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INTRODUCTION Hyperpolarized carbon-13 (HP-13C) MR enables rapid dynamic imaging of metabolic pathways in the human brain using non-toxic, non-radioactive metabolites as tracers. This study presents our unique experience on the benefit of using [1-13C]pyruvate and [2-13C]pyruvate MR for evaluating patients with glioma. METHODS 132 scans (71 using an integrated 13C /1H coil) including steady-state 1H-MRSI (~10 min) and dynamic HP-13C imaging (60 sec) following injection of HP [1-13C]pyruvate (N=125) or [2-13C]pyruvate (N=7) were acquired from 46 patients with glioma (18F/28M; 15 IDH-mutant, 27 IDH-wildtype, 4 IDH-status-unknown). Maps of temporally-summed 13C-metabolite signals, ratios, and kinetic rate constants were calculated for contrast-enhancing, nonenhancing, and normal-appearing-white-matter (NAWM) regions and compared to steady-state metabolic metrics. RESULTS The only adverse event, in a single patient, was a burning sensation after the injection that resolved after saline flush. The mean time-to-injection of HP probes was 58.6±14.0 sec. Signal-to-noise ratios of [1-13C]lactate and [13C]bicarbonate within the NAWM from the HP [1-13C]pyruvate data were 53±39 and 13±6, respectively. The SD/mean of repeated injections (N=3) for lactate/pyruvate and pyruvate-to-lactate conversion rates were 3.8±3.1% and 6.5±3.1% in the NAWM, respectively. Patients with progressive GBM had significantly higher lactate/pyruvate and lower bicarbonate/lactate (p< 0.05) in contrast- and non-enhancing lesions compared to NAWM. Significantly elevated lactate/pyruvate and reduced bicarbonate/lactate (p< 0.01) were found in contrast-enhancing compared to nonenhancing regions, whereas choline/NAA and steady-state 1H-lactate levels were similar. HP [2-13C]pyruvate data showed reduced glutamate/pyruvate and pyruvate-to-glutamate conversion rates in T2 lesions compared to contralateral normal-appearing brain in IDH-mutant gliomas, consistent with known metabolic reprogramming. CONCLUSION This study demonstrates the potential benefit of dynamic HP-13C MRI for evaluating patients with glioma, which provides unique and spatially distinct contrast compared to steady-state metabolic imaging. Ongoing studies will further characterize dynamic metabolism in specific glioma subtypes and provide biomarkers for evaluating responses to treatment.

Although physiologic (diffusion-weighted and perfusion-weighted) MRI has shown promise in identifying regions of recurrent tumor (rTumor) in patients with glioblastoma suspected of progression, distinguishing treatment-induced effects (TxE) from rTumor on anatomical MRI remains a challenge. Whereas prior larger-scale machine learning (ML)-based studies mostly utilize anatomical imaging alone and/or perform lesion-level predictions, this study aimed to develop a non-invasive, radiopathomic tool for regional probabilistic mapping of TxE using 208 tissue-samples (55 pathologically-confirmed TxE, 153 recurrent glioblastoma) acquired from 107 patients with known spatial coordinates on pre-surgical MRI. We tested the hypothesis that applying a deep-learning (DL) model that included physiological MRI can: 1) more accurately identify areas of TxE that mimic rTumor on anatomical MRI and 2) better generalize to an independent test set than ML-models or a DL-model that uses anatomical MRI alone. An 80/20 split for training/validation was used after 1/3 of the patients were withheld for testing. Oversampling of TxE samples was employed to address class imbalance and an equal proportion of TxE samples was maintained across all datasets. Three ML-models, their ensemble, and a deep 4D-convolutional-neural-network were trained based on normalized anatomical (post-contrast T1, T2-FLAIR), diffusion-weighted (ADC, FA), and DSC perfusion-weighted (PeakHeight, %recovery) images cropped to 10mm-cubic patches centered on the coordinates from where tissue was obtained. Although Random Forest and voting-ensembled ML-models using all imaging and the anatomical DL-model had the best validation performance (AUC=0.81-0.82), these models did not generalize (test AUC=0.58-0.59). The DL-model including physiologic images had slightly lower validation AUC (0.78) but the best overall test AUC (0.795), indicating superior generalizability. Elevated blood volume (nPeakHeight) was the most important feature. Our DL-model’s interpretability was also demonstrated by disrupting class separation after shuffling voxels within each input patch. These results suggest that using deep-learning with physiologic MRI can improve intratumoral classification of TxE from rTumor.

