bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-52. CORRELATION OF BIDIMENSIONAL AND VOLUMETRIC TUMOR SIZING IN NEWLY DIAGNOSED AND RECURRENT GLIOBLASTOMA
Authors: Kim John , Nelson Thomas , Youssef Gilbert , Rahman Rifaquat , Arnaout Omar , Bi Wenya Linda , Cagney Daniel , Chang Yuh-Shin , DeSalvo Matthew , Flood Thomas , Gerstner Elizabeth , Castro Luis Nicolas Gonzalez , Guenette Jeffrey , Kim Albert , Lee Eudocia , McFaline-Figueroa Jose Ricardo , Potter Christopher , Reardon David , Wen Patrick , Huang Raymond Y 
Published: November 2023

Abstract

BACKGROUND
Glioblastoma (GBM) is the most common malignant primary nervous system tumor and remains incurable with a poor prognosis despite current therapy. The standard for monitoring response to treatment, as defined by the Response Assessment in Neuro-Oncology (RANO) criteria, relies on a bidimensional (2D) measurement product of T1-gadolinium enhancing disease. However, due to the variability of bidimensional tumor characterization, there is considerable interest in volumetric segmentation to assess tumor burden, which could be implemented using machine learning models offering workflow automatization.

METHODS
In this study, we evaluate a commercially available automated tumor segmentation tool and correlate its volumetric output to the manual assessment of bidimensional 2D enhancing tumor burden by experts. MRI examinations during systemic treatment for two retrospective cohorts of patients with either newly diagnosed (nGBM) or recurrent GBM (rGBM) were assessed. The 2D diameter product in cm^2 of enhancing tumor was determined by blinded readers and volumetric segmentations in cm^3 were automatically generated. Spearman's correlation between the manual 2D and automated volume measurements was calculated for each cohort.

RESULTS
315 patients with nGBM, and 482 with rGBM were evaluated, comprising 3073 total brain MRIs in the nGBM cohort and 2439 MRIs in the rGBM cohort. Spearman's correlation between the manual 2D and automated volumetric measurements was 0.7463 (0.730, 0.762) in the nGBM cohort and 0.8598 (0.849, 0.870) in the rGBM cohort.

CONCLUSION
Automated segmentation tools have the potential to revolutionize radiology workflows and increase sensitivity to subtle changes on imaging. However, the differential performance of this volumetric assessment confirms the need for further refinement and customization of such automated tools to account for tumor- and patient-specific factors.
NIMG-56. GENERATIVE ADVERSARIAL NETWORKS FOR BRAIN MRI SYNTHESIS: IMPACT OF TRAINING SET SIZE ON CLINICAL APPLICATIONS
Authors: Zoghby Marc , Erickson Bradley , Conte Gian Marco 
Published: November 2023

Abstract
Incomplete MRI exams reduce the possibility of applying AI models. We previously trained two generative adversarial networks (GANs) on 135 subjects from the BraTS-2017 dataset to generate T1 from post-contrast T1 and FLAIR from T2 sequences (baseline models). To test the impact of the training set size, here, we trained the same models using 1251 subjects from the BraTS-2021 dataset. We used the same architecture and hyperparameters from the baseline models: batch size of 1, learning rate 2x10-4, Adam optimizer, cross-entropy and L1-loss. We trained two versions of the updated models: one stopped at an early checkpoint (early models) and one after 50 epochs (late models). We tested all models on institutional MRIs from 487 IDH-wt gliomas. Synthetic MRIs were compared to the originals using the structural-similarity-index (SSI) and MSE. We simulated scenarios where either the T1, FLAIR, or both were missing and used their synthetic version as inputs of a segmentation model. We compared the segmentations using the dice score (DSC) and used Friedman and Dunn’s test to compare the scores and correct for multiple comparisons. Median SSI for the T1 were 0.957, 0.947, 0.947, and median MSE were 0.006, 0.014, 0.008 for the baseline, early, and late models. For the FLAIR, median SSI were 0.924, 0.908, 0.915, and median MSE were 0.016, 0.016, 0.019 for the baseline, early, and late models. The DSC ranges for the baseline and updated models were (0.655 - 0.953), (0.420 - 0.952), and (0.610 - 0.952). The baseline and late models showed no statistically significant differences in DSC, but both performed significantly better than the early models. We show that GANs trained to synthesize brain MRIs on a small cohort perform similarly to those trained on an x10-larger cohort, making them a viable option for rare diseases or institutions with limited resources.
NIMG-57. PERFORMANCE OF DEEP LEARNING IN MGMT PROMOTER METHYLATION STATUS PREDICTION USING BRAIN MRI: RESULTS FROM A LARGE COHORT OF IDH-WILDTYPE GLIOMAS TESTED BY A SINGLE METHYLATION ASSAY
Authors: Conte Gian Marco , Moassefi Mana , Faghani Shahriar , Sanvito Francesco , Decker Paul , Kizilbash Sani H , Eckel-Passow Jeanette , Ida Cristine , Erickson Bradley 
Published: November 2023

