Assessment of higher-order singular value decomposition denoising methods on dynamic hyperpolarized [1-13C]pyruvate MRI data from patients with glioma.

BACKGROUND: Real-time metabolic conversion of intravenously-injected hyperpolarized [1-13C]pyruvate to [1-13C]lactate and [13C]bicarbonate in the brain can be measured using dynamic hyperpolarized carbon-13 (HP-13C) MRI. However, voxel-wise evaluation of metabolism in patients with glioma is challenged by the limited signal-to-noise ratio (SNR) of downstream 13C metabolites, especially within lesions. The purpose of this study was to evaluate the ability of higher-order singular value decomposition (HOSVD) denoising methods to enhance dynamic HP [1-13C]pyruvate MRI data acquired from patients with glioma. METHODS: Dynamic HP-13C MRI were acquired from 14 patients with glioma. The effects of two HOSVD denoising techniques, tensor rank truncation-image enhancement (TRI) and global-local HOSVD (GL-HOSVD), on the SNR and kinetic modeling were analyzed in [1-13C]lactate data with simulated noise that matched the levels of [13C]bicarbonate signals. Both methods were then evaluated in patient data based on their ability to improve [1-13C]pyruvate, [1-13C]lactate and [13C]bicarbonate SNR. The effects of denoising on voxel-wise kinetic modeling of kPL and kPB was also evaluated. The number of voxels with reliable kinetic modeling of pyruvate-to-lactate (kPL) and pyruvate-to-bicarbonate (kPB) conversion rates within regions of interest (ROIs) before and after denoising was then compared. RESULTS: Both denoising methods improved metabolite SNR and regional signal coverage. In patient data, the average increase in peak dynamic metabolite SNR was 2-fold using TRI and 4-5 folds using GL-HOSVD denoising compared to acquired data. Denoising reduced kPL modeling errors from a native average of 23% to 16% (TRI) and 15% (GL-HOSVD); and kPB error from 42% to 34% (TRI) and 37% (GL-HOSVD) (values were averaged voxelwise over all datasets). In contrast-enhancing lesions, the average number of voxels demonstrating within-tolerance kPL modeling error relative to the total voxels increased from 48% in the original data to 84% (TRI) and 90% (GL-HOSVD), while the number of voxels showing within-tolerance kPB modeling error increased from 0% to 15% (TRI) and 8% (GL-HOSVD). CONCLUSION: Post-processing denoising methods significantly improved the SNR of dynamic HP-13C imaging data, resulting in a greater number of voxels satisfying minimum SNR criteria and maximum kinetic modeling errors in tumor lesions. This enhancement can aid in the voxel-wise analysis of HP-13C data and thereby improve monitoring of metabolic changes in patients with glioma following treatment.

The influence of race and socioeconomic status on therapeutic clinical trial screening and enrollment.

PURPOSE: Under-enrollment in clinical trials significantly limits valid analyses of clinical interventions and generalizability of findings. Often it results in premature study termination, with estimates of 22% to 50% of clinical trials terminated due to poor accrual. Currently, there are limited reports addressing the influence of race/ethnicity and socioeconomic status on clinical trial enrollment in adult glioma patients. The goal of this study was to test the hypothesis that race and socioeconomic status negatively impact therapeutic clinical trial enrollment. METHODS: 988 adult patients were identified from the UCSF Tumor Board Registry and analyzed to determine the rate of therapeutic clinical trial screening and study enrollment. RESULTS: At initial diagnosis, 43.6% and 17.5% of glioma patients were screened and enrolled in a therapeutic clinical trial, respectively. At recurrence, 49.8% and 26.3% of patients were screened and enrolled in a clinical trial, respectively. Thirty-three percent of the study population belonged to a NIH-designated underrepresented minority group; Asian/Pacific-Islander comprised 19.6% of the overall cohort. On univariate analysis, only in-state location, distance to the hospital, and WHO grade were associated with enrollment at initial diagnosis and recurrence. Minority status, insurance type, median household income, and percent below poverty were not associated with clinical trial enrollment. CONCLUSION: Minority and socioeconomic status did not impact adult glioma clinical trial enrollment. Proximity to the tertiary care cancer center may be an important consideration for minority patients. Patient screening should be carefully considered in order to avoid bias based on minority and socioeconomic status.