Apparent diffusion coefficient and tumor volume measurements help stratify progression-free survival of bevacizumab-treated patients with recurrent glioblastoma multiforme

Abstract

Background

The aim of this study was to determine whether apparent diffusion coefficient (ADC) bi-component curve-fitting histogram analysis and volume percentage change (VPC) prior to bevacizumab treatment can stratify progression-free survival (PFS) and overall survival (OS) in patients with glioblastoma multiforme (GBM) on first recurrence.

Methods

We retrospectively evaluated 17 patients with recurrent GBM who received bevacizumab and fotemustine (n = 13) or only bevacizumab (n = 4) on first recurrence at our institution between December 2009 and July 2015. Both T2/FLAIR abnormalities and enhancing tumor on T1 images were mapped to the ADC images. ADC-L and ADC-M values were obtained trough bi-Gaussian curve fitting histogram analysis. Furthermore, the study population was dichotomized into two subgroups: patients displaying a reduction in enhancing tumor volume of either >55% or <55% between the mean value calculated at baseline and first follow-up. Subsequently, a second dichotomization was performed according to a reduction in the T2 / FLAIR volume >41% or <41% at first check after treatment. OS and PFS were assessed using volume parameters in a Cox regression model adjusted for significant clinical parameters.

Results

In univariate analysis, contrast-enhanced (CE)-ADC-L was significantly predictive of PFS (p = 0.01) and OS (p = 0.03). When we dichotomized our sample using the 55% cut-off for enhancing tumor volume, CE-VPC was able to predict PFS (p = 0.01) but not OS (p = 0.08). In multivariate analysis, only the CE-ADC-L was predictive of PFS (p = 0.01), albeit not predictive of OS (p = 0.14). CE-ADC-M, T2/FLAIR-ADC-L, T2/FLAIR-ADC, and T2/FLAIR VPC were not significantly predictive of PFS and OS (p > 0.05) in both univariate and multivariate analysis.

Conclusions

CE-ADC and CE-VPC can stratify PFS for patients with recurrent glioblastoma prior to bevacizumab treatment.

Keywords

Apparent diffusion coefficient bevacizumab MRI neuroradiology

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References

Central Brain Tumor Registry of the United States. 2007–2008 Primary brain tumors in the United States. Statistical report: 2000–2004 data, www.cbtrus.org/reports/2007-2008/2007report.pdf (2008, accessed 3 May 2019).

Wong

Hess

Gleason

et al.

Outcomes and prognostic factors in recurrent glioma patients enrolled onto phase II clinical trials. J Clin Oncol 1999; 17: 2572–2578.

Kreisl

Kim

Moore

et al.

Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. J Clin Oncol 2009; 27: 740–745.

Norden

Drappatz

Muzikansky

et al.

An exploratory survival analysis of anti-angiogenic therapy for recurrent malignant glioma. J Neurooncol 2009; 92: 149–155.

Andre

Spearman

et al.

Peritumoral apparent diffusion coefficient as a metric of response in patients with recurrent glioblastoma multiforme treated with bevacizumab and irinotecan. Neuroradiol J 2009; 3: 350–361.

Roy

Lahiri

Maji

et al.

Recurrent glioblastoma: where we stand. South Asian J Cancer 2015; 4: 163–173.

König

Fuchs

Hansen

et al.

What is precision medicine?

Eur Respir J 2017; 50: 1700391.

Huang

Held-Feindt

Buhl

et al.

Expression of VEGF and its receptors in different brain tumors. Neurol Res 2005; 27: 371–377.

Hicklin

Ellis

. Role of the vascular endothelial growth factor pathway in tumor growth and angiogenesis. J Clin Oncol 2005; 23: 1011–1027.

10.

Friedman

Prados

Wen

et al.

Bevacizumab alone and in combination with irinotecan in recurrent glioblastoma. J Clin Oncol 2009; 27: 4733–4740.

11.

Kreisl

Kim

Moore

et al.

Phase II trial of single-agent bevacizumab followed by bevacizumab plus irinotecan at tumor progression in recurrent glioblastoma. J Clin Oncol 2009; 27: 740–745.

12.

Nghiemphu

Liu

Lee

et al.

Bevacizumab and chemotherapy for recurrent glioblastoma: a single institution experience. Neurology 2009; 72: 1217–1222.

13.

Lai

Tran

Nghiemphu

et al.

Phase II study of bevacizumab plus temozolomide during and after radiation therapy for patients with newly diagnosed glioblastoma multiforme. J Clin Oncol 2011; 29: 142–148.

14.

Clarke

Molinaro

Phillips

et al.

A single-institution phase II trial of radiation, temozolomide, erlotinib, and bevacizumab for initial treatment of glioblastoma. Neuro Oncol 2014; 16: 984–990.

15.

Gilbert

Dignam

Armstrong

et al.

A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med 2014; 370: 699–708.

16.