INTRODUCTION Pathologically aggressive tumor biology can extend beyond the contrast-enhancing or non-enhancing anatomical lesions in patients with glioma. Identification of malignant regions can help guide diagnosis and subsequent treatment planning. This study leverages a unique multi-parametric MRI dataset with tissue samples of known spatial coordinates to noninvasively predict cellular proliferation (KI-67) and a novel index of tumor aggressiveness (TAI), that combines proliferation, cellularity, and tumor-score. METHODS 420 tissue samples were collected from 162 patients with newly-diagnosed glioma (47% IDH-wildtype). Clinical imaging consisted of T2-weighted, T2-FLAIR, T1-weighted pre- and post-contrast images, and apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion-weighted imaging. Mean normalized imaging metrics were quantified from 5mm spheres centered at the location of the tissue sample. A single spectrum was reconstructed at the location of each tissue sample from 3D 1H-MR Spectroscopic Imaging (MRSI) before quantifying normalized metabolite peak-heights for choline, creatine, NAA, lactate/lipid, and relative indices. Univariate mixed-effects linear regression models were employed and features with p< 0.2 were selected for subsequent model building. Support vector machine (SVM), random forest, and gradient boosting machine-learning algorithms were trained and tested on a ⅔-⅓ train-test split with 4-fold cross-validation in training to predict a high/low KI-67 and TAI. RESULTS Although none of the individual imaging metrics were significantly associated with KI-67 in the univariate analysis, all diffusion and several MRSI metrics (ncholine, nNAA, CNI, excess choline and creatine) were significantly associated with cellularity. Preliminary multivariate analyses to date suggest that the best radiopathomic model performance is achieved when an SVM was used along with T1-precontrast, nADC, and all metabolite levels (mean cross-validation AUC=0.73 and accuracy=.77). CONCLUSION Our results suggest that multi-parametric physiologic and metabolic MRI are useful for radiopathomic-mapping of tumor aggressiveness and are currently being optimized in a larger cohort.

INTRODUCTION Noninvasive, radiopathomic mapping of tumor aggressiveness can benefit patients with glioma by guiding the selection of tissue samples for diagnosis, increasing extent of resection, and non-invasively characterizing residual tumor burden for subsequent treatment. Although prior studies have demonstrated the utility of metabolic metrics quantified from 1H-MR Spectroscopy (MRS) in probing tumor pathology, this study evaluated the benefit of using the entire 1D-spectrum and deep learning for radiopathomic mapping of intratumoral cellularity, proliferation (ki-67), and a new tumor aggressiveness index (TAI) defined as log((n(ki−67)+n(cellularity))*tumor-score). METHODS Multi-voxel 1H-MRS was acquired on 281 patients newly diagnosed with a glioma (47% IDH-wildtype) immediately before surgical resection. After reconstructing individual spectra at the locations where tissue samples were obtained during surgery and normalizing by NAA in contralateral normal-appearing-white-matter, 607 spectra with corresponding histopathology were deemed of sufficient quality for analysis. A 1D convolutional-neural-network with bidirectional long- and short-term memory deep-learning model using the entire spectrum (0.6-3.6ppm) was compared to mixed-effects regression (with choline-to-NAA index[CNI]) and Random Forest (with CNI+normalized peak heights) models for predicting ki-67, cellularity, and TAI. Results & DISCUSSION Using deep-learning on the entire spectrum resulted in 10.3%-22.1% lower mean absolute error (MAE) and 0.32-0.37 higher R2 values compared to using CNI alone or a random forest model with multiple metabolic metrics. MAE values for all 3 deep-learning models were 26-44% < 1 standard deviation of the ground truth, demonstrating reasonable prediction accuracy within the test data set. Although the lowest MAE (0.16) and highest R2 (0.41) was attained when predicting TAI with deep-learning, the prediction of cellularity resulted in the lowest %MAE. Colormaps of predicted pathology identified regions of heightened aggressiveness surrounding tissue samples with most abnormal pathological features that sometimes extended beyond the non-enhancing lesion. Current work is evaluating the clinical utility of our deep-learning model and predicted maps of aggressiveness.

