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Browsing by Author "Yeom, Kristen"
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Item Integrating neuroimaging biomarkers into the multicentre, high-dose erythropoietin for asphyxia and encephalopathy (HEAL) trial: rationale, protocol and harmonisation(BMJ, 2021-04-22) Wisnowski, Jessica L.; Bluml, Stefan; Panigrahy, Ashok; Mathur, Amit M.; Berman, Jeffrey; Chen, Ping-Sun Keven; Dix, James; Flynn, Trevor; Fricke, Stanley; Friedman, Seth D.; Head, Hayden W.; Ho, Chang Y.; Kline-Fath, Beth; Oveson, Michael; Patterson, Richard; Pruthi, Sumit; Rollins, Nancy; Ramos, Yanerys M.; Rampton, John; Rusin, Jerome; Shaw, Dennis W.; Smith, Mark; Tkach, Jean; Vasanawala, Shreyas; Vossough, Arastoo; Whitehead, Matthew T.; Xu, Duan; Yeom, Kristen; Comstock, Bryan; Heagerty, Patrick J.; Juul, Sandra E.; Wu, Yvonne W.; McKinstry, Robert C.; Radiology and Imaging Sciences, School of MedicineIntroduction: MRI and MR spectroscopy (MRS) provide early biomarkers of brain injury and treatment response in neonates with hypoxic-ischaemic encephalopathy). Still, there are challenges to incorporating neuroimaging biomarkers into multisite randomised controlled trials. In this paper, we provide the rationale for incorporating MRI and MRS biomarkers into the multisite, phase III high-dose erythropoietin for asphyxia and encephalopathy (HEAL) Trial, the MRI/S protocol and describe the strategies used for harmonisation across multiple MRI platforms. Methods and analysis: Neonates with moderate or severe encephalopathy enrolled in the multisite HEAL trial undergo MRI and MRS between 96 and 144 hours of age using standardised neuroimaging protocols. MRI and MRS data are processed centrally and used to determine a brain injury score and quantitative measures of lactate and n-acetylaspartate. Harmonisation is achieved through standardisation-thereby reducing intrasite and intersite variance, real-time quality assurance monitoring and phantom scans. Ethics and dissemination: IRB approval was obtained at each participating site and written consent obtained from parents prior to participation in HEAL. Additional oversight is provided by an National Institutes of Health-appointed data safety monitoring board and medical monitor.Item Statistical multiscale mapping of IDH1, MGMT, and microvascular proliferation in human brain tumors from multiparametric MR and spatially-registered core biopsy(Nature Research, 2019-11-19) Parker, Jason G.; Diller, Emily E.; Cao, Sha; Nelson, Jeremy T.; Yeom, Kristen; Ho, Chang; Lober, Robert; Radiology and Imaging Sciences, School of MedicineWe propose a statistical multiscale mapping approach to identify microscopic and molecular heterogeneity across a tumor microenvironment using multiparametric MR (mp-MR). Twenty-nine patients underwent pre-surgical mp-MR followed by MR-guided stereotactic core biopsy. The locations of the biopsy cores were identified in the pre-surgical images using stereotactic bitmaps acquired during surgery. Feature matrices mapped the multiparametric voxel values in the vicinity of the biopsy cores to the pathologic outcome variables for each patient and logistic regression tested the individual and collective predictive power of the MR contrasts. A non-parametric weighted k-nearest neighbor classifier evaluated the feature matrices in a leave-one-out cross validation design across patients. Resulting class membership probabilities were converted to chi-square statistics to develop full-brain parametric maps, implementing Gaussian random field theory to estimate inter-voxel dependencies. Corrections for family-wise error rates were performed using Benjamini-Hochberg and random field theory, and the resulting accuracies were compared. The combination of all five image contrasts correlated with outcome (P < 10−4) for all four microscopic variables. The probabilistic mapping method using Benjamini-Hochberg generated statistically significant results (α ≤ 0.05) for three of the four dependent variables: (1) IDH1, (2) MGMT, and (3) microvascular proliferation, with an average classification accuracy of 0.984 ± 0.02 and an average classification sensitivity of 1.567% ± 0.967. The images corrected by random field theory demonstrated improved classification accuracy (0.989 ± 0.008) and classification sensitivity (5.967% ± 2.857) compared with Benjamini-Hochberg. Microscopic and molecular tumor properties can be assessed with statistical confidence across the brain from minimally-invasive, mp-MR.