bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-26. GLIOBLASTOMA TISSUE INVASION MAPPING WITH NOVEL HYPOXIA-TARGETED BLOOD OXYGENATION-LEVEL DEPENDENT MRI
Authors: Stumpo Vittorio , Bellomo Jacopo , van Niftrik Christiaan Hendrick Bas , Sebök Martina , Kulcsar Zsolt , Weller Michael , Bink Andrea , Regli Luca , Fierstra Jorn 
Published: November 2024

Abstract

BACKGROUND
Glioblastoma is a devastating incurable malignant primary brain tumor exhibiting an erratic growth pattern and brain tissue invasion exceeding the typically depicted contrast-enhancing lesion on MRI. This behaviour is strongly associated with the presence of aberrant vasculature. Advanced clinical neuroimaging techniques, however, have not provided convincing imaging markers to determine glioblastoma tissue infiltration and monitor treatment effects. We investigated a new imaging technique based on blood oxygenation-level dependent (BOLD) MRI contrast with precise hypoxic respiratory targeting in order to better determine tumor tissue invasion not restricted to the typically depicted focal glioblastoma lesion on contrast-enhanced MRI.

METHODS
A computer-controlled gas blender was used to induce transient and standardized hypoxic stimulus in isocapnic conditions during echo-planar-imaging acquisition in patients with brain tumors.8,9 Data were preprocessed using SPM and in-house written MATLAB scripts. The induced BOLD signal changes were used to calculate %BOLD signal change during hypoxic stimulus with respect to the baseline, contrast to noise (CNR) ratio and goodness-of-fit (Rsquared) for each voxel in the whole-brain and automatically segmented region of interest (contrast-enhancement, necrosis, edema) and overlayed using color-maps onto anatomical high resolution T1.

RESULTS
Hypoxic targeting during gas modulation could be performed with high reproducibility, caused no discomfort in patients with brain tumors and was able to induce strong signal change during hypoxic stimulus with high contrast-noise ratio and Rsquared. In particular, when compared with other tumors, BOLD hypoxia glioblastoma tissue invasion maps show very distinct hypoxic signal responses in some cases extending beyond the focal -contrast-enhancing- glioblastoma lesion.

CONCLUSION
The ability to map at voxel level BOLD hypoxia signal patterns may determine a novel therapeutic imaging marker based on structural vascular alterations in the tumour tissue infiltration zone before these become appreciable with standard neuroimaging. This may support a refinement and tailoring of tumor resection and improve follow-up after resection and during treatment i.e. chemoradiotherapy.
NIMG-38. DIAGNOSIS AND TREATMENT OF BRAIN TUMORS USING METHIONINE PET
Authors: Shiba Yoshiki , Maeda Sachi , Yamaguchi Junya , Deguchi Shoichi , Ohka Fumiharu , Saito Ryuta 
Published: November 2024

Abstract

INTRODUCTION
The usefulness of methionine PET examination in diagnosing brain tumors is widely recognized, and it is a frequently used examination method for distinguishing primary tumors from non-neoplastic lesions and recurrent lesions from radiation necrosis. In addition, during surgery, it has the property of being taken up by actively dividing tumor cells, which is useful for identifying the extent of resection. Method: Methionine PET examination was performed in 228 consecutive cases in our department from January 2021 to April 2023, and medical records and genetic analysis data were retrospectively analyzed.

RESULTS
Of the 228 cases, 146 cases provided useful data for clinical practice, such as the diagnosis of tumors and the differentiation of recurrent tumors from necrosis. On the other hand, there were 9 cases in which the pathological diagnosis of the resected specimen was “tumor” despite the lack of significant methionine accumulation. The details were astrocytoma, cavernous hemangioma, spinal ependymoma, etc. On the other hand, 26 cases were finally diagnosed as “non-neoplastic disease”. Of these, 13 cases had methionine accumulation. The details of the cases that were finally diagnosed as “non-neoplastic disease” were multiple sclerosis, cerebral infarction, dural arteriovenous fistula, inflammatory disease, etc. There were 12 cases in which a diagnosis of “neoplastic disease” was not revealed after methionine PET examination, and the patient was continuously observed. None of these 12 cases had an acute or malignant course.

