bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-28. PROSPECTIVE HISTOPATHOLOGY-VALIDATED MACHINE LEARNING FOR DISTINGUISHING TRUE PROGRESSION FROM TREATMENT-RELATED CHANGES IN GLIOBLASTOMA PATIENTS
Authors: Akbari Hamed , Kazerooni Anahita Fathi , Bakas Spyridon , Sako Chiharu , Mamourian Elizabeth , Rudie Jeffrey D , Shukla Gaurav , Bagley Stephen J , Desai Arati , Brem Steven , Lustig Robert A , Wolf Ronald L , Bilello Michel , O’Rourke Donald M , Mohan Suyash , Nasrallah MacLean , Davatzikos Christos 
Published: November 2021

Abstract

PURPOSE
Decision making about the best course of treatment for glioblastoma patients becomes challenging when a new enhancing lesion appears in the vicinity of the surgical bed on follow-up MRI (after maximal safe tumor resection and chemoradiation), raising concerns for tumor progression (TP). Literature indicates 30-50% of these new lesions describe primarily treatment-related changes (TRC). We hypothesize that quantitative analysis of specific and sensitive features extracted from multi-parametric MRI (mpMRI) via machine learning (ML) techniques may yield non-invasive imaging signatures that distinguish TP from TRC and facilitate better treatment personalization.

METHODS
We have generated an ML model on a retrospective cohort of 58 subjects, and prospectively evaluated on an independent cohort of 58 previously unseen patients who underwent second resection for suspicious recurrence and had availability of advanced mpMRI (T1, T1-Gd, T2, T2-FLAIR, DTI, DSC). The features selected by our retrospective model, representing principal components analysis of intensity distributions, morphological, statistical, and texture descriptors, were extracted from the mpMRI of the prospective cohort. Integration of these features revealed signatures distinguishing between TP, mixed response, and TRC. Independently, a board-certified neuropathologist evaluated the resected tissue by blindly classifying it in the above three categories, based on mitotic figures, pseudopalisading necrosis, geographic necrosis, dystrophic calcification, vascular changes, and Ki67.

RESULTS
Tissues classified as TRC by the neuropathologist were associated with imaging phenotypes of lower angiogenesis (DSC-derived features), lower cellularity (DTI-derived features), and higher water concentration (T2, T2-FLAIR features). Our ML model characterized TP with 78% accuracy (sensitivity:86%, specificity:70%, AUC:0.80 (95%CI, 0.68-0.92)) and TRC with 81% accuracy (sensitivity:80%, specificity:81%, AUC:0.87 (95%CI, 0.72-1.00)).

CONCLUSION
Our proposed ML model reveals distinct non-invasive markers of TP and TRC, directly associated with histopathological changes in prospective glioblastoma patients. Reliable stratification of TP and TRC entities may help to noninvasively determine whether the course of treatment should change.
NIMG-32. THE FEDERATED TUMOR SEGMENTATION (FETS) INITIATIVE: THE FIRST REAL-WORLD LARGE-SCALE DATA-PRIVATE COLLABORATION FOCUSING ON NEURO-ONCOLOGY
Authors: Baid Ujjwal , Pati Sarthak , Thakur Siddhesh , Edwards Brandon , Sheller Micah , Martin Jason , Bakas Spyridon 
Published: November 2021

Abstract

PURPOSE
Robustness and generalizability of artificial intelligent (AI) methods is reliant on the training data size and diversity, which are currently hindered in multi-institutional healthcare collaborations by data ownership and legal concerns. To address these, we introduce the Federated Tumor Segmentation (FeTS) Initiative, as an international consortium using federated learning (FL) for data-private multi-institutional collaborations, where AI models leverage data at participating institutions, without sharing data between them. The initial FeTS use-case focused on detecting brain tumor boundaries in MRI.

METHODS
The FeTS tool incorporates: 1) MRI pre-processing, including image registration and brain extraction; 2) automatic delineation of tumor sub-regions, by label fusion of pretrained top-performing BraTS methods; 3) tools for manual delineation refinements; 4) model training. 55 international institutions identified local retrospective cohorts of glioblastoma patients. Ground truth was generated using the first 3 FeTS functionality modes as mentioned earlier. Finally, the FL training mode comprises of i) an AI model trained on local data, ii) local model updates shared with an aggregator, which iii) combines updates from all collaborators to generate a consensus model, and iv) circulates the consensus model back to all collaborators for iterative performance improvements.

