Functional diffusion map of malignant brain tumors: A surrogate imaging biomarker for early prediction of therapeutic response and patient survival

Predictive value of diffusion MRI-based parametric response mapping for prognosis and treatment response in glioblastoma

BACKGROUND

Early detection of treatment response is important for the management of patients with malignant brain tumors such as glioblastoma to assure good quality of life in relation to therapeutic efficacy.

AIM

To investigate whether parametric response mapping (PRM) with diffusion MRI may provide prognostic information at an early stage of standard therapy for glioblastoma.

MATERIALS AND METHODS

This prospective study included 31 patients newly diagnosed with glioblastoma WHO grade IV, planned for primary standard postoperative treatment with radiotherapy 60Gy/30 fractions with concomitant and adjuvant Temozolomide. MRI follow-up including diffusion and perfusion weighting was performed at 3 T at start of postoperative chemoradiotherapy, three weeks into treatment, and then regularly until twelve months postoperatively. Regional mean diffusivity (MD) changes were analyzed voxel-wise using the PRM method (MD-PRM). At eight and twelve months postoperatively, after completion of standard treatment, patients were classified using conventional MRI and clinical evaluation as either having stable disease (SD, including partial response) or progressive disease (PD). It was assessed whether MD-PRM differed between patients having SD versus PD and whether it predicted the risk of disease progression (progression-free survival, PFS) or death (overall survival, OS). A subgroup analysis was performed that compared MD-PRM between SD and PD in patients only undergoing diagnostic biopsy. MGMT-promotor methylation status (O6-methylguanine-DNA methyltransferase) was registered and analyzed with respect to PFS, OS and MD-PRM.

RESULTS

Of the 31 patients analyzed: 21 were operated by resection and ten by diagnostic biopsy. At eight months, 19 patients had SD and twelve had PD. At twelve months, ten patients had SD and 20 had PD, out of which ten were deceased within twelve months and one was deceased without known tumor progression. Median PFS was nine months, and median OS was 17 months. Eleven patients had methylated MGMT-promotor, 16 were MGMT unmethylated, and four had unknown MGMT-status. MD-PRM did not significantly predict patients having SD versus PD neither at eight nor at twelve months. Patients with an above median MD-PRM reduction had a slightly longer PFS (P = 0.015) in Kaplan-Maier analysis, as well as a non-significantly longer OS (P = 0.099). In the subgroup of patients only undergoing biopsy, total MD-PRM change at three weeks was generally higher for patients with SD than for patients with PD at eight months, although no tests were performed. MGMT status strongly predicted both PFS and OS but not MD-PRM change.

CONCLUSION

MD-PRM at three weeks was not demonstrated to be predictive of treatment response, disease progression, or survival. Preliminary results suggested a higher predictive value in non-resected patients, although this needs to be evaluated in future studies.

Differentiation of pseudoprogression and real progression in glioblastoma using ADC parametric response maps

Purpose

The purpose of this study was to investigate whether a voxel-wise analysis of apparent diffusion coefficient (ADC) values may differentiate between progressive disease (PD) and pseudoprogression (PsP) in patients with high-grade glioma using the parametric response map, a newly introduced postprocessing tool.

Methods

Twenty-eight patients with proven PD and seven patients with PsP were identified in this retrospective feasibility study. For all patients ADC baseline and follow-up maps on four subsequent MRIs were available. ADC maps were coregistered on contrast enhanced T1-weighted follow-up images. Subsequently, enhancement in the follow-up contrast enhanced T1-weighted image was manually delineated and a reference region of interest (ROI) was drawn in the contralateral white matter. Both ROIs were transferred to the ADC images. Relative ADC (rADC) (baseline)/reference ROI values and rADC (follow up)/reference ROI values were calculated for each voxel within the ROI. The corresponding voxels of rADC (follow up) and rADC (baseline) were subtracted and the percentage of all voxels within the ROI that exceeded the threshold of 0.25 was quantified.

Results

rADC voxels showed a decrease of 59.2% (1^st quartile (Q1) 36.7; 3^rd quartile (Q3) 78.6) above 0.25 in patients with PD and 18.6% (Q1 3.04; Q3 26.5) in patients with PsP (p = 0.005). Receiver operating characteristic curve analysis showed the optimal decreasing rADC cut-off value for identifying PD of > 27.05% (area under the curve 0.844±0.065, sensitivity 0.86, specificity 0.86, p = 0.014).

Conclusion

This feasibility study shows that the assessment of rADC using parametric response maps might be a promising approach to contribute to the differentiation between PD and PsP. Further research in larger patient cohorts is necessary to finally determine its clinical utility.

Functional diffusion map: A biomarker of brain metastases response to treatment based on magnetic resonance image analysis

Validated biomarkers for treatment response in patients suffering from brain metastases are needed in daily clinical practice as they may improve survival by providing reliable prognostic information and allowing alternative therapies. This work presents a new analysis tool for an early and non-invasive evaluation of treatment response in patients with brain metastases. A set of twenty-five metastases from sixteen patients were examined by T1-weighted and diffusion magnetic resonance imaging before starting radiotherapy and at least once after treatment. Diffusion MRI can show a correlation between water diffusion variation within metastasis area and its clinical evolution. Images were co-registered to pretreatment scans. Diffusion changes, resulting in spatially varying changes in apparent diffusion coefficient values of metastatic lesions, were quantified and presented as a functional diffusion map (fDM). These functional maps were compared to two traditional criteria for assessing oncological response. Of the twenty-five metastases analyzed, seven were classified as partial response (PR), eight as stable disease (SD) and nine as progressive disease (PD). Normalized volume values of the metastases for each response group were obtained, disclosing that apparent diffusion coefficient increase was a good predictor of response. Sensitivity was 88%, specificity 100%, positive predictive value 100% and negative predictive value was 94%. Outcome reveals that the implemented tool, based on functional diffusion mapping as evolution biomarker, provides a reliable prediction of metastases response to treatment.

Functional diffusion maps (fDMs) evaluated before and after radiochemotherapy predict progression-free and overall survival in newly diagnosed glioblastoma

Functional diffusion mapping (fDM) has shown promise as a sensitive imaging biomarker for predicting survival in initial studies consisting of a small number of patients, mixed tumor grades, and before routine use of anti-angiogenic therapy. The current study tested whether fDM performed before and after radiochemotherapy could predict progression-free and overall survival in 143 patients with newly diagnosed glioblastoma from 2007 through 2010, many treated with anti-angiogenic therapy after recurrence. Diffusion and conventional MRI scans were obtained before and 4 weeks after completion of radiotherapy and concurrent temozolomide treatment. FDM was created by coregistering pre- and posttreatment apparent diffusion coefficient (ADC) maps and then performing voxel-wise subtraction. FDMs were categorized according to the degree of change in ADC in pre- and posttreatment fluid-attenuated inversion recovery (FLAIR) and contrast-enhancing regions. The volume fraction of fDM-classified increasing ADC(+), decreasing ADC(-), and change in ADC(+/-) were tested to determine whether they were predictive of survival. Both Bonferroni-corrected univariate log-rank analysis and Cox proportional hazards modeling demonstrated that patients with decreasing ADC in a large volume fraction of pretreatment FLAIR or contrast-enhancing regions were statistically more likely to progress earlier and expire sooner than in patients with a lower volume fraction. The current study supports the hypothesis that fDM is a sensitive imaging biomarker for predicting survival in glioblastoma.