Quarles, C. Chad

The Quantitative Imaging Biomarkers Alliance Profile for dynamic susceptibility contrast (DSC) MRI and its longitudinal claims for the precision of a DSC MRI–derived quantitative imaging biomarker to assess the relative cerebral blood volume changes in gliomas are summarized.

AbstractPurposePerfusion MRI reveals important tumor physiological and pathophysiologic information, making it a critical component in managing brain tumor patients. This study aimed to develop a dual‐echo 3D spiral technique with a single‐bolus scheme to simultaneously acquire both dynamic susceptibility contrast (DSC) and dynamic contrast‐enhanced (DCE) data and overcome the limitations of current EPI‐based techniques.MethodsA 3D spiral‐based technique with dual‐echo acquisition was implemented and optimized on a 3T MRI scanner with a spiral staircase trajectory and through‐plane SENSE acceleration for improved speed and image quality, in‐plane variable‐density undersampling combined with a sliding‐window acquisition and reconstruction approach for increased speed, and an advanced iterative deblurring algorithm. Four volunteers were scanned and compared with the standard of care (SOC) single‐echo EPI and a dual‐echo EPI technique. Two patients were scanned with the spiral technique during a preload bolus and compared with the SOC single‐echo EPI collected during the second bolus injection.ResultsVolunteer data demonstrated that the spiral technique achieved high image quality, reduced geometric artifacts, and high temporal SNR compared with both single‐echo and dual‐echo EPI. Patient perfusion data showed that the spiral acquisition achieved accurate DSC quantification comparable to SOC single‐echo dual‐dose EPI, with the additional DCE information.ConclusionA 3D dual‐echo spiral technique was developed to simultaneously acquire both DSC and DCE data in a single‐bolus injection with reduced contrast use. Preliminary volunteer and patient data demonstrated increased temporal SNR, reduced geometric artifacts, and accurate perfusion quantification, suggesting a competitive alternative to SOC‐EPI techniques for brain perfusion MRI.

Abstract INTRODUCTION Differentiation of glioblastoma recurrence from post treatment radiation effects (PTRE) would improve patient management. Dynamic susceptibility contrast (DSC) MRI can be used to generate fractional tumor burden (FTB) maps, via application of regional cerebral blood volume (RCBV) thresholds, to spatially differentiate recurrence from PTRE. FTB maps derived from a DSC-MRI protocol that includes a contrast agent preload in addition to the bolus injection and a moderate-flip angle (MFA) been histologically validated. Recently, a single-dose, low-flip angle (LFA) protocol was shown to provide RCBV measurements consistent with the MFA protocol and has been recommended as a consensus protocol. This study aims to identify the RCBV thresholds for the single-dose, LFA protocol that generates FTB maps, concordant with those obtained from the reference double-dose, MFA protocol. METHODS Two sets of DSC-MRI data were collected sequentially from 52 patients using a single-dose, low-flip angle (30o) protocol and a double-dose, moderate flip angle (60o) protocol using TR = 1.5s and TE = 30ms. Standardized RCBV maps (sRCBV) were obtained and co-registered with the post-contrast T1-weighted images using the FDA-approved clinical software, IB Neuro™ and IB Delta Suite™, respectively. The ground truth MFA-based FTB maps were computed using multiple sRCBV thresholds (1.0 and 1.75). An ROC analysis was conducted to identify the optimal, voxel-wise LFA sRCBV thresholds to produce FTB maps that best match the reference maps. RESULTS/ CONCLUSIONS The ROC analysis identified a lower LFA threshold of 1.0 to accurately distinguish PTRE from tumor recurrence and an upper threshold of 1.55 to differentiate aggressive tumor regions. The single-dose, LFA protocol eliminates variations in the contrast agent incubation time between the preload and bolus injection. This study provides motivation for the use of the single-dose LFA protocol in the management of patients with glioblastoma and is a reliable method for distinguishing tumor from PTRE.

