bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-53. DELTA RELAXOMETRY WITH CONTRAST ENHANCED MAGNETIC RESONANCE FINGERPRINTING: INITIAL APPLICATION TO DIFFERENTIATE BRAIN METASTASES AND GLIOBLASTOMA
Authors: Deng Shengwen , Zhao Walter , Nirappel Abraham , Jordan David , Sloan Andrew , Ma Dan , Badve Chaitra 
Published: November 2022

Abstract

PURPOSE
The utility of post-contrast magnetic resonance fingerprinting (MRF) for brain tumor assessment has not been assessed. The aim of this work is to report differences in MRF-derived delta-relaxometry metrics between brain metastases and glioblastoma, offering a new strategy for tumor differentiation.

METHODS
Post- and pre-contrast MRF (T1, T2 and proton density maps) were acquired from 29 patients (14 brain metastases (Met), 15 glioblastoma (GB)) along with conventional MRI (T1w, T1w-Gd, T2w, T2w-FLAIR, and ADC). Post-contrast MRF was skull-stripped and non-linearly co-registered with pre-contrast MRF. Delta relaxometry metrics (ΔR1/ΔR2 ratio, ΔR1, ΔR2, and normalized ΔR1/ΔR2) were calculated in the native image space (1.2x1.2x3.0 mm3). Tumor regions were segmented using DeepMedic into necrotic core (NC), enhancing tumor (ET), and peritumoral edema (ED). ROI-averaged means of delta relaxometry metrics were compared using paired t-test for ET and ED regions in Met vs GB. Voxel-wise ΔR1/ΔR2 ratios (log transformed) were compared using unpaired two-tailed t-test with Bonferroni correction to quantify distribution differences in delta relaxometry between Met and GB.

RESULTS
Across all voxels of all patients in each group, ΔR1/ΔR2 ratios between Met and GB were different (0.272±0.61 vs 0.247±0.66 in ED, p &lt; .001; 0.278±0.69 vs 0.264±0.67 in ET, p &lt; .001). On a per-patient basis, the median, 75th percentile, and 90th percentile of ΔR1/ΔR2 ratios were different between ET and ED regions (p &lt; .001). Within-ROI (NC, ET, and ED) averaged mean ΔR1/R2 ratios were not significantly different between Met and GB.

CONCLUSION
Voxel-wise distribution of ΔR1/ΔR2 ratios was different between tumor types (Met and GB) in both ET and ED regions. ROI-averaged ΔR1/ΔR2 ratios were different between ET and ED regions, but not specific for tumor type. Delta relaxometry provides a unique tumor-specific contrast and shows potential for solid tumor as well as peritumoral edema differentiation between Met and GB.
NIMG-55. A COMPARISON OF THREE DIFFERENT DEEP LEARNING-BASED MODELS TO PREDICT THE MGMT PROMOTER METHYLATION STATUS IN GLIOBLASTOMA USING BRAIN MRI
Authors: Faghani Shahriar , Khosravi Bardia , Moassefi Mana , Conte Gian Marco , Erickson Bradley 
Published: November 2022

Abstract
GBM is the most common primary malignant brain tumor in adults. O6-methylguanine DNA methyltransferase (MGMT) promoter methylation status is an important prognostic biomarker that predicts the response to temozolomide and guides the treatment decisions. At present, the only reliable way to determine MGMT promoter methylation status is through the analysis of tumor tissues. Given the limitations and complications of histology-based methods, an imaging-based approach for non-invasively MGMT promoter methylation status prediction is beneficial. This study aimed to develop and compare three different deep-learning-based approaches for predicting MGMT promoter methylation status non-invasively. We obtained 576 T2 weighted images (T2WIs) with their corresponding tumor masks and MGMT promoter methylation status from, The Brain Tumor Segmentation (BraTS) 2021 datasets. Dataset was split into five folds at the patient's level stratified by MGMT promoter methylation status to perform a 5-fold cross-validation. We developed three different models: voxel-wise, slice-wise, and whole-brain. For voxel-wise classification, methylated and unmethylated MGMT tumor masks were made into 1 and 2 with 0 background, respectively. We converted each T2WI into 32x32x32 patches. We trained a 3D-Vnet model for tumor segmentation. After inference, we constructed the whole brain volume based on the patch's coordinates. The final prediction of MGMT methylation status was made by majority voting between the predicted voxel values of the biggest connected component. For slice-wise classification, we trained an object detection model for tumor detection and MGMT methylation status prediction; then, we used majority voting for the final prediction. For the whole-brain approach, we trained a 3D Densenet121 for prediction. Whole-brain, slice-wise, voxel-wise, accuracy was 65.42%(SD 3.97%), 61.37%(SD 1.48%), and 56.84%(SD 4.38%) respectively. We found that across the whole-brain, slice-wise, and voxel-wise deep learning approaches, the whole-brain approach is the most effective approach for MGMT methylation status prediction on the BraTS 2021 dataset.
NIMG-57. NON-INVASIVE DIAGNOSIS OF IDH-MUTANT BRAINSTEM GLIOMAS
Authors: Kushnirsky Marina , Thakur Sunitha , Karajannis Matthias , Bale Tejus , Rosenblum Marc , Malani Rachna , Gavrilovic Igor T , Schaff Lauren , Pentsova Elena , Grommes Christian , Moss Nelson , Sait Sameer Farouk , Hill Katherine , Miller Alexandra , Mellinghoff Ingo , Young Robert , Lin Andrew 
Published: November 2022

