam Waldman

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Original languageEnglish
Pages (from-to)54-64
Number of pages11
JournalEuropean Journal of Radiology
Volume72
Issue number1
DOIs
Publication statusPublished - Oct 2009

Abstract

Adult low-grade gliomas (LGG) grow slowly, but most eventually undergo malignant transformation. The relationship between tumour volume, growth rate and the likelihood of transformation is unknown. Twenty-seven patients with biopsy-proven, untreated LGG had at least three MRI studies at 6 monthly intervals. Tumour volumes and growth rates were calculated using semi-automated segmentation, and analysed in a hierarchical regression model. In a 3-year period, patients who showed clinical deterioration and/or new (or significantly increased) contrast enhancement were classified as transformers (T), whilst non-transformers (NT) remained stable clinically and by conventional radiological criteria. All LGG showed progressive growth. Volumes at study entry were smaller in 9NT (57 ml, 95% CI 35-80 ml) than in 18T (83 ml, 95% CI 70-96 ml) (p=0.03). Average annual growth rates were lower in NT (16% (95% CI 9-23%)) than in T (26% (95% CI 20-31%)) (p=0.046), until the penultimate study. Growth in T increased to 56% p.a. (95% CI 20-92%) in the 6 months prior to transformation. In T, tumour volume was the most significant predictor of transformation in the following 12 months. Sequential measurement of LGG volume allows accurate determination of growth rates and identification of patients whose tumours are at high risk of early transformation.

    Research areas

  • Adult, Aged, Algorithms, Brain Neoplasms, Cell Transformation, Neoplastic, Female, Glioma, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Imaging, Three-Dimensional, Magnetic Resonance Imaging, Male, Middle Aged, Reproducibility of Results, Risk Assessment, Risk Factors, Sensitivity and Specificity, Journal Article, Research Support, Non-U.S. Gov't

ID: 46188748