RESULTS
The first FeTS consensus model, from 23 institutions with data of 2,200 patients, showed an average improvement of 11.1% in the performance of the model on each collaborator’s validation data, when compared to a model trained on the publicly available BraTS data (n=231).

CONCLUSION
Our findings support that data increase alone would lead to AI performance improvements without any algorithmic development, hence indicating that the model performance would improve further when trained with all 55 collaborating institutions. FL enables AI model training with knowledge from data of geographically-distinct collaborators, without ever having to share any data, hence overcoming hurdles relating to legal, ownership, and technical concerns of data sharing.
NIMG-43. ADVANCED MULTI-PARAMETRIC HYPERPOLARIZED 13C/1H IMAGING OF GBM
Authors: Autry Adam , Vaziri Sana , LaFontaine Marisa , Gordon Jeremy , Chen Hsin-Yu , Villanueva-Meyer Javier , Chang Susan , Clarke Jennifer , Xu Duan , Lupo Janine , Larson Peder , Vigneron Daniel , Li Yan 
Published: November 2021

Abstract

INTRODUCTION
The goal of this study was to characterize progressive and pseudoprogressive GBM using multi-parametric hyperpolarized (HP)-13C / 1H MRI.

METHODS
Dynamic HP-13C MRI was acquired from 13 patients with progressive GBM [patients (scans): 2(3) IDH-mutant; 11(13) IDH-wildtype] and 2 IDH-wildtype patients (3 scans) demonstrating pseudo-progression following intravenous injection of HP [1-13C]pyruvate. Frequency-selective echo-planar imaging (3s temporal resolution, 3.38 cm3 spatial resolution) captured [1-13C]pyruvate metabolism to [1-13C]lactate and 13C-bicarbonate in the brain. Dynamic 13C data were kinetically modeled to obtain the pyruvate-to-lactate conversion rate constant k PL and temporally summed to calculate 13C-metabolite percentiles and ratios (linearly interpolated 2x in-plane). 1H imaging included T2, post-Gd T1, perfusion (nCBV, %recovery), diffusion (ADC), and lactate-edited spectroscopy (CNI, choline-to-NAA index; 1H-lactate). The normal-appearing white matter (NAWM), non-enhancing lesion (NEL), and contrast-enhancing lesion (CEL) were segmented from 1H images. 13C-resolution masks were iteratively applied on a voxel-wise basis to evaluate 1H imaging parameters within each ROI and multi-parametric data were collectively evaluated using a mixed effects model in R.

RESULTS
Progressive IDH-mutant GBM compared to wildtype counterparts displayed increased perfusion %recovery (p &lt; 0.001) and k PL (p &lt; 0.01), together with reduced 1H-lactate (p &lt; 0.001) and pyruvate percentile (p &lt; 0.01), in the T2 lesion. Among IDH-wildtype progressive GBM, the CEL was distinguished from NEL/NAWM by increased nCBV (p &lt; 0.05/0.001), 1H-lactate (p &lt; 0.05/0.001); and decreased bicarbonate / lactate (p &lt; 0.05/0.001). The CEL and NEL were collectively distinguished from NAWM by elevated CNI (p &lt; 0.001/0.001), ADC (p &lt; 0.05/0.001), pyruvate percentile (p &lt; 0.001/0.001), lactate percentile (p &lt; 0.001/0.001), and relative lactate / pyruvate (p &lt; 0.001/0.05). Psuedo-progressive IDH-wildtype GBM displayed lower k PL (T2 Lesion; p &lt; 0.01) and nCBV (CEL; p &lt; 0.01) compared to progressive GBM.

CONCLUSION
HP-13C parameters can potentially augment proton imaging and demonstrated Warburg-associated metabolic alterations.
NIMG-44. PROGNOSTIC VALUE OF PH- AND OXYGEN-SENSITIVE MRI IN GLIOMA PATIENTS
Authors: Jingwen Yao , Hagiwara Akifumi , Oughourlian Talia , Wang Chencai , Raymond Catalina , Pope Whitney , Salamon Noriko , Lai Albert , Nghiemphu Phioanh , Everson Richard , Kim Won , Prins Robert , Liau Linda , Cloughesy Timothy , Nathanson David , Ellingson Benjamin 
Published: November 2021