CONCLUSION
Although methionine PET is considered useful for diagnosing neoplastic disease, there are cases in which there is no uptake, especially in astrocytoma, and a certain number of cases in which uptake was observed even in non-neoplastic diseases, so caution is required. In addition, in terms of treatment, it is possible to aim to improve surgical accuracy by integrating methionine PET images with intraoperative MRI and intraoperative molecular analysis techniques, and we report an example of this.
NIMG-40. RADIOMICS-BASED DIFFERENTIATION OF GLIOBLASTOMA AND METASTATIC DISEASE: DOES DIFFERENCE IN TYPE OF T1-CONTRAST ENHANCED SEQUENCE MATTER?
Authors: soni Neetu , Zamboni Camila , Agarwal Amit , Larson Nicholas B , Sonka Milan , Bathla Girish 
Published: November 2024

Abstract

BACKGROUND
To evaluate the radiomics-based model performance for differentiation between glioblastoma (GB) and intracranial metastatic disease (IMD) using magnetization prepared rapid gradient echo (MPRAGE) and volumetric interpolated breath-hold examination (VIBE) T1-contrast enhanced sequences.

MATERIALS AND METHODS
T1-CE MPRAGE and VIBE sequences acquired in 108 patients (31 GBs and 77 IMD) during the same MRI session were retrospectively evaluated. Post standardized image pre-processing and segmentation, radiomics features were extracted from necrotic and enhancing tumor components. A total of 90 machine learning (ML) pipelines were evaluated using a five-fold cross-validation. Performance was measured by mean AUC, Log-loss, and Brier scores. Pearson correlation analysis of radiomics features from tumor subcomponents was also performed.

RESULTS
The mean AUC across the top-ten pipelines varied between 0.890-0.851 with T1-CE MPRAGE and 0.907-0.869 with T1-CE VIBE sequence. Top performing models for the MPRAGE sequence commonly used support vector machines, while those for VIBE sequence used either support vector machines or random forest. Common feature reduction methods for top-performing models included linear combination filter and least absolute shrinkage and selection operator (LASSO) for both sequences. Only three of the top ten models used the same combination of feature reduction-ML algorithm pipeline. A feature-wise comparison showed that the radiomic features between sequences were strongly correlated, with the highest correlation for shape-based features.

CONCLUSION
Radiomics features derived from T1-CE MPRAGE and VIBE sequences may have similar overall classification performance for differentiating GB from IMD, albeit often using different feature selection-ML algorithm pipelines.
NIMG-42. ANALYSIS OF TUMOR RELAPSE PROBABILITY AND OVERALL SURVIVAL PREDICTION FOLLOWING TEMOZOLOMIDE CHEMORADIATION USING FET PET INCLUDING RADIOMICS IN PATIENTS WITH GLIOBLASTOMA
Authors: Stetter Isabelle , Werner Jan-Michael , Wollring Michael , Ceccon Garry , Lohmann Philipp , Ciantar Keith George , Stoffels Gabriele , Mottaghy Felix M , Fink Gereon R , Langen Karl-Josef , Galldiks Norbert 
Published: November 2024

Abstract

BACKGROUND
Early after surgery and completion of first-line concomitant chemoradiation with temozolomide, the evaluation of tumor relapse probability and overall survival prediction is of considerable interest for the management of patients with glioblastoma

