bluuebunny/crossref_metadata_2025_split huggingface dataset:
PATH-36. GENOMIC MARKERS OF RECURRENCE RISK IN ATYPICAL MENINGIOMA FOLLOWING GROSS TOTAL RESECTION
Authors: Vaubel Rachael , Kumar Rahul , Weiskittel Taylor , Jenkins Sarah , Dasari Surendra , Uhm Joon , Lachance Daniel Honore , Brown Paul , Van Gompel Jamie , Kipp Benjamin , Sukov William , Giannini Caterina , Johnson Derek , Raghunathan Aditya 
Published: November 2022

Abstract
Atypical meningiomas (CNS WHO grade 2) show an intermediate risk of recurrence/progression, and molecular parameters informing management following gross total resection (GTR) remain to be established. We performed comprehensive genomic analysis in 63 patients who underwent GTR of an atypical meningioma. Median age was 59.8 years, 41 patients (65.1%) were female, and 8/62 patients (12.9%) underwent radiation therapy following resection. Follow-up was available for all patients (median 10 years), with recurrence/progression in 17 (5-year PFS: 84.2%; 10-year PFS: 71.0%). Tumors were evaluated by next generation sequencing (n=61), chromosomal microarray (n=63), DNA methylation profiling (n=62), and H3K27me3 immunohistochemistry (n=62). Next generation sequencing using a CLIA-certified 50-gene Neuro-Oncology specific panel identified pathogenic alterations in NF2 (n=31; 50.8%), SMARCB1 (n=5; 8.2%), AKT1 (n=2; 3.3%), TERT (n=1, 1.6%), SMO (n=1), SUFU (n=1), TP53 (n=1), and PTEN (n=1). There was trend toward decreased PFS in patients with NF2 alterations (p=0.078) which was not statistically significant. Copy number variants (CNVs) were identified using OncoScan (ThermoFisher). The most frequent CNVs were loss -22 (n=45; 71.4%), -1p (n=30, 47.6%), -14 (n=21, 33.3%), -6q (n=18, 28.6%), -10q (n=15, 23.8%), -X (n=13, 20.6%), and -18 (n=11, 17.4%). CNVs associated with decreased PFS included -1p (p=0.015), -7p (p=0.007), -10q (p=0.0007), and -18 (p=0.0376). DNA methylation profiling was performed using the Illumina MethylationEPIC array (n=62) and evaluated using the DKFZ classifier (v12.3). Based on the highest score, tumors matched to benign (n=32; 51.6%) or intermediate (n=29; 46.7%) meningioma subclasses or showed non-contributory profiles (n=1; 1.6%). Methylation class (benign vs. intermediate) showed no association with PFS (p=0.96). H3K27 trimethylation (H3K27me3) was unequivocally lost in 4 (6.5%) tumors, which was insufficient for PFS analysis. Overall, our study supports CNV profiling of atypical meningiomas following GTR, which can be implemented using existing, clinically validated technologies. Molecular predictors could guide use of radiotherapy in these patients.
PATH-37. SPATIAL GENE EXPRESSION PROGRAMS AND PROTEIN SIGNALING MECHANISMS DRIVE MENINGIOMA EVOLUTION AND THERAPEUTIC VULNERABILITY
Authors: Lucas Calixto-Hope , Vasudevan Harish , Seo Kyounghee , Chen William , Liu S John , Choudhury Abrar , Magill Stephen , Solomon David A , Perry Arie , Raleigh David 
Published: November 2022