Abstract
There have been several attempts to predict the MGMT promoter methylation status of gliomas using deep learning (DL), with mixed results. These studies did not account for IDH mutation status and did not utilize a uniform methylation assay, introducing potential confounders in the experimental design. We assessed the performance of DL applied to brain MRI to predict the MGMT promoter methylation status of IDH-wildtype gliomas tested with a standardized assay. We included 539 IDH-wildtype gliomas tested with real-time methylation-specific PCR from 2017-2022; 205 (38%) were MGMT promoter methylated. The assay targets 8 downstream MGMT CpG sites using ACTB as control; methylation status is determined based on the difference of PCR crossing-point values: methylated if difference &lt; 5, unmethylated if difference &gt; 7, indeterminate otherwise. We used post-contrast T1 and T2 MRIs as inputs for our models trained using stratified 5-fold cross-validation, 400 epochs, batch size of 16, a learning rate scheduler starting at 10-3, weighted binary cross-entropy loss, and AdamW optimizer. We repeated the experiments using two 3D DL architectures (DenseNet121 and EfficientNetB0) and two image preprocessing pipelines: the BraTS protocol and one that uses padding instead of registration to an MRI atlas, keeping constant other experimental variables. For Densenet121, the best cross-validation AUROCC and F1-score (median, median absolute deviation) were 0.67 (0.06), 0.59 (0.02) using the BraTS protocol, and 0.67 (0.06), 0.58 (0.01) using padding. For EfficientNetB0, were 0.67 (0.02), 0.66 (0.01) using the BraTS protocol and 0.67 (0.01), 0.68 (0.01) using padding. Our findings suggest that it is unlikely that DL models trained on brain MRI can predict the MGMT promoter methylation of IDH-wildtype gliomas. Importantly, our study is the first to focus on an IDH-wildtype adult diffuse glioma cohort tested with a single, standardized MGMT methylation assay, eliminating the variability introduced by different assays and IDH status.
NIMG-59. RADIOMIC FEATURE CLUSTER ANALYSIS OF IDH-MUTANT GLIOMA SUBTYPES
Authors: Willms Klara , Chadha Saahil , von Reppert Marc , Ramakrishnan Divya , Lost Jan , Tillmanns Niklas , Jekel Leon , Merkaj Sara , Aboian Mariam 
Published: November 2023

Abstract

INTRODUCTION
IDH-mutant glioma subtypes share similarities on MRI and histopathology but have distinct differences in survival based on their molecular subtypes. Radiomic features provide novel analysis of glioma imaging phenotypes that may not appear in clinical evaluations. We evaluated whether clustering of radiomic features of IDH-mutant gliomas could identify different subtypes based on the 2021 WHO classification.

METHODS
We extracted up to 5,763 radiomic features per case from pretreatment FLAIR and T1 post-contrast tumor segmentations of 206 patients with IDH-mutant gliomas WHO grade 2-4 using a standardized Pyradiomics 3.1.0 pipeline. Segmentations were performed using a semiautomatic workflow with a UNETR algorithm trained on the BRaTS 2021 dataset. To visualize high-dimensional feature space, we evaluated the following unsupervised clustering algorithms to differentiate tumor subtypes: PCA, t-SNE, UMAP, and PHATE. We performed redundancy reduction by calculating a Pearson correlation-matrix, and removed highly correlated features above a threshold of 0.7. Significant features were selected using the L1-norm regularization (LASSO).

RESULTS
The dataset of 206 patients included 19 grade 4, 28 grade 3, and 36 grade 2 astrocytomas as well as 23 grade 3 and 58 grade 2 oligodendrogliomas. The UNETR algorithm performed with a 0.85 Dice score on the segmentations. Clustering of the extracted 2D radiomic features from FLAIR and T1postgad using PCA, tSNE, UMAP, and PHATE did not identify distinct classes. To further analyze the two tumor entities, we applied XGBOOST, RandomForest, and KNN classifiers and found them to have AUCs &lt; 0.5.