Abstract

INTRODUCTION
Treatment of brainstem tumors is often initiated without a tissue diagnosis due to the risk of biopsy. A subset of brainstem gliomas harbor an isocitrate dehydrogenase 1/2 (IDH) mutation, which predicts response to alkylator chemotherapy and IDH inhibitors; non-invasive diagnostic tests are needed to identify these mutations.

METHODS
We identified a cohort of patients with IDH-mutant brainstem gliomas through chart review of patients who underwent magnetic resonance spectroscopy (MRS) and/or brain biopsy. IDH mutation was established by biopsy, presence of a 2-hydroxyglutarate (2HG) peak on MRS, or identification on cerebrospinal fluid (CSF) sequencing of cell-free DNA (cfDNA).

RESULTS
We identified 15 patients with IDH-mutant brainstem gliomas, age range 5-48, 47% women. 15/15 were involving/abutting the brachium pontis, and 10/15 were non-enhancing at diagnosis. 13/15 patients were identified by biopsy, 1/15 by MRS and CSF, and 1/15 by MRS only. 10/13 patients with available data had a non-IDH1 R132H mutation in IDH1/2. 7/7 patients with MRS prior to radiation had a 2HG peak (MRS was concordant with tissue in all cases in which biopsy was obtained). 4/6 patients with MRS post-radiation retained a 2HG peak. 4 IDH-mutant tumors had sequencing of cfDNA and an IDH mutation was found in 2/4. Response data was available in 13/15 patients; all received radiation, 10 with concurrent temozolomide: best response was partial response in 8 and stable disease in 5. 4 patients received an IDH inhibitor: 2 patients after progression, and 2 as maintenance after radiation/temozolomide, achieving tumor control in all. For this cohort, median PFS was 71.4 months and median OS has not been reached.

CONCLUSION
IDH-mutant brainstem gliomas have a characteristic appearance, a high response rate to treatment, and a better overall prognosis than other brainstem tumors. MRS and CSF cfDNA sequencing allow for non-invasive diagnosis of IDH mutations in these patients.
NIMG-60. MAGNETIC RESONANCE SPECTROSCOPY IN THE DIAGNOSTIC AND MANAGEMENT OF PATIENTS WITH BRAIN TUMORS OF GLIAL ORIGIN
Authors: Acosta Pablo Moreno , Padín Trinidad González , Malaver Gina , Gamboa Oscar , Villamil Daniel Fino 
Published: November 2022

Abstract

PURPOSE
Magnetic resonance spectroscopy (MRS) is a quantitative technique based on the chemical shift of protons that allows the metabolic profile of tissues to be analyzed and characterized. Standard glial tumor analysis comprises Creatine (Cr), N-acetil aspartato (NAA) and Choline (Cho) metabolites, usually altered in brain pathologies (low grade tumor -LGT-, high-grade cancer -HGT-, metastasis -MTT-, gliosis -GLS-). The aim of this study is to analyze the feasibility of MRS technique in brain glial tumor diagnosis.

METHODS
The prospective study, approved by the Institutional Review Board, included 68 randomized patients (39 female and 29 male, mean age 47 y, with confirmed histopathology). Data was acquired at a Essenza Siemens 1.5T scanner, single voxel (SV) at the lesion (IT) and at healthy tissue (HT) in the contralateral hemisphere. A multivoxel (CS) sample was obtained from a ROI containing HT and IT. Postprocessing was done at Syngo workstation, statistical analyses were performed using SPSS v25.0 and p-values &lt; 0.05 were considered statistically significant.