Abstract
Intratumor heterogeneity drives cancer evolution and resistance to therapy, but unbiased approaches to elucidate mechanisms driving intratumor heterogeneity have been lacking. Here, we integrate spatial gene expression programs and protein signaling mechanisms across 16 meningioma samples to define how intratumor heterogeneity drives molecular classification, temporal evolution, or spatial evolution of the most common primary intracranial tumor. Spatial transcriptomic analysis was performed on 38,718 regions and spatial profiling of 72 proteins comprising proliferation, stress, microenvironment, immune, or signaling modules was performed on 82 regions. DNA methylation, copy number variant, targeted gene expression profiling, targeted DNA sequencing, histologic, or immunohistochemical analyses (Ki67, H3K27me3, p16) were performed on all meningiomas to study intratumor heterogeneity in the context of pre-existing classification schemes. Primary meningioma cells, CRISPR interference, pharmacology, and 3D co-culture models were used for mechanistic and functional validation. Spatial analyses revealed significant intratumor or intertumor heterogeneity irrespective of meningioma histologic subtype or grade, DNA methylation group (Merlin-intact, Immune-enriched, Hypermitotic), copy number variant (1p loss, 1q gain, 6p gain, 9p loss, 14q loss, 22q loss), gene expression risk score, or driver mutation (NF2, CDKN2A/B, TERT promoter, BAP1, SMARCB1, ARID1A). Spatial analyses on paired histologically or molecularly distinct regions from individual meningiomas (n=4), or on paired primary and recurrent meningioma samples (n=9), revealed conserved proliferation, immune, or signaling mechanisms underlying meningioma evolution. Mechanistic and functional studies validated therapeutic vulnerabilities across spatial clusters to combinations of FDA-approved small molecules inhibiting the cell cycle (abemaciclib), epigenetic regulators (vorinostat), the DNA damage response (nariparib), MAPK signaling (erlotinib, selumetinib), or PI3K-AKT signaling (copanlisib). In summary, these data define spatial gene expression programs and protein signaling mechanisms driving meningioma evolution and therapeutic vulnerability, shedding light on past clinical failures and elucidating novel combinations of concurrent or sequential molecular therapies to treat meningiomas that are resistant to standard interventions.
NIMG-01. PREDICTING POST-STEREOTACTIC RADIOTHERAPY MAGNETIC RESONANCE IMAGE OUTCOMES OF BREAST CANCER METASTASES TO THE BRAIN
Authors: Pandey Shraddha , Kutuk Tugce , Mills Matthew , Abdalah Mahmoud , Stringfield Olya , Latifi Kujtim , Moreno Wilfrido , Ahmed Kamran , Raghunand Natarajan 
Published: November 2022

Abstract

BACKGROUND
Stereotactic radiosurgery (SRS) is a cornerstone in the management of Breast Cancer Metastases to the Brain (BCMB). While control rates are high following SRS, radiation necrosis is a rare but potentially devastating long-term toxicity. There is a clinical need for automated/semi-automated methods to assess tumor response and optimize the RT plans for local control with minimal long-term toxicity. Multiparametric MRI (mpMRI), particularly Apparent Diffusion Coefficient of water (ADC) maps, contain information that is mechanistically relatable to voxel-level tumor response to RT. We report a deep learning-based approach to predict post-SRS ADC maps, FLAIR, T2-weighted (T2W), T1-weighted unenhanced (T1W) and contrast-enhanced (T1WCE) images, from pre-SRS T1W, T1WCE, T2W and FLAIR images, ADC maps, and the delivered RT dose map. These “forward models” will enable the radiation oncologist to simulate radiologic outcomes and iteratively optimize RT plans for local control with minimal toxicity.

METHODS
We trained a variant of the pix2pix Generative Adversarial Network (GAN) on MRI and RT dose map data from 18 BCMB patients treated with stereotactic radiation with confirmed controlled and locally recurrent metastases. Patients were treated with stereotactic radiation dose of 1-40 Gy between 2013-2019.

RESULTS
On test data from 6 BCMB patients, the trained forward model predicted post-SRS ADC values within the Gross Tumor Volume (GTV) that were broadly in agreement with ground truth post-SRS ADC maps. In agreement with expectations, the forward model also predicts increasing post-RT ADC within the GTV with increasing simulated RT doses in the range of 1-71 Gy. We have also explored an inverse model to predict the RT dose map required to produce “prescribed” post-SRS ADC values within the GTV.

CONCLUSIONS
We envision that the forward models will assist the radiation oncologist in initial RT dose plan optimization, while the inverse model may be useful for daily RT plan optimization.
NIMG-02. PACS-INTEGRATED AUTO-SEGMENTATION WORKFLOW FOR BRAIN METASTASES USING NNU-NET
Authors: Jekel Leon , Bousabarah Khaled , Lin MingDe , Merkaj Sara , Kaur Manpreet , Avesta Arman , Aneja Sanjay , Omuro Antonio , Chiang Veronica , Scheffler Björn , Aboian Mariam 
Published: November 2022

Abstract

PURPOSE
Monitoring metastatic disease to the brain is laborious and time-consuming, especially in the setting of multiple metastases and when performed manually. Response assessment in brain metastases based on maximal unidimensional diameter as per the RANO-BM guideline is commonly performed1, however, accurate volumetric lesion estimates can be crucial for clinical decision-making2 and enhance outcome prediction3. We propose a deep learning (DL)-based auto-segmentation approach with the potential for improvement of time-efficiency, reproducibility and robustness against inter-rater variability. Materials and

METHODS
We retrospectively retrieved 259 patients with a total number of 916 lesions from our institutional database from 2014 - 2021. Patients with prior history of local radiation therapy or surgery were excluded. Manually generated trainee segmentations were revised and adjusted by a board-certified radiologist and served as ground truth for evaluation of segmentation accuracy. Model performance was tested via dice-similarity-coefficient (DSC). Volumetric measurements were then obtained within our PACS-integrated workflow on Visage 7 (Visage Imaging, Inc., San Diego, CA) at the click of one button.