METHODS
Sixty-three newly diagnosed patients with glioblastoma (age range, 19-82) who received static and dynamic PET imaging using the radiolabeled amino acid O-(2-[18F]fluoroethyl)-L-tyrosine (FET) after surgery or biopsy and completion of concomitant chemoradiation with temozolomide were evaluated. Static FET PET parameters such as maximum and mean tumor-to-brain ratios (TBRmax, TBRmean) and metabolic tumor volumes (MTV), and the dynamic FET PET parameter time-to-peak (TTP) were obtained. Additionally, FET PET radiomics features (n=1,303) were extracted for each patient and a test-retest analysis was performed to identify robust features prior to feature selection. The prognostic value of FET PET parameters and radiomics features was evaluated using receiver-operating-characteristic (ROC) analyses with regard to a significantly prolonged progression-free and overall survival (PFS, OS). Subsequently, univariate and multivariate survival estimates were performed to assess the prognostic value of these parameters to predict a significantly longer PFS and OS

RESULTS
ROC analyses revealed that the parameter TBRmax was a powerful parameter to predict both a significantly longer PFS (20.3 vs. 8.9 months; P=0.002; threshold, 2.75) and OS (34.9 vs. 18.7 months; P=0.005; threshold, 2.75). MTV had a similar prognostic value for both PFS (16.7 vs. 7.2 months; P=0.002; threshold, 34.0 mL) and OS (30.6 vs. 14.8 months; P=0.002; threshold, 32.6 mL). After feature selection, 3 of 15 selected radiomics features predicted a significantly longer PFS and OS in univariate analyses. TBRmax, MTV, and one of 15 radiomics features remained significant in multivariate survival analysis (all P≤0.03)

CONCLUSION
Our results suggest that FET PET and radiomics parameters were highly prognostic in this group of patients at an early stage of first-line therapy.
NIMG-46. UNDERSTANDING PERMEABILITY CHANGES IN VESTIBULAR SCHWANNOMAS AS PART OF THE DYNAMIC RESPONSE TO RADIOSURGERY USING GOLDEN-ANGLE RADIAL SPARSE PARALLEL IMAGING, A RETROSPECTIVE STUDY
Authors: Meng Ying , Lee Matthew D , Berger Assaf , Wiggins Roy , O’Callaghan James , Bernstein Kenneth , Santhumayor Brandon , Block Kai Tobias , Fatterpekar Girish , Kondziolka Douglas 
Published: November 2024

Abstract
Vestibular schwannomas demonstrate different responses after stereotactic radiosurgery (SRS), commonly including a transient loss of internal enhancement on post contrast T1-weighted MRI thought to be due to an early reduction in tumor vascularity. We used dynamic contrast-enhanced based golden-angle radial sparse parallel (GRASP) MRI to characterize the vascular permeability changes underlying this phenomenon, with correlations to long-term tumor regression. Consecutive patients with vestibular schwannoma who underwent SRS between 2017-2019, had a transient loss of enhancement after SRS, and had long-term longitudinal GRASP studies (6, 12, and 24-36 months) were included in this retrospective cohort analysis (n = 19). Using GRAVIS, an analysis pipeline for GRASP studies, we extracted the key parameters normalized to the venous sinus from a region of interest within the tumor. The peak, area under the curve (AUC), and wash-in phase slope were significantly reduced at 6, 12, and 24 months after SRS (corrected p &lt; 0.05), even while the internal enhancement returned in the tumors. Larger pre-SRS tumors were more likely to have a greater reduction in peak (p = 0.013) and AUC (p = 0.029) at 6 months. In a subset of patients (N = 13) with long-term follow-up, the median percentage reduction in tumor volume was 58% at median 62 months. These patients showed a strong correlation between changes in peak, AUC, and wash-in phase slope at 6 months and tumor volume at last follow-up. After SRS and loss of internal contrast uptake within the vestibular schwannomas, a slow vascular permeability dynamic found on GRASP studies persisted, suggesting the internal development of scar tissue. Moreover, the degree of reduction in GRASP parameters at 6 months positively predicted the degree of tumor regression long-term.
NIMG-55. DEVELOPING A COMPUTATIONAL PIPELINE TO FORECAST AND SPATIALLY MAP HIGH-GRADE GLIOMA RESPONSE TO RADIOTHERAPY USING BIOPHYSICAL MODELS AND DATA-DRIVEN STATISTICAL ANALYSIS
Authors: Ty Sophia , Kowalski-Muegge Jeanne , Farhat Maguy , Panthi Bikash , Langshaw Holly , Shankar Mihir D , Talpur Wasif , Thrower Sara , Goldman Jodi , Custer James , Yankelov Thomas E , Chung Caroline , Hormuth, II David A 
Published: November 2024

