4.6 Article Proceedings Paper

Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme

期刊

NEURO-ONCOLOGY
卷 13, 期 5, 页码 546-557

出版社

OXFORD UNIV PRESS INC
DOI: 10.1093/neuonc/noq194

关键词

magnetic resonance imaging; glioblastoma multiforme; survival

资金

  1. NCI NIH HHS [R01 CA127612, P50 CA97257, R01 CA127612-04] Funding Source: Medline

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The objective of this study was to test the predictive value of serial MRI data in relation to clinical outcome for patients with glioblastoma multiforme (GBM). Sixty-four patients with newly diagnosed GBM underwent conventional MM and diffusion-weighted and perfusion-weighted imaging postsurgery and prior to radiation/chemotherapy (pre-RT), immediately after RT (post-RT), and every 1-2 months thereafter until tumor progression, up to a maximum of 1 year. Tumor volumes and perfusion and diffusion parameters were calculated and subject to time-independent and time-dependent Cox proportional hazards models that were adjusted for age and MR scanner field strength. Larger volumes of the T2 hyperintensity lesion (T2ALL) and nonenhancing lesion (NEL) at pre-RT, as well as increased anatomic volumes at post-RT, were associated with worse overall survival (OS). Higher normalized cerebral blood volumes (nCBVs), normalized peak height (nPH) and normalized recirculation factors (nRF) at pre-RT, and nCBV at post-RT, in the T2ALL and NEL, were associated with shorter progression-free survival (PFS). From pre- to post-RT, there was a reduction in nCBV and nPH and an increase in apparent diffusion coefficient (ADC). Patients with lower nRF values at pre-RT, or a larger increase in nRF from pre-RT to post-RT, had significantly longer PFS. Time-dependent analysis showed that patterns of changes in ADC and anatomic volumes were associated with OS, while changes in nCBV, nPH, and the contrast-enhancing volume were associated with PFS. Our studies suggest that quantitative MRI variables derived from anatomic and physiological MRI provide useful information for predicting outcome in patients with GBM.

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