Abstract
Hypoxia and tissue acidosis are two key features of the glioma microenvironment, both associated with a more aggressive phenotype through promotion of invasion, angiogenesis, and resistance to a vast number of therapies. In the current study, we demonstrate that higher levels of acidity and hypoxia in glioma are associated with worse prognosis by using simultaneous pH- and oxygen-sensitive amine chemical exchange saturation transfer spin-and-gradient echo echo-planar imaging (CEST-SAGE-EPI). A total of 159 histologically confirmed adult glioma patients (WHO grade II: N = 42; grade III: N = 38; grade IV, N = 79) were retrospectively evaluated. All patients were scanned with a custom amine CEST-SAGE-EPI MRI pulse sequence at 3T. Magnetization transfer ratio asymmetry (MTRasym) at 3ppm was used as a measure of relative acidity, R2’ was used as a surrogate of hypoxia, and their product MTRasym×R2' was used to quantify the degree of both acidity and hypoxia. Cox regression was performed to evaluate prognostic factors for OS and PFS. Univariate results suggested higher hypoxia, R2' (HR = 1.44, p = 0.0002), and higher combined measure of acidity and hypoxia, MTRasym×R2' (HR = 1.14, p = 0.0008), were associated with a shorter OS. When considering age, treatment status, and IDH mutation status as covariates, R2' and MTRasym×R2' remained significantly associated with patient OS (R2': HR = 1.27, p = 0.045; MTRasym×R2': HR = 1.17, p = 0.002). Within the treatment naïve patients, tumor acidity MTRasym, was also associated with OS (HR = 3.72, p = 0.003). R2' and MTRasym×R2' were significantly associated with PFS, both using univariate (R2': p &lt; 0.0001; MTRasym×R2': p &lt; 0.0001) and multivariate analyses including clinical factors (R2': p = 0.010; MTRasym×R2': p &lt; 0.0001). In summary, tumor acidity and hypoxia measured using pH- and oxygen-sensitive metabolic MRI are significant prognostic factors in glioma.
NIMG-57. MANAGEMENT OF TECTAL GLIOMAS IN ADULTS
Authors: Tadipatri Ramya , Plasto Dinko , Hughes Jeremy , Nakaji Peter , Eschbacher Jennifer , Fonkem Ekokobe , Azadi Amir , Braun Kelly , Myers Charlotte , Ashby Lynn 
Published: November 2021

Abstract

BACKGROUND
Tectal gliomas (TG) are rare tumors occurring primarily in children but also found in adults during workup of various neurological symptoms. Surgery is not required in asymptomatic cases, so histopathological information is sparse. No consensus on timing of imaging surveillance or management has been established.

OBJECTIVE
We seek to standardize neuroimaging, including MRI protocol and surveillance time intervals, and clinical management of symptoms and disease progression, including surgery, radiotherapy, and chemotherapy.

METHODS
At our institution, patients with TG were identified through a search of radiology reports and clinic notes between 1989 and 2020. Initial and serial MRI exams were evaluated for tumor size, enhancement, edema, hydrocephalus, and other radiographic features. When tissue was available, cellularity, mitotic activity, and morphology were described. We documented neurological symptoms and signs potentially related to the tumor.

RESULTS
37 cases were identified: 22 female, 15 male; 5 children, 32 adults. Age of diagnosis ranged from 7 to 69 years. Presenting symptoms included headache (59%), visual symptoms (35%), and imbalance (14%), less commonly: seizure, weakness, nausea/vomiting, and dizziness. Surgical procedures included biopsy (9), resection (7), endoscopic third ventriculostomy (15), and shunt placement (11). Eight patients received radiotherapy, including IMRT, CyberKnife, GammaKnife, and Zap-X (all adults; 4 at diagnosis, 3 at progression, 1 at diagnosis and again at progression). Four patients received chemotherapy (all adults; 1 at diagnosis, 3 at progression), all with temozolomide. One additionally received bevacizumab for radionecrosis. Three patients died with progressive disease, two following treatment and one without. Of interest, 5 adult patients developed signs of parkinsonism during their follow-up period.

CONCLUSION
Management of TG encompasses both neoplastic progression and symptom control, either from local compression or infiltrative disease. We have developed an algorithm for imaging surveillance and treatment, including MRI protocol, definition of progressive disease, and indications for antineoplastic therapies.
NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS
Authors: Petersen Gabriel Cassinelli , Shatalov Julia , Brim W R , Subramanian Harry , cui Jin , Johnson Michele , Malhotra Ajay , Aboian Mariam , Brackett Alexandria 
Published: November 2021

Abstract

PURPOSE
Differentiating gliomas and Primary CNS Lymphomas (PCNSL) represents a diagnostic challenge with important therapeutic ramifications. MR imaging combined with Machine Learning (ML) has shown promising results in differentiating tumors non-invasively. The purpose of this systematic review is to evaluate and synthesize the findings on the application of ML in differentiating PCNSL and gliomas.