Abstract
High-grade glioma (HGG) presents significant treatment challenges, particularly in identifying less responsive tumor areas to target with adaptive radiotherapy (RT). This study introduces a patient-specific computational pipeline designed to predict and spatially map HGG response to RT, focusing on regions demonstrating resilience to RT. Our computational pipeline is centered around a family of 3D biophysical models describing tumor cell invasion, proliferation, and response to RT which can be personalized for individual patients using longitudinal multiparametric MRI data. Utilizing longitudinal multiparametric MRI data reporting on cell density (diffusion weighted MRI) and the extent of disease (T2-FLAIR and contrast-enhanced T1-weighted MRIs) from 21 patients, ranging from baseline to three weeks into RT, we calibrated a family of biophysical models to forecast tumor cell density up to the final week of RT and one-month follow-up. By employing pareto tail analysis on empirical cumulative distribution function fits of voxel-wise changes in apparent diffusion coefficient maps (ΔADC) between the future images (end of RT, one-month follow-up) and pre-treatment images, we identified areas of increased tumor cellularity on both the ground truth and model predicted cellularity maps. The analysis differentiated tumor voxels into “non-responding” and “responding” categories, based on cutoffs determined through ROC analysis across various conditions—including assessments of both enhancing and non-enhancing tumor regions at different time points. Optimal ΔADC cutoffs for predicted and measured data were established, leading to high area under curve (AUC; ranging from 0.803 to 0.934) values and significant Youden’s index scores (ranging from 0.526 to 0.765) across all conditions. Our findings demonstrate the feasibility of this novel pipeline in delineating biologically relevant RT targets, offering a potential tool for enhancing the efficacy of adaptive RT strategies in treating HGG.
NIMG-57. EMBEDDED GRAPH DERIVED FROM PSEUDO-RESTING-STATE FUNCTIONAL MRI CAN PREDICT COGNITIVE IMPAIRMENT IN GLIOMA
Authors: Wang Chencai , Cho Nicholas , Van Dyk Kathleen , Sanvito Francesco , Oshima Sonoko , Yao Jingwen , Lai Albert , Salamon Noriko , Cloughesy Timothy , Nghiemphu Phioanh , Ellingson Benjamin 
Published: November 2024