Abstract High grade glioma (HGG) represents a group of devastating diseases with dismal prognosis. Surgical resection of the contrast enhancing (CE) region of HGG remains the mainstay of treatment, but recurrence inevitably arises from the unresected non-contrast enhancing (NE) region, surgically inaccessible due to cancer cell invasion into healthy brain tissue. Due to its critical role in recurrence, understanding of the NE region is central to the improvement of clinical outcomes. We reveal the biological characteristics of this region through image localized multi-regional sampling. We linked microenvironmental characteristics measured by multi-parametric MRI to genomic mutations and transcriptional phenotypes using mixed effect modeling which allowed us to control for individualized patient effects. We first confirmed that T2 is a significant indicator of IDH mutation status in the NE region, being the first description of such a relationship in a HGG cohort. We found the combination of EGFR amplification and CDKN2A homozygous loss was associated with a significantly lower mean diffusivity (MD) compared to double wild type tumors in the NE region, indicating the presence of greater cellular packing and proliferation in EGFR amplification/CDKN2A loss regions. Finally, using single cell pathway based tumor classifications, we showed that nK2, a DSC-MRI metric representing cell size heterogeneity, correlated positively with neuronal signature and negatively with glycolytic/plurimetabolic signature within the NE tumor, indicating that glycolytic/plurimetobolic tumors possessed a high amount of cell size heterogeneity compared to neuronal samples. This hypothesis was supported using digital reference object (DRO) modeling which confirmed that cell size and heterogeneity drove the differential nK2 signal between neuronal and glycolytic/plurimetabolic samples. We identified immune cell infiltrate as one possible mechanism of increased cell size heterogeneity using transcriptomic signature analysis which found more immune cell signatures within glycolytic/plurimetobolic tumors compared to neuronal. Collectively this study demonstrates the central role of multi-parametric MRI as a non-invasive measure of tumor biology and a tool for understanding the clinically critical NE region which can then inform new therapies targeting this region of HGG recurrence. Citation Format: Matthew Flick, Taylor Weiskittel, Kevin Meng-Lin, Fulvio D'Angelo, Francesca Caruso, Shannon Ensign, Mylan Blomquist, Luija Wang, Christopher Sereduk, Gustavo De Leon, Ashley Nespodzany, Javier Urcuyo, Ashlynn Gonzalez, Lee Curtin, Kyle Singleton, Aliya Anil, Natenael Simmineh, Erika Lewis, Teresa Noviello, Reyna Patel, Panwen Wang, Junwen Wang, Jennifer Eschbacher, Andrea Hawkins-Daarud, Pamela Jackson, Kris Smith, Peter Nakaji, Bernard Bendok, Richard Zimmerman, Chandan Krishna, Devi Patra, Naresh Patel, Mark Lyons, Matthew Neal, Kliment Donev, Maciej Mrugala, Alyx Porter, Scott Beeman, Yuxiang Zhou, Leslie Baxter, Christopher Plaisier, Jing Li, Hu Li, Anna Lasorella, Chad Quarles, Kristin Swanson, Michele Ceccarelli, Antonio Iavarone, Nhan Tran, Leland Hu. Multi-parametric MRI maps regional heterogeneity of high grade glioma phenotypes. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5621.

PurposeThe purpose of this study was to develop a spiral‐based combined spin‐ and gradient‐echo (spiral‐SAGE) method for simultaneous dynamic contrast‐enhanced (DCE‐MRI) and dynamic susceptibility contrast MRI (DSC‐MRI).MethodsUsing this sequence, we obtained gradient‐echo TEs of 1.69 and 26 ms, a SE TE of 87.72 ms, with a TR of 1663 ms. Using an iterative SENSE reconstruction followed by deblurring, spiral‐induced image artifacts were minimized. Healthy volunteer images are shown to demonstrate image quality using the optimized reconstruction, as well as for comparison with EPI‐based SAGE. A bioreactor phantom was used to compare dynamic‐contrast time courses with both spiral‐SAGE and EPI‐SAGE. A proof‐of‐concept cohort of patients with brain tumors shows the range of hemodynamic maps available using spiral‐SAGE.ResultsComparison of spiral‐SAGE images with conventional EPI‐SAGE images illustrates substantial reductions of image distortion and artifactual image intensity variations. Bioreactor phantom data show similar dynamic contrast time courses between standard EPI‐SAGE and spiral‐SAGE for the second and third echoes, whereas first‐echo data show improvements in quantifying T1 changes with shorter echo times. In a cohort of patients with brain tumors, spiral‐SAGE–based perfusion and permeability maps are shown with comparison with the standard single‐echo EPI perfusion map.ConclusionSpiral‐SAGE provides a substantial improvement for the assessment of perfusion and permeability by mitigating artifacts typically encountered with EPI and by providing a shorter echo time for improved characterization of permeability. Spiral‐SAGE enables quantification of perfusion, permeability, and vessel architectural parameters, as demonstrated in brain tumors.

Abstract Advanced molecular and pathophysiologic characterization of primary central nervous system lymphoma (PCNSL) has revealed insights into promising targeted therapeutic approaches. Medical imaging plays a fundamental role in PCNSL diagnosis, staging, and response assessment. Institutional imaging variation and inconsistent clinical trial reporting diminishes the reliability and reproducibility of clinical response assessment. In this context, we aimed to: (1) critically review the use of advanced positron emission tomography (PET) and magnetic resonance imaging (MRI) in the setting of PCNSL; (2) provide results from an international survey of clinical sites describing the current practices for routine and advanced imaging, and (3) provide biologically based recommendations from the International PCNSL Collaborative Group (IPCG) on adaptation of standardized imaging practices. The IPCG provides PET and MRI consensus recommendations built upon previous recommendations for standardized brain tumor imaging protocols (BTIP) in primary and metastatic disease. A biologically integrated approach is provided to addresses the unique challenges associated with the imaging assessment of PCNSL. Detailed imaging parameters facilitate the adoption of these recommendations by researchers and clinicians. To enhance clinical feasibility, we have developed both “ideal” and “minimum standard” protocols at 3T and 1.5T MR systems that will facilitate widespread adoption.

