Extent of tumor-brain interface: a new tool to predict evolution of malignant gliomas

Preoperative Two-Dimensional Size of Glioblastoma is Associated with Patient Survival

BACKGROUND

Although tumor size affects survival of patients with lower-grade glioma, a prognostic effect on patients with glioblastoma remains to be established.

METHODS

We performed a retrospective analysis of 61 patients using volumetric data of tumor compartments of 61 patients obtained by preoperative magnetic resonance images using the visual ABC/2 method. Preoperative enhancing, nonenhancing, necrosis, and edema volume, the preoperative tumor area (TA) as a product of the 2 largest tumor diameters perpendicular to each other on axial T1-weighted postcontrast images, as well as postoperative enhancing residual volumes, were measured. Multivariable Cox proportional hazard models were used to associate these parameters with overall survival, adjusting for potential confounders.

RESULTS

The median preoperative enhancing tumor volume was 18.2 mL (interquartile range, 8.2-41.7 mL); the median remnant tumor volume was 1.3% (interquartile range, 0.0%-42.9%). During follow-up, 59 patients (92%) died; median survival time and median follow-up time were both 404 days. We found a statistically significant multiplicative effect of TA on survival: the hazard ratio (HR) was increased by 1.096 per unit increase of 200 mm² (95% confidence interval [CI], 1.027-1.170; P < 0.01). The effect of remnant tumor on HR increased multiplicatively by 1.013 (95% CI, 1.001-1.026; P = 0.04) per unit increase of 1 log (day) and 1% in tumor remnant. HR associated with age at surgery increased by 1.503 per 5 years of age (95% CI, 1.243-1.817; P < 0.01).

CONCLUSIONS

Preoperative TA proved to be the only glioblastoma size parameter that affects patient survival.

Analysis of heterogeneity in T2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

PURPOSE

To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in T2-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs).

METHODS

Using a retrospective patient cohort (n = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort.

RESULTS

The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the T2-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression.

CONCLUSION

Analysis of heterogeneity, in T2-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.

Tumour progression or pseudoprogression? A review of post-treatment radiological appearances of glioblastoma

Glioblastoma (GBM) is a common brain tumour in adults, which, despite multimodality treatment, has a poor median survival. Efficacy of therapy is assessed by clinical examination and magnetic resonance imaging (MRI) features. There is now a recognised subset of treated patients with imaging features that indicate "progressive disease" according to Macdonald's criteria, but subsequently, show stabilisation or resolution without a change in treatment. In these cases of "pseudoprogression", it is believed that non-tumoural causes lead to increased contrast enhancement and conventional MRI is inadequate in distinguishing this from true tumour progression. Incorrect diagnosis is important, as failure to identify pseudoprogression could lead to an inappropriate change of effective therapy. The purpose of this review is to outline the current research into radiological assessment with MRI and molecular imaging of post-treatment GBMs, specifically the differentiation between pseudoprogression and tumour progression.

The role of imaging in the management of progressive glioblastoma : a systematic review and evidence-based clinical practice guideline

QUESTION

Which imaging techniques most accurately differentiate true tumor progression from pseudo-progression or treatment related changes in patients with previously diagnosed glioblastoma?

TARGET POPULATION

These recommendations apply to adults with previously diagnosed glioblastoma who are suspected of experiencing progression of the neoplastic process.

RECOMMENDATIONS LEVEL II

Magnetic resonance imaging with and without gadolinium enhancement is recommended as the imaging surveillance method to detect the progression of previously diagnosed glioblastoma.

LEVEL II

Magnetic resonance spectroscopy is recommended as a diagnostic method to differentiate true tumor progression from treatment-related imaging changes or pseudo-progression in patients with suspected progressive glioblastoma.

LEVEL III

The routine use of positron emission tomography to identify progression of glioblastoma is not recommended.

LEVEL III

Single-photon emission computed tomography imaging is recommended as a diagnostic method to differentiate true tumor progression from treatment-related imaging changes or pseudo-progression in patients with suspected progressive glioblastoma.