MATERIALS AND METHODS
A systematic search of literature was performed in October 2020 and February 2021 on Ovid Embase, Ovid MEDLINE, Cochrane trials, and Web of Science – Core Collection. The search strategy included keywords and controlled vocabulary including the terms: gliomas, artificial intelligence, machine learning, and related terms. Publications were reviewed and screened by four different reviewers in accordance with TRIPOD.

RESULTS
The literature search yielded 11,727 studies and 1,135 underwent full-text review. Data was extracted from 16 publications showing that 10 ML and 3 deep learning (DL) algorithms were tested. The analyzed databases had an average size of 118 patients per study. 50% of the publications validated the algorithm in an independent test cohort. The most commonly tested ML and DL algorithms were support vector machines and Convolutional Neural Networks, respectively. In internal (external) datasets, ML algorithms reached an average AUC of 89% (83%); and DL 74% (77%). Preliminary TRIPOD bias analysis yielded an average score of 0.5 (range 0.31-0.62), with most papers showing deficiencies in reporting model specifications, and funding details among other items.

CONCLUSIONS
AI-based methods for differentiating gliomas and PCNSL have been reported and show that ML methods result in accuracy = &gt; 85%.With few studies using DL algorithms, further research into novel DL-based approaches is recommended. Additionally, most studies lack large datasets and external validation, thus increasing the risk of overfitting. Bias analysis of the published studies using TRIPOD identified reporting deficiencies, and close adherence to reporting criteria is recommended.
NCMP-05. LEFT BASAL GANGLIA/INTERNAL CAPSULE GLIOBLASTOMA WITH MEMORY LOSS FROM CONTRALATERAL RADIATION INDUCED VASCULOPATHIES
Authors: Higgins Shannon , Lassman Andrew , Lennihan Laura , Willey Joshua , Wang Tony , Sisti Michael , Shapiro Peter , Kim Esther , Koshy Amily 
Published: November 2021

Abstract

INTRODUCTION
Radiation induced cerebral vasculopathy encompasses a complex and broad range of effects such as ischemia, hemorrhage, vascular malformation, capillary telangiectasias, and large vessel stenosis caused by pathological reorganization of tissue after radiation exposure. Necrosis and inflammation induce damage and demyelinating changes to other vessels over the corresponding areas that may occur months to years after brain irradiation. Here we report an unusual case of hemorrhagic basal ganglia/internal capsule glioblastoma followed by contralateral basal ganglia/internal capsule acute infarct with resulting acute transient global amnesia followed by chronic memory impairment.

CASE REPORT
A 58-year old man was diagnosed with a hemorrhagic left basal ganglia/internal capsule mass after presenting with severe headaches, agitation, and vomiting. Glioblastoma (IDH wild type by sequencing, MGMT unmethylated) was identified on resection. He underwent radiotherapy and concurrent and adjuvant (12 cycles) temozolomide. Serial surveillance brain MRI scans demonstrated multiple incidental vascular abnormalities including subacute right basal ganglia/internal capsule ischemic infarct, right temporal cavernoma, and right temporal intra-parenchymal hemorrhage approximately 1, 2, and 3 years after diagnosis, respectively. Approximately 4 years after diagnosis, he presented with transient global amnesia and imaging demonstrated right basal ganglia/internal capsule ischemic stroke.

DISCUSSION
Bilateral basal ganglia/internal capsule damage from stroke has been reported as causing memory impairment (Tatemichi TK et al, Neurology 1992;42:1966-79; PMID 1407580). Here we report memory impairment from unilateral basal ganglia/internal capsule tumor and contralateral infarct following brain radiotherapy as another mechanism of neurocognitive injury. Our case highlights the significance of continuing to surveil for these findings as new neurologic symptoms may mimic tumor progression.
NCMP-13. ID8 OVARIAN CANCER MOUSE MODEL MIMICS NEUROLOGICAL SEQUELAE OF OVARIAN CANCER IN WOMEN
Authors: Lomeli Naomi , Bota Daniela , Argueta Donovan , Gupta Kalpna 
Published: November 2021

Abstract

OBJECTIVES
Chemotherapy-related cognitive impairment (CRCI) and chemotherapy-induced peripheral neuropathy (CIPN) are neurological complications of cancer treatment. Cisplatin is used to treat ovarian malignancies, and over 70% of women experience CRCI/CIPN during and after platinum-based chemoTx. However, over 30% of non-CNS cancer patients experience cognitive impairment prior to chemoTx. To examine the contribution of cancer itself and additional neurological impairment with chemoTx, we used an ID8 syngeneic mouse model of ovarian cancer and assessed hyperalgesia and cognition +/- cisplatin treatment.

