Heterogeneous Optimization Framework: Reproducible Preprocessing of Multi-Spectral Clinical MRI for Neuro-Oncology Imaging Research.

Neuroimaging research often relies on clinically acquired magnetic resonance imaging (MRI) datasets that can originate from multiple institutions. Such datasets are characterized by high heterogeneity of modalities and variability of sequence parameters. This heterogeneity complicates the automation of image processing tasks such as spatial co-registration and physiological or functional image analysis. Given this heterogeneity, conventional processing workflows developed for research purposes are not optimal for clinical data. In this work, we describe an approach called Heterogeneous Optimization Framework (HOF) for developing image analysis pipelines that can handle the high degree of clinical data non-uniformity. HOF provides a set of guidelines for configuration, algorithm development, deployment, interpretation of results and quality control for such pipelines. At each step, we illustrate the HOF approach using the implementation of an automated pipeline for Multimodal Glioma Analysis (MGA) as an example. The MGA pipeline computes tissue diffusion characteristics of diffusion tensor imaging (DTI) acquisitions, hemodynamic characteristics using a perfusion model of susceptibility contrast (DSC) MRI, and spatial cross-modal co-registration of available anatomical, physiological and derived patient images. Developing MGA within HOF enabled the processing of neuro-oncology MR imaging studies to be fully automated. MGA has been successfully used to analyze over 160 clinical tumor studies to date within several research projects. Introduction of the MGA pipeline improved image processing throughput and, most importantly, effectively produced co-registered datasets that were suitable for advanced analysis despite high heterogeneity in acquisition protocols.

The role of imaging in the management of adults with diffuse low grade glioma: A systematic review and evidence-based clinical practice guideline.

QUESTION: What is the optimal imaging technique to be used in the diagnosis of a suspected low grade glioma, specifically: which anatomic imaging sequences are critical for most accurately identifying or diagnosing a low grade glioma (LGG) and do non-anatomic imaging methods and/or sequences add to the diagnostic specificity of suspected low grade gliomas? TARGET POPULATION: These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG. LEVEL II: In patients with a suspected brain tumor, the minimum magnetic resonance imaging (MRI) exam should be an anatomic exam with both T2 weighted and pre- and post-gadolinium contrast enhanced T1 weighted imaging. CRITICAL IMAGING FOR THE IDENTIFICATION AND DIAGNOSIS OF LOW GRADE GLIOMA: LEVEL II: In patients with a suspected brain tumor, anatomic imaging sequences should include T1 and T2 weighted and Fluid Attenuation Inversion Recovery (FLAIR) MR sequences and will include T1 weighted imaging after the administration of gadolinium based contrast. Computed tomography (CT) can provide additional information regarding calcification or hemorrhage, which may narrow the differential diagnosis. At a minimum, these anatomic sequences can help identify a lesion as well as its location, and potential for surgical intervention. IMPROVEMENT OF DIAGNOSTIC SPECIFICITY WITH THE ADDITION OF NON-ANATOMIC (PHYSIOLOGIC AND ADVANCED IMAGING) TO ANATOMIC IMAGING: LEVEL II: Class II evidence from multiple studies and a significant number of Class III series support the addition of diffusion and perfusion weighted MR imaging in the assessment of suspected LGGs, for the purposes of discriminating the potential for tumor subtypes and identification of suspicion of higher grade diagnoses. LEVEL III: Multiple series offer Class III evidence to support the potential for magnetic resonance spectroscopy (MRS) and nuclear medicine methods including positron emission tomography and single-photon emission computed tomography imaging to offer additional diagnostic specificity although these are less well defined and their roles in clinical practice are still being defined. QUESTION: Which imaging sequences or parameters best predict the biological behavior or prognosis for patients with LGG? TARGET POPULATION: These recommendations apply to adults with a newly diagnosed lesion with a suspected or histopathologically proven LGG. RECOMMENDATION: Anatomic and advanced imaging methods and prognostic stratification LEVEL III: Multiple series suggest a role for anatomic and advanced sequences to suggest prognostic stratification among low grade gliomas. Perfusion weighted imaging, particularly when obtained as a part of diagnostic evaluation (as recommended above) can play a role in consideration of prognosis. Other imaging sequences remain investigational in terms of their role in consideration of tumor prognosis as there is insufficient evidence to support more formal recommendations as to their use at this time. QUESTION: What is the optimal imaging technique to be used in the follow-up of a suspected (or biopsy proven) LGG? TARGET POPULATION: This recommendation applies to adults with a newly diagnosed low grade glioma. LEVEL II: In patients with a diagnosis of LGG, anatomic imaging sequences should include T2/FLAIR MR sequences and T1 weighted imaging before and after the administration of gadolinium based contrast. Serial imaging should be performed to identify new areas of contrast enhancement or significant change in tumor size, which may signify transformation to a higher grade. LEVEL III: Advanced imaging utility may depend on tumor subtype. Multicenter clinical trials with larger cohorts are needed. For astrocytic tumors, baseline and longitudinal elevations in tumor perfusion as assessed by dynamic susceptibility contrast perfusion MRI are associated with shorter time to tumor progression, but can be difficult to standardize in clinical practice. For oligodendrogliomas and mixed gliomas, MRS may be helpful for identification of progression.

