Functional diffusion map: a noninvasive MRI biomarker for early stratification of clinical brain tumor response

Abstract

Assessment of radiation and chemotherapy efficacy for brain cancer patients is traditionally accomplished by measuring changes in tumor size several months after therapy has been administered. The ability to use noninvasive imaging during the early stages of fractionated therapy to determine whether a particular treatment will be effective would provide an opportunity to optimize individual patient management and avoid unnecessary systemic toxicity, expense, and treatment delays. We investigated whether changes in the Brownian motion of water within tumor tissue as quantified by using diffusion MRI could be used as a biomarker for early prediction of treatment response in brain cancer patients. Twenty brain tumor patients were examined by standard and diffusion MRI before initiation of treatment. Additional images were acquired 3 weeks after initiation of chemo- and/or radiotherapy. Images were coregistered to pretreatment scans, and changes in tumor water diffusion values were calculated and displayed as a functional diffusion map (fDM) for correlation with clinical response. Of the 20 patients imaged during the course of therapy, 6 were classified as having a partial response, 6 as stable disease, and 8 as progressive disease. The fDMs were found to predict patient response at 3 weeks from the start of treatment, revealing that early changes in tumor diffusion values could be used as a prognostic indicator of subsequent volumetric tumor response. Overall, fDM analysis provided an early biomarker for predicting treatment response in brain tumor patients.

Fig. 1.

Biological processes proposed to be involved in therapeutic-induced changes in tumor ADC values along with a pictorial description of the fDM analytical process. (A) A schematic representation of the dynamic biological processes associated with changes (increase or decrease) in tumor water diffusion values. Tumor cells within an image voxel have several fates during treatment. Cells can be resistant to therapy (unaltered ADC, green) or can undergo necrosis initiated by a transient cell swelling (decreased ADC, blue). Cell enlargement (swelling) can also be associated with mitotic catastrophe or a reduction in tumor blood flow resulting in focal ischemia/hypoxia (decreased ADC, blue). These processes can eventually progress to cell lysis and necrosis (increased ADC, red). Cells can also undergo apoptosis involving cell shrinkage and blebbing followed by phagocytosis (increased ADC, red). (B) The concept that necrotic or cystic regions of a tumor can undergo drainage (displacement) of water as cells move into the region resulting in a drop in diffusion values (decreased ADC, blue) is summarized. (C) Diffusion MRI data undergo digital image postprocessing and analysis that involves coregistration of images before and during treatment. Data are used to generate a three-color overlay representing regions in which tumor ADC values are unchanged (green voxels), significantly increased (red voxels), or significantly decreased (blue voxels). This data can also be presented in a scatter plot and percentages assigned to the three defined ADC regions, allowing quantitative assessment of overall changes in tumor ADC values.

Fig. 2.

MRI of three patients with oligodendrogliomas. MR image datasets obtained from three different patients diagnosed with anaplastic oligodendrogliomas. Images shown are at 3 weeks into a seven-week fractionated ionizing radiation regimen. Regions of interest were drawn for each tumor image by using anatomical images. (A, C, and E) Shown are the regional spatial distribution of ADC changes (fDMs) of a single slice through each tumor as color overlays for the PD, SD, and PR patients, respectively. The red pixels indicate areas of increased diffusion, whereas the blue and green pixels indicate regions of decreased and unchanged ADC, respectively. The scatter plots (B, D, and F) show quantitatively the distribution of ADC changes for the entire three-dimensional tumor volume for each corresponding patient (A, C, and E), respectively.

Fig. 3.

Box plots summarizing fDM tumor volumes as a percent of total tumor volume for each patient group PR (n = 6), SD (n = 6), and PD (n = 8). (A) The volumes (percent of total) within the tumor that experienced significantly increased diffusion values (V_R). (B) The results for the tumor volume that had a significant decrease in diffusion values (V_B). (C) The total volume of tumor that had a significant change in ADC (V_T where V_T = V_R + V_B). Error bars reflect 95% confidence intervals.