Volumetric analysis of functional diffusion maps is a predictive imaging biomarker for cytotoxic and anti-angiogenic treatments in malignant gliomas

Abstract

Anti-angiogenic agents targeting brain tumor neovasculature may increase progression-free survival in patients with recurrent malignant gliomas. However, when these patients do recur it is not always apparent as an increase in enhancing tumor volume on MRI, which has been the standard of practice for following patients with brain tumors. Therefore alternative methods are needed to evaluate patients treated with these novel therapies. Furthermore, a method that can also provide useful information for the evaluation of conventional therapies would provide an important advantage for general applicability. Diffusion-weighted magnetic resonance imaging (DWI) has the potential to serve as a valuable biomarker for these purposes. In the current study, we explore the prognostic ability of functional diffusion maps (fDMs), which examine voxel-wise NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript changes in the apparent diffusion coefficient (ADC) over time, applied to regions of fluidattenuated inversion recovery (FLAIR) abnormalities in patients with malignant glioma, treated with either anti-angiogenic or cytotoxic therapies. Results indicate that the rate of change in fDMs is an early predictor of tumor progression, time to progression and overall survival for both treatments, suggesting the application of fDMs in FLAIR abnormal regions may be a significant advance in brain tumor biomarker technology.

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

“…The remaining top 10 features contributing to IDH prediction were MRI features, and 8 among them were obtained by filtering tumor volumes using ADC thresholds rather than the whole tumor volumes. The utility of DWI in glioma prognostication has been demonstrated previously 20,26,43,44 ; we corroborated the utility of DWI to subselect volumes of interests with greater contribution to our model performance.…”

“…This difference has not been reported previously and could be explored on other datasets. While steroid treatment has been shown to affect the appearance of tumors by reducing contrast permeability and peritumoral edema, 20,37,38 including this feature in our model did not improve IDH prediction. The remaining top 10 features contributing to IDH prediction were MRI features, and 8 among them were obtained by filtering tumor volumes using ADC thresholds rather than the whole tumor volumes.…”

“…16 In recent years, machine-learning algorithms have been applied to imaging studies of gliomas to predict genotype and patient survival outcomes based on imaging features extracted from conventional MRI. [17][18][19][20] In this study, we retrospectively examined the preoperative MRI of 120 patients diagnosed with either primary grade III or IV glioma with known IDH genotype. We hypothesized that a model integrating multimodal MRI features using a machine-learning approach could accurately predict IDH genotype in HGGs.…”

Quantification of edema reduction using differential quantitative T2 (DQT2) relaxometry mapping in recurrent glioblastoma treated with bevacizumab

“…It is important to mention that many valuable biomarkers have shown the ability to predict patient survival in anti-angiogenic therapies, including [ 18 F]-fluorothymidine PET [36], dynamic contrast-enhanced (DCE)-MRI combined with blood serum markers [37], pre-treatment diffusion MR characteristics [38], conventional MR imaging characteristics [25], and functional diffusion maps (fDMs) using serial diffusion MR data [39,40]. These techniques, however, are distinctly different from the DQT2 mapping technique in that they quantify particular aspects of tumor growth, metabolism, or vasculature instead of changes in tissue T2 characteristics likely due to change in water homeostasis.…”

Differences In High-Intensity Signal Volume Between Arterial Spin Labeling And Contrast-Enhanced T1-Weighted Imaging May Be Useful For Differentiating Glioblastoma From Brain Metastasis

“…Various volumetric parameters derived from the high-intensity signals on traditional MRI sequences, such CE+ T1WI, FLAIR, and T2WI, are used for brain tumor evaluation, especially volumetric changes in contrast enhancement (16)(17)(18). It has been reported that three -dimensional (3D) volumetry maybe more accurate for defining tumor volume than estimates based on 2D axial slices (19).…”