Abstract 2459: Automated VIF methods improve DCE-MRI parameterization repeatability in GBM
Authors: Woodall Ryan T. , Sahoo Prativa , Cui Yujie , Chen Bihong , Shiroishi Mark , Lavini Cristina , Frankel Paul , Gutova Margarita , Brown Christine , Munson Jennifer , Rockne Russell 
Published: June 2022

Introduction: Analysis of Dynamic Contrast Enhanced (DCE) MRI is widely used for measuring perfusion kinetics of cancerous tissue, though its implementation and results may vary between sites. Here we study the repeatability of quantitative DCE-MRI parameterization in a publicly available Glioblastoma (GBM) double-baseline dataset and set forth percent repeatability coefficients (%RC) which constitute a level of change at which a detectable biological change in biology may be differentiated from experimental noise.

Methods: Double-baseline imaging data was previously acquired for The Cancer Imaging Archive, in the Quantitative Imaging Network (QIN) GBM Treatment Response (TR) dataset. This data includes dual baseline acquisitions of DCE-MRI MRI data performed within 5 days, for 29 patients. Each acquisition is analyzed using two vascular input function (VIF) methods: a manually segmented VIF from the superior sagittal sinus, and an automatically derived VIF determined from the top 10% of rapidly enhancing voxels. Each dynamic scan is analyzed the the Extended Tofts-Kety model (eTM; Ktrans, ve, vp) pharmacokinetic (PK) model. Bland-Altman analysis is then performed on the log-transformed PK parameters to determine the percent repeatability coefficients (%RC) of the data (95% confidence), as well as bias between first and second DCE scans. This analysis was repeated for two separate observers, writing their own analysis code, both of which are validated on a digital reference object to ensure consistency between analysis methods with zero noise.

Results: For bothVIF determination methods, the 25-75th percentiles of the change in PK parameters was less than +/- 25%, the standard set forth by QIBA as a detectable biological change. For most parameters, VIF methods, and model combinations, the automatic VIF method has smallest %RC, indicating that automatic VIF determination may enhance the repeatability of the measurements between timepoints. The %RC was lowest for vp when using an automatically determined VIF. Using the automatically determined VIF and eTM, the %RCs of Ktrans, ve, and vp were measured as 72%, 82%, and 53%, respectively. Using the eTM and manually segmented VIF, the %RCs of Ktrans, ve, and vp were measured as 83%, 88%, and 66%.

Conclusions: Using the QIN-GBM-TR dataset, the authors have determined that the %RC of Ktrans, ve, and vp to be 72%, 82%, and 53%, respectively (eTM, auto-VIF). Because these relative changes are large, we recommend that all sites abide by QIBA guidelines to enhance DCE parameterization reproducibility. We also recommend usage of an automated AIF determination method, as opposed to manual segmentation of large blood vessels.

Citation Format: Ryan T. Woodall, Prativa Sahoo, Yujie Cui, Bihong Chen, Mark Shiroishi, Cristina Lavini, Paul Frankel, Margarita Gutova, Christine Brown, Jennifer Munson, Russell Rockne. Automated VIF methods improve DCE-MRI parameterization repeatability in GBM [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2459.