Abstract
Resting-state brain network analyses are of great interest in studying the neurocognition and measuring the functional connectivity (FC) patterns of glioma patients. However, resting-state functional MRI (rs-fMRI) is not assessed in clinical routine due to the scan time and cost. The current study used pseudo-resting-state functional MRI (pseudo-rs-fMRI) derived from dynamic susceptibility contrast (DSC) perfusion MRI to predict cognitive impairment in glioma. 37 glioma patients were enrolled consecutively in the current study with DSC perfusion MRI acquired and neurocognition assessed, where 24 glioma patients also got rs-fMRI collected. The pseudo-rs-fMRI was created by voxel-wise subtracting the Gamma-variate modeled signal of contrast agent bolus from the original DSC perfusion signal. Following the pre-processing of pseudo-rs-fMRI and full rs-fMRI, the functional connectivity network (FCN) was established for each patient. Graph embedding was implemented to learn and extract features of each node in the binarized FCN, and the decision tree classification algorithm was applied to distinguish cognitive impairment. FCNs of 24 patients with DSC perfusion and rs-fMRI acquired were used to train the model, and FCNs of 13 patients with only DSC perfusion MRI acquired were used to test the model. The AUC (area under the receiver operating characteristic curve) was used to evaluate the model’s performance. The similarity and mean-square-difference between average FCNs extracted from rs-fMRI and pseudo-rs-fMRI were 0.6769 and 0.0263, respectively. Classifier trained using 24 FCNs of pseudo-rs-fMRI has an AUC of 0.750 in identifying cognitively impaired patients in the testing cohort. Combining FCNs of both rs-fMRI and pseudo-rs-fMRI to train the classifier resulted in a higher AUC of 0.833 in the testing cohort. To summary, DSC perfusion MRI-derived pseudo-rs-fMRI can be used to predict cognitive impairment in patients with gliomas. External validation would be required.
NIMG-62. SEX-DISTINCT TRANSCRIPTOMIC SIGNATURES UNDERLY MRI-DEFINED EDEMA PATTERNS IN HUMAN GLIOMAS
Authors: Jackson Pamela , Curtin Lee , Singleton Kyle , Mrugala Maciej , Zimmerman Richard , Bendok Bernard , Canoll Peter , Swanson Kristin 
Published: November 2024

Abstract
Magnetic resonance imaging (MRI) is key to clinically managing brain tumor patients, however connecting the biology to imaging remains challenging. We previously developed a two-compartment model of MRI signal intensity to quantitatively estimate relative edema abundance from T2-weighted MRIs. Using this model, we previously identified the fatty acid metabolism (FAM) and oxidative phosphorylation (OxPhos) pathways as sex-distinct, with both pathways amplified for high edema in males and low edema in females. The purpose of this project was to further delineate sex-distinct biology associated with MRI-estimated brain tumor edema abundance. We analyzed 179 multiregional samples (Female: 75; Male: 104) from 55 high grade glioma patients (Female: 21; Male: 34) for bulk RNA-Seq. Patients’ pre-surgical multiparametric MRIs were preprocessed and segmented for abnormal regions and normal tissue. Utilizing the segmentations and preprocessed images we estimated the relative fractions of extracellular and intracellular space based on the edema mathematical model. Samples were characterized by their edema scores, which were analyzed using differential expression for high and low edema, gene set enrichment analysis (GSEA) using MSigDB hallmarks, and leading edge interpretation. Based on transcriptomic leading edge analyses of high and low edema samples from sex-separated cohorts, we identified common and sex-distinct enrichment of the FAM and OxPhos pathways underlying edema patterns. Of the OxPhos pathway leading edge genes, 45 were common, 36 were unique to females, and 66 were unique to males. Of the FAM pathway leading edge genes, 8 were common, 39 were unique to females, and 29 were unique to males. Notably, expression of both IDH1 and IDH2 were increased for males in regions of high edema in the OxPhos pathway. IDH3a was decreased for females in regions of low edema in the OxPhos pathway. These data suggest that there may be sex-distinct metabolism underlying MRI measurable edema formation.
NIMG-66. DEFINING PROGRESSIVE DISEASE: LOCALIZING VOXEL-AT-RISK ON MAGNETIC RESONANCE IMAGING USING DEEP LEARNING
Authors: Freeman Nicolas , Pan Ian , Youssef Gilbert , Wen Patrick , Huang Raymond 
Published: November 2024

