Survival analysis for apparent diffusion coefficient measures in children with embryonal brain tumours

Magnetic resonance imaging based functional imaging in paediatric oncology

Imaging is central to management of solid tumours in children. Conventional magnetic resonance imaging (MRI) is the standard imaging modality for tumours of the central nervous system (CNS) and limbs and is increasingly used in the abdomen. It provides excellent structural detail, but imparts limited information about tumour type, aggressiveness, metastatic potential or early treatment response. MRI based functional imaging techniques, such as magnetic resonance spectroscopy, diffusion and perfusion weighted imaging, probe tissue properties to provide clinically important information about metabolites, structure and blood flow. This review describes the role of and evidence behind these functional imaging techniques in paediatric oncology and implications for integrating them into routine clinical practice.

A Bayesian spatial random effects model characterisation of tumour heterogeneity implemented using Markov chain Monte Carlo (MCMC) simulation

The focus of this study is the development of a statistical modelling procedure for characterising intra-tumour heterogeneity, motivated by recent clinical literature indicating that a variety of tumours exhibit a considerable degree of genetic spatial variability. A formal spatial statistical model has been developed and used to characterise the structural heterogeneity of a number of supratentorial primitive neuroectodermal tumours (PNETs), based on diffusion-weighted magnetic resonance imaging. Particular attention is paid to the spatial dependence of diffusion close to the tumour boundary, in order to determine whether the data provide statistical evidence to support the proposition that water diffusivity in the boundary region of some tumours exhibits a deterministic dependence on distance from the boundary, in excess of an underlying random 2D spatial heterogeneity in diffusion. Tumour spatial heterogeneity measures were derived from the diffusion parameter estimates obtained using a Bayesian spatial random effects model. The analyses were implemented using Markov chain Monte Carlo (MCMC) simulation. Posterior predictive simulation was used to assess the adequacy of the statistical model. The main observations are that the previously reported relationship between diffusion and boundary proximity remains observable and achieves statistical significance after adjusting for an underlying random 2D spatial heterogeneity in the diffusion model parameters. A comparison of the magnitude of the boundary-distance effect with the underlying random 2D boundary heterogeneity suggests that both are important sources of variation in the vicinity of the boundary. No consistent pattern emerges from a comparison of the boundary and core spatial heterogeneity, with no indication of a consistently greater level of heterogeneity in one region compared with the other. The results raise the possibility that DWI might provide a surrogate marker of intra-tumour genetic regional heterogeneity, which would provide a powerful tool with applications in both patient management and in cancer research.

Pre-Adult MRI of Brain Cancer and Neurological Injury: Multivariate Analyses

Brain cancer and neurological injuries, such as stroke, are life-threatening conditions for which further research is needed to overcome the many challenges associated with providing optimal patient care. Multivariate analysis (MVA) is a class of pattern recognition technique involving the processing of data that contains multiple measurements per sample. MVA can be used to address a wide variety of neuroimaging challenges, including identifying variables associated with patient outcomes; understanding an injury's etiology, development, and progression; creating diagnostic tests; assisting in treatment monitoring; and more. Compared to adults, imaging of the developing brain has attracted less attention from MVA researchers, however, remarkable MVA growth has occurred in recent years. This paper presents the results of a systematic review of the literature focusing on MVA technologies applied to brain injury and cancer in neurological fetal, neonatal, and pediatric magnetic resonance imaging (MRI). With a wide variety of MRI modalities providing physiologically meaningful biomarkers and new biomarker measurements constantly under development, MVA techniques hold enormous potential toward combining available measurements toward improving basic research and the creation of technologies that contribute to improving patient care.

Magnetic resonance diffusion metrics indexing high focal cellularity and sharp transition at the tumour boundary predict poor outcome in glioblastoma multiforme

AIM

To investigate the prognostic power of intra-tumoural and gradient magnetic resonance imaging (MRI) diffusion metrics in patients with glioblastoma multiforme (GBM).

MATERIALS AND METHODS

Forty-six consecutive patients with histologically confirmed GBM who had undergone preoperative diffusion tensor imaging at 3 T were included. Mean diffusivity (MD) and MD gradient maps were computed. Regions of interest were analysed to determine the minimum MD within the enhancing tumour (minMD). MD gradients were calculated along the enhancing tumour boundary and subjected to histogram analysis. Overall survival (OS) and time to progression (TTP) were derived and survival analysis was undertaken.

RESULTS

There were 31 deaths and 37 patients progressed during the study period. Multivariate survival analysis, controlling for treatment and gender, showed that minMD values<6.1×10(-4) mm(2)/s predicted shorter OS (hazard ratio [HR]=2.82, 1.25-6.34; p=0.012) and TTP (HR=5.43, 1.96-15.05; p=0.001). Higher MD gradient values of the tumour boundary predicted shorter survival: MD gradient values >4.7×10(-5) mm(2)/s (10(th) centile) had a significantly shorter OS with a HR of 0.43 (0.19-0.96; p=0.04). Similarly, a value above 1.4×10(-4) mm(2)/s (75(th) centile) was a significant predictor for shorter OS (HR=0.39, 0.17-0.89; p=0.03).

CONCLUSIONS

Lower minMD and higher MD gradient values for the 10(th) and 75(th) percentile of the tumour boundary demonstrated prognostic value in preoperative GBM. This suggests that MRI diffusion metrics indicative of higher focal cellularity and steeper transition from high cellular tumour edge to low cellular oedema define more aggressive glioblastoma subtypes with a poorer prognosis.

Challenges for the functional diffusion map in pediatric brain tumors

Background

The functional diffusion map (fDM) has been suggested as a tool for early detection of tumor treatment efficacy. We aim to study 3 factors that could act as potential confounders in the fDM: areas of necrosis, tumor grade, and change in tumor size.

Methods

Thirty-four pediatric patients with brain tumors were enrolled in a retrospective study, approved by the local ethics committee, to examine the fDM. Tumors were selected to encompass a range of types and grades. A qualitative analysis was carried out to compare how fDM findings may be affected by each of the 3 confounders by comparing fDM findings to clinical image reports.

Results

Results show that the fDM in areas of necrosis do not discriminate between treatment response and tumor progression. Furthermore, tumor grade alters the behavior of the fDM: a decrease in apparent diffusion coefficient (ADC) is a sign of tumor progression in high-grade tumors and treatment response in low-grade tumors. Our results also suggest using only tumor area overlap between the 2 time points analyzed for the fDM in tumors of varying size.

Conclusions

Interpretation of fDM results needs to take into account the underlying biology of both tumor and healthy tissue. Careful interpretation of the results is required with due consideration to areas of necrosis, tumor grade, and change in tumor size.