BACKGROUND Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment-related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion-weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time point and those who remained clinically stable. METHODS Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion, and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed. RESULTS Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients. CONCLUSIONS LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

Treatment failure in glioblastoma, the most common and lethal primary brain tumor in adults, is often attributed to intratumoral heterogeneity as it fosters tumor evolution and selection of resistant clones. Sources of intratumoral heterogeneity may include genomic alterations, such as single-nucleotide and structural variants, and epigenomic alterations, such as changes in chromatin structure and transcriptional regulation. The relative extent and functional significance of these contributors to intratumoral heterogeneity in glioblastoma have yet to be elucidated. In collaboration with neurosurgeons and neuro-imaging experts, we have established a novel approach towards characterizing intratumoral heterogeneity in three-dimensional (3D) space. For patients undergoing tumor resection, we utilize 3D surgical neuronavigation to safely acquire ~10 samples representing maximal anatomical diversity. Samples are mapped by 3D spatial coordinates and integrated with patient MRI scans for 360º visualization of sample location in context of the brain. We have now conducted whole-exome sequencing (Exome-Seq), assay for transposase-accessible chromatin (ATAC-Seq), and RNA-sequencing (RNA-Seq) for 83 spatially mapped samples obtained from 8 patients with primary IDH-WT glioblastoma. Integrative data analysis provides unprecedented insight into sources of intratumoral heterogeneity in glioblastoma and their 3D spatial patterning. We find that tumor cells show aberrant transcription factor activity, activation of fetal brain programs, and variable chromatin accessibility at CTCF-binding loop anchors indicating plasticity in higher-order chromatin structure. Chromosome conformation capture analysis by Hi-C extends these findings and reveals intratumoral differences in long-range chromatin interactions due to structural variants and enhancer hijacking. Further, we use deconvolution to identify microenvironmental contributors to epigenomic intratumoral heterogeneity including neural, glial, and immune populations. We define chromatin signatures associated with microenvironmental cell types and states, revealing their 3D spatial patterning, and validate these findings by single-cell ATAC-Seq. Our work thus establishes both tumor and microenvironmental sources of intratumoral heterogeneity in glioblastoma, revealing their chromatin programs and 3D spatial patterning. As a resource for further investigation, we have developed an interactive data sharing platform that enables visualization of brain tumor intratumoral heterogeneity in 360 degrees. Citation Format: Radhika Mathur, Qixuan Wang, Patrick Schupp, Ana Nikolic, Takafumi Yamaguchi, Stephanie Hilz, Chibo Hong, Ivan Smirnov, Marisa LaFontaine, Joanna Phillips, Susan Chang, Yan Li, Janine Lupo, Paul Boutros, Marco Gallo, Michael Oldham, Feng Yue, Joseph Costello. 3D spatial sampling and integrated omics reveal sources and patterning of intratumoral heterogeneity in glioblastoma [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 3621.

OBJECTIVE To identify genetic predictors of neurocognition, CMBs, brain volume, and WM changes in pediatric brain tumor survivors. METHODS Patients were selected from an existing cohort (RadART) if they had: 1) at least one neurocognitive evaluation using computer-based CogState; 2) available DNA; 3) standard imaging. Candidate gene or genome-wide genotyping was performed on all patients. CMBs were identified using a semi-automated algorithm developed in MATLAB. Volume of T2/FLAIR WM signal abnormality was measured using a semi-automated method based on a convolutional neural network. Brain volume and cortical thickness were measured using FreeSurfer volumetric analysis. Logistic and linear regression were done to compare phenotypes with candidate genotypes. Genome-wide efficient mixed-model analysis was done to compare neurocognition and CMBs. Gene set analysis was done using https://fuma.ctglab.nl/. RESULTS APOE4 was a candidate variant associated with non-lobar, larger volume CMBs (p CONCLUSION Genetic markers may be associated with neurocognition, CMBs, brain volume and WM changes in pediatric brain tumor survivors; however, larger cohorts are needed to confirm specific gene relevance.

