Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas

Pallud, Johan; Mandonnet, Emmanuel; Duffau, Hugues; Kujas, M.; Guillevin, Rémy; Galanaud, Damien; Taillandier, Luc; Laurent, Césari

doi:10.1002/ana.20946

Cited by 222 publications

(134 citation statements)

References 19 publications

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“…Specifically, our discovery that the model-predicted linear radial growth of the imagingdetectable portion of the lesion (at a velocity dependent on both r and D) was accurate in a population of WHO grade II gliomas followed serially without intervening treatment (Mandonnett et al, 2003) which led to confirmation that the velocity of growth predicts survival time in a similar population (Pallud et al, 2006). However, since higher grade gliomas (glioblastomas) are typically treated immediately, a similar population of serially imaged untreated glioblastomas has been difficult to obtain and only a few cases have been reported (Swanson and Alvord 2002;Harpold et al, 2007).…”

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confidence: 90%

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

2007

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The prediction of the outcome of individual patients with glioblastoma would be of great significance for monitoring responses to therapy. We hypothesise that, although a large number of genetic-metabolic abnormalities occur upstream, there are two 'final common pathways' dominating glioblastoma growth -net rates of proliferation (r) and dispersal (D). These rates can be estimated from features of pretreatment MR images and can be applied in a mathematical model to predict tumour growth, impact of extent of tumour resection and patient survival. Only the pre-operative gadolinium-enhanced T1-weighted (T1-Gd) and T2-weighted (T2) volume data from 70 patients with previously untreated glioblastoma were used to derive a ratio D/r for each patient. We developed a 'virtual control' for each patient with the same size tumour at the time of diagnosis, the same ratio of net invasion to proliferation (D/r) and the same extent of resection. The median durations of survival and the shapes of the survival curves of actual and 'virtual' patients subjected to biopsy or subtotal resection (STR) superimpose exactly. For those actually receiving gross total resection (GTR), as shown by post-operative CT, the actual survival curve lies between the 'virtual' results predicted for 100 and 125% resection of the T1-Gd volume. The concordance between predicted (virtual) and actual survivals suggests that the mathematical model is realistic enough to allow precise definition of the effectiveness of individualised treatments and their site(s) of action on proliferation (r) and/or dispersal (D) of the tumour cells without knowledge of any other clinical or pathological information.

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confidence: 90%

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

2007

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“…By allowing a motility coefficient to differ depending on the local tissue composition (so that glioma cells migrate more rapidly along white matter than in grey matter), the algorithm predicted sites of potential tumor recurrence to a degree beyond the limits of current medical imaging techniques. Interestingly, as supported by the results of this model, two independent factors, velocity of diametric expansion and initial tumor size at diagnosis, were indeed found to be statistically significant in a recent clinical survey on the prognostic evaluation of patients who harbor a grade II glioma [52]. Based on their previous studies [61,62,65], [63] also investigated the effects of chemotherapy on the spatio-temporal response of gliomas.…”

Section: Continuum Modelingmentioning

confidence: 53%

Computational modeling of brain tumors: discrete, continuum or hybrid?

Wang

Deisboeck

2008

Sci Model Simul

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In spite of all efforts, patients diagnosed with highly malignant brain tumors (gliomas), continue to face a grim prognosis. Achieving significant therapeutic advances will also require a more detailed quantitative understanding of the dynamic interactions among tumor cells, and between these cells and their biological microenvironment. Datadriven computational brain tumor models have the potential to provide experimental tumor biologists with such quantitative and cost-efficient tools to generate and test hypotheses on tumor progression, and to infer fundamental operating principles governing bidirectional signal propagation in multicellular cancer systems. This review highlights the modeling objectives of and challenges with developing such in silico brain tumor models by outlining two distinct computational approaches: discrete and continuum, each with representative examples. Future directions of this integrative computational neuro-oncology field, such as hybrid multiscale multiresolution modeling are discussed.

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“…Determining how WHO grade II gliomas will progress is a major issue in the therapeutic management of these tumors (19). It has been shown that the clinical outcome may be different between LGGs with similar characteristics based on both immunohistopathological examination and conventional MRI findings (3).…”

Section: Discussionmentioning

confidence: 99%

Proton MR spectroscopy in predicting the increase of perfusion MR imaging for WHO grade II gliomas

Guillevin¹,

Menuel²,

Abud³

et al. 2011

Magnetic Resonance Imaging

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Purpose: To investigate the correlation between the metabolite ratios obtained from proton magnetic resonance (MR) spectroscopy and those obtained from MR perfusion parameters (relative cerebral blood volume [rCBV]) in a cohort of low-grade glioma (LGG). Materials and Methods:Patients underwent prospectively conventional MR, proton magnetic resonance spectroscopy ( 1 HMRS), and perfusion-weighted images (PWI). Statistical analyses were performed to determine the correlative and independent predictive factors of rCBVmax and the metabolite ratio thresholds with optimum sensitivity and specificity.Results: Thirty-one patients were included in this study. Linear correlations were observed between the metabolic ratios (lactate [Lac]/creatine [Cr], choline [Cho]/N-acetylaspartate [NAA], free-lipids/Cr) and rCBVmax (P < 0.05). These metabolic ratios were determined to be independent predictive factors of rCBVmax (P ¼ 0.027, 0.011 and 0.032, respectively). According to the receiver operating characteristic curves, the cutoff values of the metabolic ratios to discriminate between the two populations of rCBVmax (<1.7 versus ¼ 1.7) were 1.72, 1.54, and 1.40, respectively, with a sensitivity ¼ 75% and a specificity >95% for Lac/Cr. Conclusion:This study demonstrated consistent correlations between the data from 1 HMRS and PWI. The Lac/Cr ratio predicts regional hemodynamic changes, which are themselves a useful biomarker of clinical prognosis in patients with LGG. As such, this ratio may provide a new parameter for making improved clinical decisions.

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Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas

Cited by 222 publications

References 19 publications

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

A mathematical modelling tool for predicting survival of individual patients following resection of glioblastoma: a proof of principle

Computational modeling of brain tumors: discrete, continuum or hybrid?

Proton MR spectroscopy in predicting the increase of perfusion MR imaging for WHO grade II gliomas

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