RESULTS
The group analysis (by pathology) for HT demonstrates homogeneity. For pathological ROIs (SV, long_TE), rNAA and rCho indexes (r stands for Cr ratio) had a p=0.003 and p=0.048 respectively; however, aCho (NAA ratio) had a p=0.11. For pathological regions, rNAA coefficient for LGT and GLS: p=0.008, LGT and MTT: p=0.02, HGT and MTT: p=0.008 and GLS with MTT: p=0.019. Meanwhile, for the rCho index, LGT and HGT: p=0.005, LGT and MTT: p=0.008, GLS and MTT: p=0.002. Short-TE SV demonstrates inhomogeneity between HT and only the aCho ratio allows to distinguish LGT and MTT (p=0.01) and GLS and MTT (p=0.008). Parametric map (from CS) of both rCho and aCho visually describes pathological regions (83% and 72%).

CONCLUSIONS
Long_TE-SV allows to characterize brain tumor lesions and CS provides a spatial metabolic description of the IT localization.
NIMG-62. RADIOMIC-BASED PROGRESSION-FREE SURVIVAL STRATIFICATION OF PEDIATRIC LOW-GRADE GLIOMA IS ASSOCIATED WITH MOLECULAR ALTERATIONS
Authors: Kazerooni Anahita Fathi , Arif Sherjeel , Haldar Debanjan , Zhao Chao , Kim Meen Chul , Madhogarhia Rachel , Familiar Ariana , Bagheri Sina , Anderson Hannah , Ware Jeffrey B , Vossough Arastoo , Storm Philip B , Resnick Adam C , Davatzikos Christos , Nabavizadeh Ali 
Published: November 2022

Abstract
Pediatric low-grade glioma (pLGG) encompasses a variety of tumor subtypes with heterogeneous treatment response and relatively long progression-free survival (PFS). Radiomics may serve as a non-invasive and in-vivo tool for early prediction of PFS as a surrogate marker for treatment response and to objectively gauge the efficacy of novel treatment strategies. Here, we present a multivariate model based on radiomic features and clinical variables for risk stratification of pLGGs in terms of PFS and seek associations of the predicted risk groups and mutations in key molecular markers using data from PedCBioportal. Pre-operative multi-parametric MRI scans (T1-pre, T1-post, T2, T2-FLAIR) of 129 patients with newly diagnosed pLGG (median age, 7.76, range, 0.35-19.58 years; median PFS, 28.5, range, 1.1-124.8 months) were collected and quantitative radiomic features (n = 881) were extracted. A multivariate Cox proportional hazard’s (Cox-PH) regression model was fitted based on clinical (age, sex, and extent of tumor resection) and radiomic variables using 4-fold cross-validation. A subset of radiomic features (n = 27) that were most predictive of PFS was selected by applying Elastic Net regularization penalty during Cox-PH model fitting. High-, medium- and low-risk groups were determined based on model predictions. Cox-PH modeling showed excellent performance for prediction of PFS as suggested by the concordance index of 0.78. Radiogenomic assessment (data available in 94/129 patients) showed more enrichment of mutations in NF1 and RB1 genes in the high-risk group, as compared to the low- and medium-risk groups. We showed the potential value of radiomics in providing upfront prediction of PFS, which may further be used as an added treatment arm for early assessment of treatment response of the pLGG patients enrolled in the clinical trials. In the next step of this work, we will expand the cohort and cross-validate these results in an external cohort.
NIMG-64. INTRAOPERATIVE LANGUAGE MAPPING USING GAMMA-BAND MODULATIONS OF ELECTROCORTICOGRAM (ECOG) INDUCED BY WORD/SOUND CATEGORIZATION TASK: VALIDATION WITH REPRODUCIBLE SPEECH ARRESTS DURING LINGUISTIC TASKS
Authors: Tasnim Israt , Ince Nuri , Prabhu Sujit , Noll Kyle , Swamy Chandra , Connelly Katherine , Asman Priscella , Sudhakar Tummala , Prinsloo Sarah , Ene Chibawanye , Grasu Roxana , Liu Ho-Ling , Muir Matthew , Bhavsar Shreyas , Kumar Vinodh 
Published: November 2022

Abstract

OBJECTIVE
Determination of the correlation between gamma-band modulations of electrocorticogram (ECoG) induced by linguistic tasks and reproducible speech arrests caused by bipolar direct cortical stimulations (DCS).