CONCLUSIONS
We showed that the spectrum of imaging features of IDH-mutant gliomas has a significant overlap on FLAIR and T1 post-contrast imaging. We propose that standard anatomical imaging does not provide sufficient information on these two sequences. To further identify specific subtypes of IDH-mutant gliomas, additional sequences, such as T2, ADC, SWI, and T1, can provide additional information.
NIMG-80. SKULL BASE CHORDOMA: CORRELATION OF18F-FDG PET/CT FINDINGS WITH PATHOLOGICAL FEATURES AND CLINICAL COURSE
Authors: Sakaguchi Yusuke , Tanaka Shota , Takami Hirokazu , Shin Masahiro , Hasegawa Hirotaka , Takao Hidemasa , Takayanagi Shunsaku , Saito Nobuhito 
Published: November 2023

Abstract

OBJECTIVE
This study aimed to evaluate 18F-fluorodeoxyglucose (FDG)-PET/CT findings, histopathological results, and clinical outcomes in patients with skull base chordoma.

METHODS
This retrospective study included patients who underwent first or repeat excision/biopsy for skull base chordoma at our institution between 2010 and 2019, with preoperative FDG-PET/CT. We assessed the SUVmean, SUVmax, lesion to white matter SUVmax/SUVmean ratio (L/N ratio), MIB-1 index, and occurrence of postoperative recurrence and metastasis. Statistical analysis was performed using Spearman's rank correlation coefficient and the Mann-Whitney U test.

RESULTS
Of the 17 participants, 52% were male, with a median age of 49 years (Interquartile Range: IQR 32-69). Among them, 41% were primary cases, and the median follow-up period was 51 months (IQR 32-77). The median SUVmax was 5.2 (IQR 3.9-6.2), SUVmean was 3.6 (IQR 3.1-5.1), L/N ratio was 1.5 (IQR 1.2-2.3), and MIB-1 index was 7.5% (IQR 2-10). Only SUVmax demonstrated a significant positive correlation with the MIB-1 index (ρ=0.556, p=0.021). Out of the 16 surgeries, 11 (69%) resulted in recurrence, while 4 of the total 17 cases (24%) showed metastasis. There were no significant differences in SUVmax, SUVmean, L/N ratio, and MIB-1 index between recurrence and non-recurrence groups. However, the MIB-1 index showed significant difference between metastasis and non-metastasis groups (p=0.0055).

CONCLUSION
In skull base chordoma, there was a significant correlation between the SUVmax on FDG-PET and the MIB-1 index. The MIB-1 index was significantly higher in the metastasis group. However, no significant correlation was found between SUVmax and distant metastasis, possibly due to the small sample size.
NIMG-87. PEDIATRIC BRAIN TUMOR SEGMENTATION INITIATIVE AT CHILDREN’S BRAIN TUMOR NETWORK (CBTN); PROJECT UPDATE ON IMPROVED PERFORMANCE AND FUTURE PROSPECTS
Authors: Kazerooni Anahita Fathi , Khalili Nastaran , Haldar Debanjan , Viswanathan Karthik , Familiar Ariana , Bagheri Sina , Anderson Hannah , Mahtabfar Aria , Storm Phillip , Davatzikos Christos , Resnick Adam , Ware Jeffrey B , Vossough Arastoo , Nabavizadeh Ali 
Published: November 2023