RESULTS
For model training and evaluation, a train-test split of 90:10 on patient-level was performed (n= 234:25 (Patients), n= 861:55 (Lesions). A DL-algorithm (nnUNet) was incrementally trained on 10 batches of 23 patients. The DSC of the U-Net gradually increased throughout the training process and heuristically reached a plateau of 0.85. The sensitivity of the algorithm was 83% with detection of 46 out of 55 lesions in the testing dataset. The lesions that were not detected by the algorithm were below 5 mm in size. The false positive rate was 8% (n=4/50).

CONCLUSION
Our study demonstrates the feasibility of PACS-based integration of automatized segmentation workflows of brain metastases. An incremental-training approach is recommended to adapt DL algorithms to specific hospital settings.
NIMG-03. PREOPERATIVE DEXAMETHASONE LEADS TO SIGNIFICANT REDUCTIONS IN PERITUMORAL EDEMA FOR PATIENTS WITH BRAIN METASTASES
Authors: Tan Hao , Bowden Stephen , Cheaney Barry , Richie Emma , de Leeuw Charles , Wood Matthew , Raslan Ahmed , Han Seunggu , Barajas Ramon 
Published: November 2022

Abstract

INTRODUCTION
Corticosteroids are commonly used preoperatively in patients with brain metastases to mitigate symptoms related to vasogenic peritumoral edema (PTE). Though these tumors are highly prevalent, surprisingly few studies have examined the effect of steroids on PTE evident on MRI. We investigated whether steroid administration was associated with greater reduction in PTE in brain metastasis patients compared to steroid-naïve patients. Additionally, we sought to determine if PTE reductions reflect preoperative neurologic improvement and whether an association existed between change in PTE and preoperative steroid dosing.

METHODS
We identified 27 patients with brain metastases and two pre-operative MRIs—one pre- and one post-steroid administration. PTE and tumor volume was calculated from FLAIR or T2 sequences using 3D Slicer. PTE was quantified with Edema Index (EI). Pertinent demographic, clinical, and dexamethasone dosing data were extracted from patient charts. Descriptive statistics were performed to compare demographic, clinical, and radiographic data between patient strata. A Pearson's R correlation was performed to assess for an association between percent change in PTE and dexamethasone dosing. All statistics were computed in R with a threshold of α= 0.05.

RESULTS
Patients that received preoperative dexamethasone demonstrated significantly greater percent reduction in PTE compared to their steroid naïve counterparts (p = 0.02). This finding was despite similar time elapsed between MRI studies across both groups. Similar PTE changes were observed in those with and without preoperative neurologic improvement. No significant correlation was found between percent-change in PTE and dexamethasone dosing.

CONCLUSIONS
We are the first to demonstrate that preoperative dexamethasone administration significantly reduces PTE in patients with brain metastases when compared to a steroid naïve reference group. Curiously, a superior reduction in PTE was not reflective of neurologic improvement. The clinical significance of this merits further study.
NIMG-09. BRAIN METASTASIS FROM PANCREATIC CANCER: OUR EXPERIENCE AND SYSTEMATIC REVIEW
Authors: Destefano Frank , Morell Alexis , Marks Katya , Fernandez Sophia , Mayo Timothy , Smith Grace , Merenzon Martin , Shah Ashish , Luther Evan , Ivan Michael , Komotar Ricardo , Eichberg Daniel 
Published: November 2022

Abstract

BACKGROUND AND PURPOSE
The goal of this study was to systematically review the existing literature on neurosurgical management and outcomes of brain metastasis from pancreatic cáncer in comparison to our institutional experience of this patient cohort.

METHODS
Following the PRISMA guidelines, a systematic literature review was performed using PubMed, Ovid Embase, Scopus, and Web of Science databases from date of database inception to January 2022. Studies were selected based on predetermined inclusion and exclusión criteria. Simulteanously, a retrospective analysis was conducted on patients who underwent neurosurgical evaluation and treatment for intracranial metastatic lesions from pancreatic origin at a single institution.