INTRODUCTION It is estimated that 25 to 35% of patients experience treatment-induced effects that can mimic recurrent high-grade gliomas. This diagnostic challenge is complicated by the coexistence of treatment-related changes and recurrent tumor within the same lesion which limits the accuracy of classification based on summary metrics of multi-parametric MRI. This study aimed to determine whether different MR features were relevant for distinguishing pathological features of recurrent tumor from the effects of treatment in the contrast enhancing and non-enhancing lesions of recurrent high-grade gliomas. METHODS Leveraging our unique dataset of image-guided tissue samples that directly maps pathology to MR characteristics, we analyzed 291 tissue samples (222 recurrent tumor; 69 treatment effect) with known coordinates on imaging from 139 patients that underwent preoperative 3T MRI and surgery for a suspected high-grade recurrent tumor. 8 MR parameter values from perfusion-weighted, diffusion-weighted, and MR spectroscopic imaging at each tissue sample location were tested for association with histopathological outcome using univariate and multivariate generalized estimating equation models for enhancing and non-enhancing tissue samples. Individual cutoff values were determined and evaluated using ROC-Curve analysis with 5-fold cross-validation. RESULTS In tissue samples obtained from contrast-enhancing lesions, elevated relative cerebral blood volume (rCBV) was significantly associated with the presence of recurrent tumor (p< 0.03), while increases in normalized choline (nCho) and choline-to-NAA index (CNI) were significantly associated with the presence of recurrent tumor in non-enhancing tissue samples (p< 0.008). Cutoff values of 1.6(rCBV), 2.7(CNI), and 2.1(nCho) had the highest performance. CONCLUSION Our results confirm the utility of rCBV in distinguishing the effects of treatment from recurrent tumor within the contrast enhancing lesion. We report a novel finding that metabolic parameters can differentiate recurrent tumor from treatment-related changes in the non-enhancing lesion of high-grade gliomas. These results will help improve future management of patients with suspected recurrence.

The goal of this study was to find the most robust algorithm for a phase-sensitive coil combination of 3D single-cycle and lactate-edited, multi-channel H-1 point-resolved spectroscopy (PRESS) localized echo planar spectroscopic imaging (EPSI) data for clinical applications in the brain. Data were acquired over 5-10 minutes at 3T using 8- or 32-channel array coils. Peak referencing with residual water and N-acetyl-aspartate, first-point phasing, generalized least squared (GLS) and whitened singular-value decomposition (WSVD) combination algorithms were evaluated relative to unsuppressed water with data from a phantom, six volunteers and 55 patients with brain tumors. Comparison metrics were signal-to-noise ratio, coefficient of variance and percent signal increase. Where residual water was present, using it as a reference peak for phasing and weighting factors from an imaging calibration scan gave the best overall performance. Greater improvement was seen for large selected volumes (720 cm

MOTIVATION: In the treatment of brain tumours, radiation therapy (RT) is associated with long-term effects including vascular injury, changes in white matter, and cognitive decline. Vascular injury typically manifests as size-varying hemosiderin deposits in the brain called cerebral microbleeds (CMBs) which can be detected with susceptibility-weighted imaging (SWI) as early as 8 months following treatment. Similar forms of vascular injury have been related to the cognitive impairments experienced by other patient populations (e.g. stroke, vascular dementia); preliminary findings suggest a similar relationship for RT-induced CMBs, though their clinical relevance remains unclear. Further characterization of RT-induced CMBs in relation to cognition, treatment and clinical parameters is thus needed to develop a more thorough understanding of the impact of CMBs on patients with brain tumours, with the ultimate goal of mitigating CMB development and associated long-term effects without sacrificing treatment efficacy. METHODS: Ultra-high field MRI and SWI were used to detect CMBs with high sensitivity in patients treated with RT for an adult (focal) or pediatric (whole-brain) brain tumour. For the adult patients, predictors of CMB development were identified, and evolutionary changes in CMB burden were characterized across serial imaging data. Neurocognitive testing was performed for the pediatric patients and the distribution of CMBs was evaluated; global and local CMB burden were related to global and region-specific measures of cognitive function. RESULTS: Total CMB burden increased with time since RT, though individual CMBs decreased in size overtime. Risk factors for CMB development in adults included multiple surgical resections, tumour type (and subsequent RT regimen), and tumour location. Majority of CMBs were in the white matter, occipital, temporal, and frontal lobes. Increased presence of CMBs in the frontal lobe was associated with worse performance on tasks measuring execution function in children.