Chinot

Wick

Mason

et al.

Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 2014; 370: 709–722.

17.

Pizzini

Beltramello

Piovan

et al.

Diffusion-weighted and diffusion tensor magnetic resonance brain imaging: principles and applications. Riv Neuroradiol 2003; 16: 207–220.

18.

Patterson

Padhani

Collins

. Technology insight: water diffusion MRI—a potential new biomarker of response to cancer therapy. Nat Clin Pract Oncol 2008; 5: 220–233.

19.

Thoeny

De Keyzer

Vandecaveye

et al.

Effect of vascular targeting agent in rat tumor model: dynamic contrast-enhanced versus diffusion-weighted MR imaging. Radiology 2005; 237: 492–499.

20.

Hamstra

Rehemtulla

Ross

. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol 2007; 25: 4104–4109.

21.

Paldino

Barboriak

Desjardins

et al.

Repeatability of quantitative parameters derived from diffusion tensor imaging in patients with glioblastoma multiforme. J Magn Reson Imaging 2009; 29: 1199–1205.

22.

Pope

Kim

Huo

et al.

Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009; 252: 182–189.

23.

Jain

Scarpace

Ellika

et al.

Imaging response criteria for recurrent gliomas treated with bevacizumab: role of diffusion weighted imaging as an imaging biomarker. J Neurooncol 2009; 96: 423–431.

24.

Tokgoz

Celik

Gultekin

et al.

Is there a role for apparent diffusion coefficients in the differential diagnosis of brain tumors?

Neuroradiol J 2006; 19: 322–329.

25.

Pope

Lai

Mehta

et al.

Apparent diffusion coefficient histogram analysis stratifies progression-free survival in newly diagnosed bevacizumab-treated glioblastoma. Am J Neuroradiol 2011; 32: 882–889.

26.

Pope

Qiao

Kim

et al.

Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol 2012; 108: 491–498.

27.

Ellingson

Malkin

Rand

et al.

Volumetric analysis of functional diffusion maps is a predictive imaging biomarker for cytotoxic and anti-angiogenic treatments in malignant gliomas. J Neurooncol 2011; 102: 95–103.

28.

Ellingson

Cloughesy

Lai

et al.

Graded functional diffusion map-defined characteristics of apparent diffusion coefficients predict overall survival in recurrent glioblastoma treated with bevacizumab. Neuro Oncol 2011; 13: 1151–1161.

29.

Crawford

Khayal

McGue

et al.

Relationship of pre-surgery metabolic and physiological MR imaging parameters to survival for patients with untreated GBM. J Neurooncol 2009; 91: 337–351.

30.

Huang

Rahman

Hamdan

et al.

Recurrent glioblastoma volumetric assessment and stratification of patient survival with early posttreatment magnetic resonance imaging in patients treated with bevacizumab. Cancer 2013; 119: 3479–3488.

31.

Wen

Macdonald

Reardon

et al.

Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010; 28: 1963–1972.

32.

Pope

Young

Ellingson

. Advances in MRI assessment of gliomas and response to anti-VEGF therapy. Curr Neurol Neurosci Rep 2011; 11: 336–344.

33.

Karnofsky

The clinical evaluation of chemotherapeutic agents in cancer. In: MacLeod

(ed). Evaluation of chemotherapeutic agents, New York: Columbia University Press, 1949, pp. 195–205.

34.

Quant

Wen

. Response assessment in neuro-oncology. Curr Oncol Rep 2011; 13: 50–56.

35.

Sawlani

Taylor

Rowley

et al.

Magnetic resonance spectroscopy for differentiating pseudo-progression from true progression in GBM on concurrent chemoradiotherapy. Neuroradiol J 2012; 25: 575–586.

36.

Ellingson

Sahebjam

Kim

et al.

Pretreatment ADC histogram analysis is a predictive imaging biomarker for bevacizumab treatment but not chemotherapy in recurrent glioblastoma. Am J Neuroradiol 2014; 35: 673–679.

37.

Leu

Pope

Cloughesy

et al.

Imaging biomarkers for anti-angiogenic therapy in malignant gliomas. CNS Oncology 2013; 2: 33–47.

38.

Rahman

Hamdan

Zweifler

et al.

Histogram analysis of apparent diffusion coefficient within enhancing and non-enhancing tumor volumes in recurrent glioblastoma patients treated with bevacizumab. J Neurooncol 2014; 119: 149–158.

39.

Hattingen

Jurcoane

Daneshvar

et al.

Quantitative T2 mapping of recurrent glioblastoma under bevacizumab improves monitoring for non-enhancing tumor progression and predicts overall survival. Neuro Oncol 2013; 15: 1395–1404.

40.

Ellingson

Cloughesy

Lai

et al.

Quantification of edema reduction using differential quantitative T2 (DQT2) relaxometry mapping in recurrent glioblastoma treated with bevacizumab. J Neurooncol 2012; 106: 111–119.