Literature DB >> 17276619

Predicting outcome of patients with high-grade gliomas after radiotherapy using quantitative analysis of T1-weighted magnetic resonance imaging.

Christina Tsien1, Diana Gomez-Hassan, Thomas L Chenevert, Julia Lee, Theodore Lawrence, Randall K Ten Haken, Larry R Junck, Brian Ross, Yue Cao.   

Abstract

PURPOSE: The aim of this study was to test the hypothesis that measuring quantitative changes in signal intensity early after radiotherapy (RT) in the contrast-enhancing tumor rim and nonenhancing core may be a noninvasive marker of early treatment response in patients with high-grade gliomas. METHODS AND MATERIALS: Twenty patients with high-grade gliomas had magnetic resonance imaging (MRI) performed 1 week before RT, during Weeks 1 and 3 of RT, and every 1 to 3 months after RT as part of a clinical prospective study. Regions of interest (ROI) including contrast-enhancing rim, and the nonenhancing core were defined automatically based on a calculated image of post- to precontrast T1-weighted MRI. Pretreatment T1-weighted MRI signal intensity changes were compared with Weeks 1 and 3 RT and 1 and 3 months post-RT MRI. Clinical and MRI parameters were then tested for prediction of overall survival.
RESULTS: Regional T1-weighted signal intensity changes in both the contrast-enhancing rim and the nonenhancing core were observed in all patients during Week 1 and Week 3 of RT. Imaging parameters including signal intensity change within the nonenhancing core after Weeks 1 to 2 RT (p = 0.004), Weeks 3 to 4 RT (p = 0.002) and 1 month after completion of RT (p = 0.002) were predictive of overall survival. Using multivariate analysis including RTOG recursive partitioning analysis (RPA) and signal intensity change, only the signal intensity change in the nonenhancing core at 1 month after RT (p = 0.01) retained significance.
CONCLUSION: Quantitative measurements of T1-weighted MRI signal intensity changes in the nonenhancing tumor core (using ratios of pre-post values) may provide valuable information regarding early response during treatment and improve our ability to predict posttreatment outcome.

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Year:  2007        PMID: 17276619     DOI: 10.1016/j.ijrobp.2006.11.020

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN: 0360-3016            Impact factor:   7.038


  1 in total

1.  Predicting the efficacy of radiotherapy in individual glioblastoma patients in vivo: a mathematical modeling approach.

Authors:  R Rockne; J K Rockhill; M Mrugala; A M Spence; I Kalet; K Hendrickson; A Lai; T Cloughesy; E C Alvord; K R Swanson
Journal:  Phys Med Biol       Date:  2010-05-18       Impact factor: 3.609

  1 in total

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