RESULTS
Of the total 292 articles in the original literature search, 17 studies were ultimately selected. A total of 23 patients with brain metastases of pancreatic origin were included. Median overall survival from original diagnosis of pancreatic cancer was 22 months (3-84). In our institution’s cohort, 4 patients were identified with a median overall survival of 30.5 months (2- 108). Our institutional cohort experienced a prolonged median overall survival (3 vs 30.5 months, p=0.03) in comparison to the literature.

CONCLUSIONS
Brain metastasis is a rare and fatal outcome of pancreatic cancer that carries a poor survival. However, based on the data presented in this review, there are patient-specific and treatment-related factors that could signal better prognosis. Further studies are needed to elucidate multimodal therapy and survival to suggest a more personalized decision-making process.
NIMG-11. THE EXTENT OF NECROSIS IN BRAIN METASTASES MAY PREDICT SUBTYPES OF PRIMARY CANCER AND OVERALL SURVIVAL IN PATIENTS RECEIVING CRANIOTOMY
Authors: Yoo Jihwan , Cha Yoon Jin , Ahn Sung Jun , Park Hun Ho 
Published: November 2022

Abstract
Although necrosis is common in brain metastasis (BM), its biological and clinical significances remain unknown. We evaluated necrosis extent differences by primary cancer subtype and correlated BM necrosis to overall survival post craniotomy. We analyzed 145 BM patients receiving craniotomy. Necrosis to tumor ratio (NTR) was measured. Patients were divided into two groups by NTR: BMs with sparse necrosis and with abundant necrosis. Clinical features were compared. To investigate factor relevance for BM necrosis, multivariate logistic regression, random forests, and gradient boosting machine analyses were performed. Kaplan–Meier analysis and log-rank tests were performed to evaluate the effect of BM necrosis on overall survival. Lung cancer was a more common origin for BMs with abundant necrosis (42/72, 58.33%) versus sparse necrosis (23/73, 31.51%, p&lt; 0.01). Primary cancer subtype and tumor volume were the most relevant factors for BM necrosis (p&lt; 0.01). BMs harboring moderately abundant necrosis showed longer survival, versus sparse or highly abundant necrosis (p=0.03). Lung cancer BM may carry larger necrosis than BMs from other cancers. Further, moderately abundant necrosis in BM may predict a good prognosis post craniotomy.
NIMG-15. INTEGRATED RADIOGRAPHIC AND PATHOLOGICAL ANALYSIS OF MALIGNANT PERIPHERAL NERVE SHEATH TUMORS (MPNSTS) REVEALS IMAGING CORRELATES OF MOLECULAR ALTERATIONS AND CLINICAL OUTCOME
Authors: Diwanji Devan , Texiera Stephanie , Lucas Calixto-Hope , Calabrese Evan , Reddy Alyssa , Pekmezci Melike , Jacques Line , Chin Cynthia , Vasudevan Harish 
Published: November 2022

Abstract
Malignant peripheral nerve sheath tumors (MPNSTs) are the most common cause of death in patients with neurofibromatosis type 1 (NF-1) yet non-invasive diagnosis and risk stratification of NF-1-associated peripheral nerve tumors remains challenging. Moreover, the relationship between radiographic features and pathologic measures such as mitotic index and necrosis, or molecular markers of malignant transformation such as H3K27 trimethylation and Schwann cell marker (S100B, SOX10) expression remain unknown. Here, we integrate positron emission tomography (PET) (n=32 studies from 18 patients) and magnetic resonance imaging (MRI) (n=20 studies from 20 patients) with immunohistochemical analysis and clinical follow-up in a total of 20 patients with pathologically confirmed MPNST diagnosis. The median age was 33 years (range 5-74 years), n = 11 were male (55%) and n=13 (65%) harbored a clinical diagnosis of NF-1. Anatomically, n=6 (30%) tumors arose near the spine, n=5 (25%) arose in the extremities, and n=4 (20%) arose in the sacrum. By PET, the average preoperative SUVmax was 8.53 with a trend toward increased SUVmax in Ki-67 high tumors (SUVmax 10.58 vs. 6.47, p=0.08). No significant differences in SUVmax based on H3K27 trimethylation, SOX10, or S100B expression was noted. The appearance of cystic necrosis by MRI was significantly associated with H3K27 trimethylation loss (Chi-square p=0.04) with a trend toward increased KI-67 labeling (43% vs. 24%, p = 0.07), suggesting cystic necrosis identifies aggressive lesions. No other features were correlated with H3K27 trimethylation status or Ki-67 labeling index. With regard to clinical outcome, dichotomized SUVmax identified patient subgroups with significant differences in overall survival (OS), with high SUVmax tumors demonstrating improved survival (median OS: 250 months vs. 60 months, log-rank test p=0.02). Our data support a relationship between radiographic features, histopathologic characteristics and clinical outcomes. Future work will include longitudinal analysis, validation in larger, multi-institutional cohorts and incorporation of radiomic approaches.
NIMG-25. OPTIMIZATION OF ARTIFICIAL INTELLIGENCE ALGORITHMS FOR LOW-RESOURCE/CLINICAL ENVIRONMENTS: FOCUS ON CLINICALLY-RELEVANT GLIOMA REGION DELINEATION
Authors: Baheti Bhakti , Thakur Siddhesh , Pati Sarthak , Karkada Deepthi , Panchumarthy Ravi , Wu Junwen , Mohan Suyash , Nasrallah MacLean , Shah Prashant , Bakas Spyridon 
Published: November 2022

