RESUMO
PURPOSE: A dedicated magnetic resonance imaging simulation (MRsim) for radiation treatment (RT) planning in patients with high-grade glioma (HGG) can detect early radiologic changes, including tumor progression after surgery and before standard of care chemoradiation. This study aimed to determine the effect of using postoperative magnetic resonance imaging (MRI) versus MRsim as the baseline for response assessment and reporting pseudoprogression on follow-up imaging at 1 month (FU1) after chemoradiation. METHODS AND MATERIALS: Histologically confirmed patients with HGG were planned for 6 weeks of RT in a prospective study for adaptive RT planning. All patients underwent postoperative MRI, MRsim, and follow-up MRI scans every 2 to 3 months. Tumor response was assessed by 3 independent blinded reviewers using Response Assessment in Neuro-Oncology criteria when baseline was either postoperative MRI or MRsim. Interobserver agreement was calculated using Light's kappa. RESULTS: Thirty patients (median age, 60.5 years; IQR, 54.5-66.3) were included. Median interval between surgery and RT was 34 days (IQR, 27-41). Response assessment at FU1 differed in 17 patients (57%) when the baseline was postoperative MRI versus MRsim, including true progression versus partial response or stable disease in 11 (37%) and stable disease versus partial response in 6 (20%) patients. True progression was reported in 19 patients (63.3%) on FU1 when the baseline was postoperative MRI versus 8 patients (26.7%) when the baseline was MRsim (P = .004). Pseudoprogression was observed at FU1 in 12 (40%) versus 4 (13%) patients, when the baseline was postoperative MRI versus MRsim (P = .019). Interobserver agreement between observers was moderate (κ = 0.579; P < .001). CONCLUSIONS: Our study demonstrates the value of acquiring an updated MR closer to RT in patients with HGG to improve response assessment, and accuracy in evaluation of pseudoprogression even at the early time point of first follow-up after RT. Earlier identification of patients with true progression would enable more timely salvage treatments including potential clinical trial enrollment to improve patient outcomes.
RESUMO
PURPOSE: The rising promise in the utility of advanced multi-parametric magnetic resonance (MR) imaging in radiotherapy (RT) treatment planning creates a necessity for testing and enhancing the accuracy of quantitative imaging analysis. Standardizing the analysis of diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI) to generate meaningful and reproducible apparent diffusion coefficient (ADC) and fractional anisotropy (FA) lays the requisite needed for clinical integration. The aim of the demonstrated work is to benchmark the generation of the ADC and FA parametric map analyses using integrated tools in a commercial treatment planning system against the currently used ones. METHODS: Three software packages were used for generating ADC and FA maps in this study; one tool was developed within a commercial treatment planning system, another by the Functional Magnetic Resonance Imaging of the Brain (FMRIB) Software Library FSL (Analysis Group, FMRIB, Oxford, United Kingdom), and an in-house tool developed at the M.D. Anderson Cancer Center. The ADC and FA maps generated by all three packages for 35 subjects were subtracted from one another, and the standard deviation of the images' differences was used to compare the reproducibility. The reproducibility of the ADC maps was compared with the Quantitative Imaging Biomarkers Alliance (QIBA) protocol, while that of the FA maps was compared to data in published literature. RESULTS: Results show that the discrepancies between the ADC maps calculated for each patient using the three different software algorithms are less than 2% which meets the 3.6% recommended QIBA requirement. Except for a small number of isolated points, the majority of differences in FA maps for each patient produced by the three methods did not exceed 0.02 which is 10 times lower than the differences seen in healthy gray and white matter. The results were also compared to the maps generated by existing MR Imaging consoles and showed that the robustness of console generated ADC and FA maps is largely dependent on the correct application of scaling factors, that only if correctly placed; the differences between the three tested methods and the console generated values were within the recommended QIBA guidelines. CONCLUSIONS: Cross-comparison difference maps demonstrated that quantitative reproducibility of ADC and FA metrics generated using our tested commercial treatment planning system were comparable to in-house and established tools as benchmarks. This integrated approach facilitates the clinical utility of diffusion imaging in radiation treatment planning workflow.