Sensitivity of MRI Tumor Biomarkers to VEGFR Inhibitor Therapy in an Orthotopic Mouse Glioma Model

MRI biomarkers of tumor edema, vascular permeability, blood volume, and average vessel caliber are increasingly being employed to assess the efficacy of tumor therapies. However, the dependence of these biomarkers on a number of physiological factors can compromise their sensitivity and complicate the assessment of therapeutic efficacy. Here we examine the response of these MRI tumor biomarkers to cediranib, a potent vascular endothelial growth factor receptor (VEGFR) inhibitor, in an orthotopic mouse glioma model. A significant increase in the tumor volume and relative vessel caliber index (rVCI) and a slight decrease in the water apparent diffusion coefficient (ADC) were observed for both control and cediranib treated animals. This contrasts with a clinical study that observed a significant decrease in tumor rVCI, ADC and volume with cediranib therapy. While the lack of a difference between control and cediranib treated animals in these biomarker responses might suggest that cediranib has no therapeutic benefit, cediranib treated mice had a significantly increased survival. The increased survival benefit of cediranib treated animals is consistent with the significant decrease observed for cediranib treated animals in the relative cerebral blood volume (rCBV), relative microvascular blood volume (rMBV), transverse relaxation time (T2), blood vessel permeability (Ktrans), and extravascular-extracellular space (νe). The differential response of pre-clinical and clinical tumors to cediranib therapy, along with the lack of a positive response for some biomarkers, indicates the importance of evaluating the whole spectrum of different tumor biomarkers to properly assess the therapeutic response and identify and interpret the therapy-induced changes in the tumor physiology

Hypoxia in prostate cancer: Correlation of bold-MRI with pimonidazole immunohistochemistry - Initial observations

Purpose: To investigate the ability of blood oxygen level- dependent (BOLD) MRI to depict clinically significant prostate tumor hypoxia. Methods and Materials: Thirty-three patients with prostate carcinoma undergoing radical prostatectomy were studied preoperatively, using gradient echo sequences without and with contrast medium enhancement, to map relative tissue oxygenation according to relaxivity rates and relative blood volume (rBV). Pimonidazole was administered preoperatively, and whole-mount sections of selected tumor-bearing slices were stained for pimonidazole fixation and tumor and nontumor localization. Histologic and imaging parameters were independently mapped onto patient prostate outlines. Using 5-mm grids, 861 nontumor grid locations were compared with 237 tumor grids (with >50% tumor per location) using contingency table analysis with respect to the ability of imaging to predict pimonidazole staining. Results: Twenty patients completed the imaging and histologic protocols. Pimonidazole staining was found in 33% of nontumor and in 70% of tumor grids. The sensitivity of the MR relaxivity parameter R-2* in depicting tumor hypoxia was high (88%), improving with the addition of low rBV information (95%) without changing specificity (36% and 29%, respectively). High R-2* increased the positive predictive value for hypoxia by 6% (70% to 76%); conversely, low R-2* decreased the likelihood of hypoxia being present by 26% (70% to 44%) and by 41% (71% to 30%) when combined with rBV information. Conclusion: R-2* maps from BOLD-MRI have high sensitivity but low specificity for defining intraprostatic tumor hypoxia. This together with the negative predictive value of 70% when combined with blood volume information make

Temporal Analysis of Tumor Heterogeneity and Volume for Cervical Cancer Treatment Outcome Prediction: Preliminary Evaluation

In this paper, we present a method of quantifying the heterogeneity of cervical cancer tumors for use in radiation treatment outcome prediction. Features based on the distribution of masked wavelet decomposition coefficients in the tumor region of interest (ROI) of temporal dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) studies were used along with the imaged tumor volume to assess the response of the tumors to treatment. The wavelet decomposition combined with ROI masking was used to extract local intensity variations in the tumor. The developed method was tested on a data set consisting of 23 patients with advanced cervical cancer who underwent radiation therapy; 18 of these patients had local control of the tumor, and five had local recurrence. Each patient participated in two DCE-MRI studies: one prior to treatment and another early into treatment (2–4 weeks). An outcome of local control or local recurrence of the tumor was assigned to each patient based on a posttherapy follow-up at least 2 years after the end of treatment. Three different supervised classifiers were trained on combinational subsets of the full wavelet and volume feature set. The best-performing linear discriminant analysis (LDA) and support vector machine (SVM) classifiers each had mean prediction accuracies of 95.7%, with the LDA classifier being more sensitive (100% vs. 80%) and the SVM classifier being more specific (100% vs. 94.4%) in those cases. The K-nearest neighbor classifier performed the best out of all three classifiers, having multiple feature sets that were used to achieve 100% prediction accuracy. The use of distribution measures of the masked wavelet coefficients as features resulted in much better predictive performance than those of previous approaches based on tumor intensity values and their distributions or tumor volume alone