Abstract

BACKGROUND
Diffuse astrocytic glioma are common and aggressive malignant primary brain tumors with grim prognosis. Artificial intelligence (AI) has shown promise across predictive, prognostic, and diagnostic neuro-oncology applications, towards improving patient management. However, clinical translation and deployment are hampered by AI models’ requirements for explicit acceleration cards, which are not typically considered in clinical environments. Here, we seek the execution of AI models in such clinical/low-resource environments, facilitated by mathematical optimizations rather than investing in acceleration cards, and focus on the use case of delineating clinically-relevant regions, namely the entire tumor burden (ETB) and tumor core (TC).

METHODS
We identified the BraTS20 retrospective cohort of 369 glioma cases, each described by 4 structural multi-parametric MRI (mpMRI) scans (T1,T1Gd,T2,T2-FLAIR). The Generally Nuanced Deep Learning Framework (GaNDLFv0.0.14/PyTorchv1.8.2) was used to train the original AI model (ResUNet), by randomly sampling 40 643 patches from each mpMRI scan. We then investigate the contributions of post-training mathematical optimizations. Quantitative performance evaluation of the original and optimized (GaNDLFv0.0.14/OpenVINOTM-INT8-v2022.1.0) models were based on 125 unseen hold-out cases (BraTS20 validation dataset), using the dice similarity coefficient (DSC), while profiling in a consumer-grade workstation, i.e., typical clinical hardware configuration.

RESULTS
Negligible delineation performance differences were observed between the original and optimized AI models, for both ETB (DSCoriginal/DSCoptimized= 0.877/0.876) and TC (DSCoriginal/DSCoptimized= 0.773/0.772). However, the optimized model yielded substantial improvements in latency (up to 5.4x faster inference) and 53% less memory footprint.

CONCLUSIONS
Post-training mathematical optimization of AI models yields substantial gains in latency and memory usage, while maintaining their accuracy. Although we focused on delineation, we anticipate these mathematical optimizations to be applicable in other AI models. Post-training optimization is a promising approach for deploying AI models on consumer-grade CPUs, and hence facilitating their translation in low-resource/clinical environments, potentially contributing to improved patient management, treatment decisions, and response assessment.
*author Siddhesh Thakur is an equal contributing first author.
NIMG-35. THE DEEP TOPOLOGY OF GLIOMAS
Authors: Ruffle James , Mohinta Samia , Brandner Sebastian , Hyare Harpreet , Nachev Parashkev 
Published: November 2022

Abstract
Tumor heterogeneity is increasingly recognized as a major obstacle to therapeutic innovation in neuro-oncology. Gliomas typically exhibit a spectrum of genetic mutations, reflecting multiple, potentially complex molecular interactions, and vary widely in their imaging appearances across size, morphology, texture, anatomical distribution, and many other spatial properties. This multifaceted diversity obscures the relationship between tumor genotypic and imaging features, and their joint influence on disease evolution and treatment susceptibility. Though opaque to simple statistical methods, it may nonetheless be accessible to models with the expressivity to capture complex interactions between many different factors. Studying an international sample of 4064 IDH-wildtype glioblastoma, IDH-mutant oligodendroglioma and astrocytoma, here we develop a highly expressive modelling framework based on deep learning and Bayesian models of networks with the power to capture the multimodal heterogeneity of glioma. We map the characteristic distribution of tumor tissue components across the brain, and link their spatial properties to regional connectivity, normal gene expression, and tumor genetics. We derive a hierarchical anatomical representation of these multimodal patterns of variation, and quantify their comparative functional impact by reference to meta-analytic maps of brain function. Our results reveal the deep topological structure of gliomas, enabling richer, more closely individuating phenotyping, the development of more robust predictive models of treatment and prognosis, and illuminating multimodal associations of potential mechanistic significance.