METHODS
3 subjects (age 39, 56, 64 years) with left temporal lobe glioma underwent surgery involving awake craniotomy. A 4x8 ECoG electrode grid (2.3mm contact exposure, 1cm contact spacing) was placed above the respective tumor area. A MATLAB-Simulink based real-time software system running on a portable laptop computer was used to map gamma-band modulations as a 2D heat map while the subjects engaged in different linguistic tasks: word/sound categorization by pressing a button, object naming, action naming, written descriptive naming, and auditory descriptive naming. Auditory stimulus was applied during word/sound categorization task (duration 300 – 500ms), auditory descriptive naming ( &gt; 1s); other tasks involved visual stimulus only. The subjects repeated the four naming tasks while bipolar DCS (2/4/6 mA, 60Hz, 2s) was applied at different electrode pairs.

RESULTS
The electrodes having stronger gamma-band modulations were distinct for different tasks. Reproducible speech arrests occurred during object, action, auditory naming tasks while stimulating specific electrode pairs, even though not all these electrodes had strong activations during these tasks. Across all subjects these electrodes had strong activations consistently during word/sound categorization tasks, starting as early as 250ms and lasting even after the auditory stimuli were terminated (~ 650ms). The longer activations can be associated with word recognition process. The subjects self-reported about having difficulty in comprehension rather than speech production during speech arrests. 3D brain rendering using MRI images showed that the speech arrest electrodes were identically located on the superior temporal gyrus, inferior to central sulcus for all 3 subjects.

CONCLUSION
Intraoperative language mapping guided by gamma-band ECoG modulations induced by word/sound categorization tasks can be utilized to localize eloquent cortex associated with auditory processing.
NIMG-65. IMPROVED SPATIAL MAPPING OF TUMOR AGGRESSIVENESS WITH 1H MAGNETIC RESONANCE SPECTROSCOPY AND DEEP LEARNING IN PATIENTS WITH NEWLY-DIAGNOSED GLIOMA
Authors: Tran Nate , Li Yan , Ellison Jacob , Adegbite Oluwaseun , Phillips Joanna J , Molinaro Annette , Pedoia Valentina , Shai Anny , Nair Devika , Jakary Angela , Lafontaine Marisa , Villanueva-Meyer Javier , Berger Mitchel S , Hervey-Jumper Shawn L , Aghi Manish , Chang Susan M , Lupo Janine 
Published: November 2022

Abstract

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% &lt; 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.
NIMG-68. IMAGING PHOSPHOLIPID BIOSYNTHESIS ENABLES NON-INVASIVE ASSESSMENT OF BRAIN TUMOR RESPONSE TO THERAPY
Authors: Taglang Celine , Batsios Georgios , Tran Meryssa , Gillepspie Anne Marie , Viswanath Pavithra 
Published: November 2022

Abstract
Elevated phospholipid biosynthesis is a metabolic hallmark of cancer. Due to the high membrane turnover associated with uncontrolled proliferation, tumor cells invariably upregulate phospholipid biosynthesis. Choline is a dietary nutrient that is phosphorylated to phosphocholine (PC) and subsequently incorporated into phosphatidylcholine, which is the primary membrane phospholipid in mammalian cells. Choline kinase α (CKα) is the key enzyme in this pathway and its expression is elevated in most cancers. Identifying non-invasive methods of imaging CKα activity, can, therefore, enable assessment of tumor burden and response to therapy. Deuterium magnetic resonance spectroscopy (DMRS) following administration of 2H-labeled substrates recently emerged as a simple, robust, clinically translatable method of assessing metabolic activity in vivo. The goals of the current study were to determine whether DMRS-based assessment of PC production from [2H9]-choline tracks CKα activity and to establish the utility of [2H9]-choline for glioma imaging in vivo. First, we show that [2H9]-choline metabolism to PC can be observed in live patient-derived glioma cells (GBM6 and BT88). Silencing CKα abrogates PC production from [2H9]-choline in both GBM6 and BT88 models, thereby confirming that [2H9]-choline provides a readout of CKα activity. Next, we examined the ability of [2H9]-choline to monitor tumor burden and response to therapy in vivo. [2H9]-choline metabolism to PC can be observed in mice bearing orthotopic patient-derived GBM6 or BT257 tumors, but not in tumor-free healthy controls. Importantly, following treatment of mice bearing orthotopic BT257 tumors with temozolomide, which is standard of care for glioma patients, PC production from [2H9]-choline is reduced at early timepoints when changes in tumor volume cannot be detected by anatomical imaging. Taken together, our studies, for the first time, identify [2H9]-choline as a probe of CKα activity and highlight its ability to assess tumor burden and early response to therapy, which is a challenge in glioma imaging.
NIMG-84. PREDICTION OF RESPONSE TO LOMUSTINE-BASED CHEMOTHERAPY IN GLIOMA PATIENTS AT RECURRENCE USING MRI AND FET PET
Authors: Wollring Michael M , Werner Jan-Michael , Bauer Elena , Tscherpel Caroline , Lohmann Philipp , Stoffels Gabriele , Kabbasch Christoph , Goldbrunner Roland , Fink Gereon , Langen Karl-Josef , Galldiks Norbert 
Published: November 2022

Abstract

BACKGROUND
This study evaluates O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET and contrast-enhanced MRI for early response assessment in glioma patients at recurrence treated with lomustine-based chemotherapy.