Abstract
Pediatric brain tumors (PBT) exhibit significant heterogeneity, and accurate assessment of tumor response is crucial for patient management. While bidimensional measurements of tumor size are commonly used, they may underestimate tumor dimensions. Our group previously developed a multi-institutional, and multi-histology model on the Children’s Brain Tumor Network (CBTN) imaging dataset to segment tumor subregions, recommended by the Response Assessment in Pediatric Neuro-Oncology (RAPNO) working group. In the most recent update, we utilized a self-configuring deep learning architecture called nnU-Net on a large dataset of well-annotated PBTs (233 training, and 60 withheld internal and 46 external test sets). The model was trained and validated on multiparametric MRI sequences and differentiated various tumor subregions, including enhancing tumor (ET), nonenhancing tumor (NET), edema (ED), and cystic component (CC) and the whole tumor (WT) regions. Clinical validity was assessed by comparing the volumes of tumor subregions predicted by the model with expert manual segmentations, demonstrating strong agreement, with statistically significant Pearson’s correlation coefficients of 0.93/0.69 for ET, 0.94/0.93 for NET, 0.78/0.93 for CC, and 0.94/0.50 for ED subregions (p&lt; 0.001) for the internal/external test cohorts. The trained model showed excellent performance for the withheld internal test set and decent accuracy for the external test set. Median Dice scores for internal/external test sets were 0.94±0.10/0.90±0.07 for WT, 0.85±0.33/0.84±0.30 for ET, 0.80±0.32/0.64±0.31 for NET, 0.79±0.37/0.67±0.33 for CC, 0.70 ±0.42/0.37±0.43 for ED, and 0.86±0.19/0.80±0.21 for all nonenhancing components (combination of NET, CC, and ED), respectively. The proposed automated segmentation method provides accurate and reproducible volumetric measurements of RAPNO-defined subregions in PBTs. It exhibits robust performance and potential generalization to external datasets and the accuracy is higher compared to the previous DeepMedic model. We will continue to improve the model by constantly adding new training data to further improve the performance and sustain model robustness.
NIMG-88. THALLIUM 201 SINGLE PHOTON EMISSION CT (TI-SPECT) FOR DIFFERENTIATION OF MALIGNANT VERSUS BENIGN NERVOUS SYSTEM SPACE OCCUPYING LESIONS: A RETROSPECTIVE STUDY, SYSTEMATIC REVIEW, AND META-ANALYSIS
Authors: Mondia Mark Willy , Alonto Anisah Hayaminnah , Pang Nicole Girlyn , Resma Francis Manuel , Gacula John Kenneth , Pauco Arnel , Chua Annabell , Batara Julette Marie 
Published: November 2023

Abstract
Importance Thallium-201 single-photon emission computed tomography (TI-SPECT) imaging has been used historically to distinguish malignant cerebral neoplasms from infectious etiologies. Objectives Our study aims to review existing literature and conduct a retrospective study and meta-analysis on the use of TI-SPECT to differentiate malignant from non-malignant nervous system lesions. Data Sources A retrospective study on use of TI-SPECT in differentiating malignant versus benign nervous system masses was conducted in two tertiary hospitals in the Philippines. Systematic review of relevant studies regarding TI-SPECT for brain and spinal cord lesions was also done. Meta-analysis was performed to generate pooled sensitivity and specificity. Study Selection All study types published in the English language were included – randomized controlled trials (RCTs), prospective/retrospective cohort studies, and case series and reports; abstract only studies were excluded. Studies that involved either pediatric and adult cases were considered. Data Extraction and Synthesis The study complied with the Meta-analysis of Observational Studies in Epidemiology (MOOSE) and Preferred Reporting Items for Systematic reviews and Meta-analyses (PRISMA) Guidelines. Main Outcome and Measure The primary outcome was sensitivity and specificity of TI-SPECT. This involved getting correlation of positive and negative thallium scan uptake with either biopsy results (if available) or diagnosis via clinical response to treatment (i.e. whether cases that were managed as infection or demyelination showed improvement on follow-up). RESULTS: Thirty-two (32) articles were qualitatively described and 25 datasets were subjected to meta-analysis. Pooled sensitivity and specificity were 0.89 (95% CI: 0.82-0.94) and 0.82 (95% CI: 0.74-0.88), respectively with an area under the curve (AUC) for diagnostic accuracy of 0.92. CONCLUSION: and Relevance Thallium scans may be utilized to discriminate malignant from benign nervous system lesions, especially, in cases with a &gt;80% pre-test probability. Further studies are needed to validate this findings.
NCMP-08. TREATMENT OF PITUITARY METASTASIS: A SINGLE CENTER EXPERIENCE AND SYSTEMATIC LITERATURE REVIEW
Authors: Begley Sabrina , Yang Kaiyun , Ye Vincent , Lynch Daniel , Gutierrez Enrique , Bernstein Mark , Zadeh Gelareh , Shultz David , Kongkham Paul 
Published: November 2023

Abstract

PURPOSE
To understand the natural history and optimal treatment strategy for pituitary gland metastasis.

METHODS
We performed both a retrospective chart review of patients treated at our institution and a scoping review of the topic.