Abstract
Follow-up magnetic resonance imaging (MRI) of post-treatment glioblastoma (GBM) can be diagnostically challenging in differentiating progressive disease (PD) from coexisting treatment-related changes. The study aim was to train a machine learning model using multiple MRI timepoints from treated GBM patients with subsequent PD or at least six months of tumor-like, persistently stable disease (SD) to estimate PD risk. A retrospective, post-treatment GBM cohort (n=189) with 453 timepoints was identified with available MRI sequences (T1-weighted with and without contrast, T2-weighted, and fluid-attenuated inversion recovery). PD cases (n=154) showed new, enhancing PD followed by death within 3-6 months; timepoints included the MRI on the PD diagnosis date and the one directly prior. SD cases (n=35) exhibited stable, tumor-like enhancement over timepoints. Cases underwent voxel-wise annotation of PD versus SD. A multiclass, three-dimensional nnU-Net was trained to label voxels as PD, SD, or neither using timepoint sequences with five-fold cross-validation. Model performance was assessed using the ratio of predicted PD/SD voxels compared to each case’s actual PD or SD status. Classification accuracy of PD versus SD status, PD case-wise sensitivity and specificity, and a Receiver Operating Characteristic (ROC) analysis using the PD/SD ratio were presented. The five-fold model ensemble was evaluated on a holdout test set. Validation set performance showed 74.3% accuracy, 0.73 PD case-wise sensitivity, and 0.80 specificity. The holdout set exhibited 79.8% accuracy, 0.81 sensitivity, and 0.78 specificity for PD cases. The ROC analysis showed an area under the curve of 0.80 and 0.83 on the validation and holdout sets, respectively. The model showed high performance and classification accuracy in predicting which voxels were at risk of PD in post-treatment GBM cases. The model also differentiated between PD and SD regions. The model’s predicted PD/SD ratio may be clinically used to stratify patients’ PD risk.
NIMG-70. COMPARISON OF VOLUMETRIC AND CROSS-SECTIONAL MEASUREMENTS IN DIFFUSE MIDLINE GLIOMAS: RETHINKING RESPONSE ASSESSMENT
Authors: Khalili Neda , Khalili Nastaran , Gandhi Deep , Franson Andrea , Jain Paarth , Anderson Hannah , Tu Wenxin , Haldar Shuvanjan , Familiar Ariana , Storm Phillip , Ware Jeffrey , Resnick Adam , Vossough Arastoo , Nabavizadeh Ali , Fathi Kazerooni Anahita 
Published: November 2024

Abstract

BACKGROUND
The Response Assessment in Pediatric Neuro-Oncology (RAPNO) criteria, which are based on bidimensional or cross-sectional product (CP) measurements of tumor size, are the standard for assessing treatment response in diffuse midline glioma (DMG). This study aims to investigate the correlation between CP and volumetric measurements in progressive disease (PD) and compare their prognostic impact in pediatric patients with DMG.

METHODS
We retrospectively reviewed clinical and imaging data from 27 subjects with newly diagnosed DMG, encompassing 16 subjects from the PNOC003 and PNOC007 clinical trials and 11 subjects from the Children’s Hospital of Philadelphia. Tumors were segmented using a pretrained pediatric-specific autosegmentation model followed by manual revisions. Tumor volume and CP measurements were automatically derived from the outlined tumor. Cox regression analysis was used to compare the performance of CP and volumetric measurements in predicting overall survival (OS). The correlation between CP and volumetric thresholds for PD was assessed by linear regression. Comparison of survival of patients classified as PD versus non-PD by CP and volumetric measurements at 7 months post-baseline scan was performed using log-rank analysis.

RESULTS
Cox regression analysis demonstrated that volumetric measurements were significantly associated with OS (p=0.04), whereas CP measurements were not a significant predictor (p=0.07). An approximate 55% increase in segmented volume corresponded to a 25% increase in CP, which is the definition of PD according to RAPNO criteria (R2 = 0.71). At 7 months, volumetric classification of PD more accurately predicted survival than the CP method (p=0.06 for volumetric vs 0.14 for CP).

CONCLUSION
Our study showed that volumetric measurements are better predictors of survival compared to bidimensional measurements. We are extending this analysis to a larger sample size, examining survival at additional time points, and incorporating other factors influencing OS, such as treatment approaches.