METHODS
Thirty-six adult patients with WHO CNS Grade 3 or 4 gliomas (glioblastoma, 69%) at recurrence (median number of recurrences, 1; range, 1-3) were retrospectively identified. Besides MRI, serial FET PET scans were performed at baseline and early after chemotherapy initiation (median number of cycles, 2). Mean and maximum tumor-to-brain ratios (TBR), metabolic tumor volumes (MTV), the occurrence of new distant hotspots with a mean TBR &gt; 1.6 at follow-up, and the dynamic parameter time-to-peak were derived from all FET PET scans. Threshold values of PET parameters were defined using receiver operating characteristic analyses to predict progression-free survival (PFS) of ≥ 6 months and overall survival (OS) of ≥ 12 months. MRI response assessment was based on RANO criteria. Using univariate and multivariate survival estimates, the predictive value of FET PET parameters and RANO criteria were subsequently evaluated.

RESULTS
After treatment initiation, the median follow-up time was 11 months (range, 3-71 months). Relative changes of the mean and maximum TBR, MTV, and RANO criteria predicted a significantly longer PFS (all P&lt; 0.003) and OS (all P&lt; 0.05). At follow-up, the occurrence of new distant hotspots (n≥ 1) predicted a worse outcome, with significantly shorter PFS (P=0.001) and OS (P&lt; 0.001). Changes of the dynamic parameter time-to-peak did not predict a significantly longer PFS or OS (P &gt; 0.05). Multivariate survival analyses revealed that new distant hotspots at follow-up had the highest significance level to predict non-response (P&lt; 0.001; hazard ratio, 8.578), independent of RANO criteria, IDH mutation status, and O6-methylguanine-DNA-methyltransferase promoter methylation.

CONCLUSIONS
FET PET seems to be a powerful tool for identifying responders to lomustine-based chemotherapy early after treatment initiation.
NIMG-85. RADIOMIC FEATURES PREDICTIVE OF RESPONSE IN HGG-TARGETING CAR-T THERAPY
Authors: Filippov Aleksandr , Shaktah Lawrence , Wong Chi Wah , Bonjoc Kimberley-Jane , Shaktah Bassam , Rockne Russell C , Brown Christine , Chaudhry Ammar , Badie Behnam 
Published: November 2022

Abstract

SIGNIFICANCE
Radiomics may improve precision medicine in CAR-T (Chimeric Antigen Receptor T Cell) therapy patient selection.

BACKGROUND
High-Grade Glioma (HGG) is a heterogenous primary CNS neoplasm with a high recurrence rate and poor outcomes. Many studies are exploring CAR T cells to combat HGG. Radiomic models have shown value in identifying biomarkers predictive of tumor genetics, response, and patient prognosis. In this study, we explore radiomic features derived from four clinical sequences and volumes of edema and enhancing tumor to predict treatment response to the first three doses of CAR-T therapy.

METHODS
In this IRB-approved phase 1 clinical trial (IRB 13384), patients underwent surgical resection of the tumor and received CAR-T cell therapy. Of the 82 patients accrued, 59 (20 females, median age = 49) completed three cycles of therapy and had 3T field strength MRI scans with minimal imaging artifacts. T1 weighted pre-contrast, T1 weighted post-contrast, T2 weighted, and T2 FLuid Attenuated Inversion Recovery sequences were used to generate 3D and 2D radiomics features. In total 28,541 radiomic features were generated per patient using the images prior to CAR-T administration. Each patient’s response to treatment after three cycles was determined to be either stable disease (29 patients) or progression. The radiomic feature set dimensionality was reduced using Maximum Relevance Minimum Redundancy. 10-fold cross-validation XGBoost was used to determine radiomic features predictive of treatment response with a randomized grid search for hyperparameter tuning.

RESULTS
Six radiomic features (four shape-based), had high SHapley Additive exPlanations (SHAP)-based importance feature predictive of RANO response with an AUC &gt;0.73.

CONCLUSION
Despite the limited study size, such imaging-based radiomic models can serve as a potential basis for optimizing clinical trial design through more precise patient screening and providing potential predictive imaging biomarkers of whether a patient will respond to CAR-T cell therapy in HGGs.