RESULTS
The retrospective review identified seven patients with an average age of 59.6. Primary histologies included breast cancer (4), melanoma (1), renal carcinoma (1), and sarcoma (1). Two patients had anterior pituitary endocrine dysfunction, one of whom was the only patient with visual symptoms. All patients were treated with radiosurgery and two also underwent surgical resection. Overall survival ranged from 6.5 – 117 months. We identified 166 patients from 71 studies in our literature review. The most common primary cancer was lung (27.7%), followed by breast (18.7%) and renal (14.5%) cancer. 107 presented with endocrine dysfunction; with 41 cases of diabetes insipidus and 55 cases of hypopituitarism. 110 presented with visual compromise. 107 patients received radiotherapy, 96 underwent surgical resection and 44 received systemic chemotherapy/immunotherapy. Surgery was significantly associated with an increased likelihood of vision improvement and a decreased likelihood of endocrine normalization. Radiographic regression predicted visual improvement. Median overall survival was 9.9 months (range: 0.2 – 96).

CONCLUSIONS
Our scoping review showed that both radiosurgery and surgical resection have been frequently used to treat pituitary metastases with good response. Vision improvement is more likely to happen following surgical resection. Despite treatment and radiographic response, patient survival remains less than a year.
NCMP-09. THE CURRENT LANDSCAPE OF SPINE ONCOLOGY SURGICAL TRIALS AND FUTURE OPPORTUNITIES
Authors: Schupper Alexander , Patel Shrey , Steinberger Jeremy , Germano Isabelle 
Published: November 2023

Abstract

BACKGROUND
Clinical trials have allowed the discovery of successful therapeutic intervention for cancer patients. The current number of cancer patients in the US has increased with an incidence of 442/100,000 population and yet with decreased yearly mortality rate. Spine metastatic disease occurs in approximately 90% of patients with cancer. The aim of this study is to analyze the current landscape of spine oncology trials to identify future opportunities.

METHODS
To assess active and ongoing clinical trials in the treatment of metastatic spinal disease, the authors queried https://clinicaltrials.gov/ under the searches “Spine Metastases” and “Spine Tumor.” Only clinical trials designated as “Recruiting” were included. Parametric and non-parametric statistical analysis was performed.

RESULTS
Our initial search yielded to 100 total studies; 18 clinical trials met entry criteria. Trial type was 16/18 therapeutic, 2/18 observational and trial design 6/18 (33%) randomized. All 18 trials were single center. Trials were were based in the United States (90%) and British Columbia, Canada. Across US geographical quadrants, a significant higher number of therapeutic trials was found in the South quadrant (12/16) compared to the other quadrants, (p&lt; 0.001; Midwest: N=3/16, Northeast: N=1). The most common therapeutic focus included radiosurgery, followed by vertebral body augmentation and/or radiofrequency ablation, and laser interstitial thermal therapy (LITT).

CONCLUSION
Clinical trials are a cornerstone for the advancement oncological treatment. Our study shows that currently there is an ample opportunity to design clinical trials for our growing population of metastatic spine disease patients. Successful strategies to enhance the needed portfolio of spinal oncology trials, include focusing on including all US geographic quadrants. As new trials are designed, continued efforts devoted to data entry in national registry remains important.
NCMP-22. FLUDARABINE NEUROTOXICITY IN PATIENTS UNDERGOING CAR T-CELL THERAPY - A REPORT OF 2 CASES
Authors: Li Yi , Drappatz Jan , Mantica Megan 
Published: November 2023

Abstract
Fludarabine is a purine analog used in the treatment of chronic lymphocytic leukemia (CLL) and as part of immunosuppressive/lymphodepleting regimens in allogeneic stem cell transplant and chimeric antigen receptor (CAR) T-cell therapy. It induces cellular cytotoxicity through multiple mechanisms, ultimately leading to the inhibition of DNA synthesis. Although myelosuppression is the primary dose-limiting side effect of fludarabine, there have been reported cases of delayed-onset neurological symptoms, such as progressive visual impairments, dementia, ataxia, hemiparesis, quadriparesis, and blindness. These symptoms have been observed in patients receiving fludarabine alone or as part of preconditioning treatments for CAR T-cell therapy. The neurological abnormalities are characterized by leukoencephalopathies and can manifest as posterior reversible encephalopathy (PRES), acute toxic leukoencephalopathy (ATL), or other leukoencephalopathy (OLE). We present two cases of patients who underwent fludarabine conditioning before CAR T-cell therapy, exhibiting late-onset and progressive changes in vision and encephalopathy. These symptoms differ in timing and pattern from the neurotoxicities commonly associated with CAR T-cell therapy. Nonetheless, given the overlapping neurological toxicities seen with both CAR-T cell therapy and fludarabine, it is important to consider implementing thorough screening measures to modify preconditioning fludarabine dose intensity, particularly for patients with advanced disease, renal insufficiency, or other risk factors to limit fludarabine-associated adverse effects.