Abstract Despite the widespread clinical use of dynamic susceptibility contrast (DSC) MRI, DSC-MRI methodology has not been standardized, hindering its utilization for response assessment in multicenter trials. Recently, the DSC-MRI Standardization Subcommittee of the Jumpstarting Brain Tumor Drug Development Coalition issued an updated consensus DSC-MRI protocol compatible with the standardized brain tumor imaging protocol (BTIP) for high-grade gliomas that is increasingly used in the clinical setting and is the default MRI protocol for the National Clinical Trials Network. After reviewing the basis for controversy over DSC-MRI protocols, this paper provides evidence-based best practices for clinical DSC-MRI as determined by the Committee, including pulse sequence (gradient echo vs spin echo), BTIP-compliant contrast agent dosing (preload and bolus), flip angle (FA), echo time (TE), and post-processing leakage correction. In summary, full-dose preload, full-dose bolus dosing using intermediate (60°) FA and field strength-dependent TE (40–50 ms at 1.5 T, 20–35 ms at 3 T) provides overall best accuracy and precision for cerebral blood volume estimates. When single-dose contrast agent usage is desired, no-preload, full-dose bolus dosing using low FA (30°) and field strength-dependent TE provides excellent performance, with reduced contrast agent usage and elimination of potential systematic errors introduced by variations in preload dose and incubation time.

Abstract Dynamic susceptibility contrast (DSC) MRI measures of brain tumor cerebral blood volume (CBV) are able to predict grade, overall survival and response to treatment. Wide-spread acceptance of DSC-MRI has been challenged by the need to balance contrast agent dose and CBV accuracy. The goal of this study was to identify and validate single-dose, BTIP compliant, DSC-MRI protocols. Using a validated, patient-based DRO, we evaluated CBV accuracy across a range of acquisition parameters (field strength, TR, TE, flip angle, multi-echo acquisitions, dosing protocols) and post-processing steps. To validate the optimal protocols, we next collected DSC-MRI data following ASFNR’s recommended “double – dose” approach, where a single-dose preload (to minimize T1 effects) is given prior to a second bolus injection (for DSC-MRI data acquisition). The single-dose DSC-MRI data was collected during the preload bolus injection. Consistency of the derived CBV data, visual agreement and data characteristics (e.g. CNR) was statistically evaluated. When using a single-dose and routine single-echo pulse sequence, the DRO analysis found that a low flip angle (LFA = 30o) and 30ms TE provided the highest CBV accuracy (concordance correlation coefficient (CCC) = 0.92) and precision (coefficient of variation (CV) = 8.2%)). For comparison, the maximum accuracy found with the DRO utilizes a double-dose injection protocol and yielded a CCC of 0.98 and CV of 6.8%. Single-dose, multi-echo acquisitions provided higher accuracy than the LFA data and matched that found with the double-dose approach. In patients (data collection ongoing), the agreement between single-dose LFA (n > 40) or multi-echo (n > 40) based CBV values and the reference double-dose approach was very high (CCC > 0.94) and were statistically equivalent. Optimized single-dose DSC-MRI protocols provide highly accurate CBV data, use lower doses of contrast agent, and simplify scan procedures, indicating their potential for robust use in clinical practice and trials.

AbstractBackgroundGlioblastoma (GBM) exhibits profound intratumoral genetic heterogeneity. Each tumor comprises multiple genetically distinct clonal populations with different therapeutic sensitivities. This has implications for targeted therapy and genetically informed paradigms. Contrast-enhanced (CE)-MRI and conventional sampling techniques have failed to resolve this heterogeneity, particularly for nonenhancing tumor populations. This study explores the feasibility of using multiparametric MRI and texture analysis to characterize regional genetic heterogeneity throughout MRI-enhancing and nonenhancing tumor segments.MethodsWe collected multiple image-guided biopsies from primary GBM patients throughout regions of enhancement (ENH) and nonenhancing parenchyma (so called brain-around-tumor, [BAT]). For each biopsy, we analyzed DNA copy number variants for core GBM driver genes reported by The Cancer Genome Atlas. We co-registered biopsy locations with MRI and texture maps to correlate regional genetic status with spatially matched imaging measurements. We also built multivariate predictive decision-tree models for each GBM driver gene and validated accuracies using leave-one-out-cross-validation (LOOCV).ResultsWe collected 48 biopsies (13 tumors) and identified significant imaging correlations (univariate analysis) for 6 driver genes: EGFR, PDGFRA, PTEN, CDKN2A, RB1, and TP53. Predictive model accuracies (on LOOCV) varied by driver gene of interest. Highest accuracies were observed for PDGFRA (77.1%), EGFR (75%), CDKN2A (87.5%), and RB1 (87.5%), while lowest accuracy was observed in TP53 (37.5%). Models for 4 driver genes (EGFR, RB1, CDKN2A, and PTEN) showed higher accuracy in BAT samples (n = 16) compared with those from ENH segments (n = 32).ConclusionMRI and texture analysis can help characterize regional genetic heterogeneity, which offers potential diagnostic value under the paradigm of individualized oncology.