Aerobic Glycolysis as a Marker of Tumor Aggressiveness: Preliminary Data in High Grade Human Brain Tumors.

OBJECTIVES: Glucose metabolism outside of oxidative phosphorylation, or aerobic glycolysis (AG), is a hallmark of active cancer cells that is not directly measured with standard (18)F-fluorodeoxyglucose (FDG) positron emission tomography (PET). In this study, we characterized tumor regions with elevated AG defined based on PET measurements of glucose and oxygen metabolism. METHODS: Fourteen individuals with high-grade brain tumors underwent structural MR scans and PET measurements of cerebral blood flow (CBF), oxygen (CMRO2) and glucose (CMRGlu) metabolism, and AG, using (15)O-labeled CO, O2 and H2O, and FDG, and were compared to a normative cohort of 20 age-matched individuals. RESULTS: Elevated AG was observed in most high-grade brain tumors and it was associated with decreased CMRO2 and CBF, but not with significant changes in CMRGlu. Elevated AG was a dramatic and early sign of tumor growth associated with decreased survival. AG changes associated with tumor growth were differentiated from the effects of nonneoplastic processes such as epileptic seizures. CONCLUSIONS: Our findings demonstrate that high-grade brain tumors exhibit elevated AG as a marker of tumor growth and aggressiveness. AG may detect areas of active tumor growth that are not evident on conventional FDG PET.

Comparison of perfusion- and diffusion-weighted imaging parameters in brain tumor studies processed using different software platforms.

RATIONALE AND OBJECTIVES: To compare quantitative imaging parameter measures from diffusion- and perfusion-weighted imaging magnetic resonance imaging (MRI) sequences in subjects with brain tumors that have been processed with different software platforms. MATERIALS AND METHODS: Scans from 20 subjects with primary brain tumors were selected from the Comprehensive Neuro-oncology Data Repository at Washington University School of Medicine (WUSM) and the Swedish Neuroscience Institute. MR images were coregistered, and each subject's data set was processed by three software packages: 1) vendor-specific scanner software, 2) research software developed at WUSM, and 3) a commercially available, Food and Drug Administration-approved, processing platform (Nordic Ice). Regions of interest (ROIs) were chosen within the brain tumor and normal nontumor tissue. The results obtained using these methods were compared. RESULTS: For diffusion parameters, including mean diffusivity and fractional anisotropy, concordance was high when comparing different processing methods. For perfusion-imaging parameters, a significant variance in cerebral blood volume, cerebral blood flow, and mean transit time (MTT) values was seen when comparing the same raw data processed using different software platforms. Correlation was better with larger ROIs (radii ≥ 5 mm). Greatest variance was observed in MTT. CONCLUSIONS: Diffusion parameter values were consistent across different software processing platforms. Perfusion parameter values were more variable and were influenced by the software used. Variation in the MTT was especially large suggesting that MTT estimation may be unreliable in tumor tissues using current MRI perfusion methods.

The comprehensive neuro-oncology data repository (CONDR): a research infrastructure to develop and validate imaging biomarkers.

BACKGROUND: Advanced imaging methods have the potential to serve as quantitative biomarkers in neuro-oncology research. However, a lack of standardization of image acquisition, processing, and analysis limits their application in clinical research. Standardization of these methods and an organized archival platform are required to better validate and apply these markers in research settings and, ultimately, in clinical practice. OBJECTIVE: The primary objective of the Comprehensive Neuro-oncology Data Repository (CONDR) is to develop a data set for assessing and validating advanced imaging methods in patients diagnosed with brain tumors. As a secondary objective, informatics resources will be developed to facilitate the integrated collection, processing, and analysis of imaging, tissue, and clinical data in multicenter clinical trials. Finally, CONDR data and informatics resources will be shared with the research community for further analysis. METHODS: CONDR will enroll 200 patients diagnosed with primary brain tumors. Clinical, imaging, and tissue-based data are obtained from patients serially, beginning with diagnosis and continuing over the course of their treatment. The CONDR imaging protocol includes structural and functional sequences, including diffusion- and perfusion-weighted imaging. All data are managed within an XNAT-based informatics platform. Imaging markers are assessed by correlating image and spatially aligned pathological markers and a variety of clinical markers. EXPECTED OUTCOMES: CONDR will generate data for developing and validating imaging markers of primary brain tumors, including multispectral and probabilistic maps. DISCUSSION: CONDR implements a novel, open-research model that will provide the research community with both open-access data and open-source informatics resources.