METHODS
C57BL/6 female mice were injected intraperitoneally with 107 ID8 ovarian cancer cells or 0.9% saline. After 10d of ID8 injections, mice received cisplatin (2.3 mg/kg/day, i.p.) or 0.9% saline (OvT+CIS, OvT+VEH, respectively) for 5d, followed by 5d of rest for 2 cycles. Mechanical and cold hyperalgesia were assessed longitudinally. Cognition was assessed 28d post-chemoTx by the open field test (OFT), novel object recognition (NOR), and novel place recognition (NPR) tasks.

RESULTS
OvT+VEH and OvT+CIS mice developed an increased sensitivity to mechanical ( &gt;200%, p&lt; 0.001) and thermal (cold) stimuli ( &gt;78%, p&lt; 0.004) starting 14d post-ID8 implantation, vs non-tumor controls (CON). In the OFT, OvT+CIS mice had increased anxiogenic behavior (55%, p&lt; 0.001) vs CON, and (46%, p&lt; 0.05) vs OvT+VEH. In NPR, OvT+CIS had reduced discrimination (37%, p&lt; 0.05) vs CON. OvT+VEH and OvT+CIS showed impaired discrimination (25%, p&lt; 0.05 & 33%, p&lt; 0.01, respectively) in NOR vs CON, with trending differences between OvT+CIS vs OvT+VEH in hyperalgesia and cognitive tasks. DISCUSSION: This is the first rodent model to demonstrate that ovarian cancer may evoke sensory and neurocognitive changes in the absence of chemotherapy. Future development of the model will address hyperalgesia and cognitive differences between OvT+VEH vs OvT+CIS. This model has potential for translational studies on the treatment of neurological sequelae of cancer and cisplatin-induced CRCI and CIPN.
OTHR-01. SNO 2020 DIVERSITY SURVEY: DEFINING DEMOGRAPHICS, RACIAL BIASES, CAREER SUCCESS METRICS AND A PATH FORWARD FOR THE FIELD OF NEURO-ONCOLOGY
Authors: Chukwueke Ugonma , Vera Elizabeth , Acquaye Alvina , Hervey-Jumper Shawn , Odia Yazmin , Klesse Laura , Dunbar Erin , Sharma Akanksha , Fonkem Ekokobe , Thomas Alissa , Werbowetski-Ogilvie Tamra , Camelo-Piragua Sandra , Gatson Na Tosha , de la Fuente Macarena , Armstrong Terri , Porter Alyx , Jackson Sadhana 
Published: November 2021

Abstract

BACKGROUND
Neuro-oncology has grown tremendously since 2010, marked by increasing society membership, specialized clinical expertise, and new journals. Yet, modest improvement in racial/ethnic diversity amongst clinical trial participants, researchers and clinicians led us to conduct a survey identifying opportunities to enhance diversity and inclusiveness amongst neuro-oncology professionals.

METHODS
Summer 2020, the Women and Diversity Committee of the Society for Neuro-Oncology (SNO) distributed an anonymous online survey to members and affiliates (European Association of Neuro-Oncology, Asian Society for Neuro-Oncology, Society for Neuro-Oncology Latin America and Society for Neuro-Oncology Sub-Saharan Africa). The survey captured personal and professional characteristics, biases, effective mentorship qualities, career success metrics, and suggested field/society changes. Results were analyzed by geography, profession, age, racial/ethnic and sexual identity. Standard descriptive statistics characterized the study population.

RESULTS
The 386 respondents were predominantly female (58%) with a median age range of 40-49 years (31%), White (65%), and SNO members (97%). Most worked in North America (77%) in a research profession (67%). A majority of White respondents reported never experiencing biases (64%), while the majority of non-White respondents reported unconscious biases/microaggressions, followed by a lack of/limited mentorship. Qualitative assessments showcased that personal/professional success metrics were linked to needed improvements in diversity and inclusion efforts within the neuro-oncology community. CONCLUSIONS: The prevalence of racial/ethnic biases and poor mentorship rates amongst underrepresented groups in neuro-oncology is high and potentially linked to the limited diverse representation amongst members and affiliates. These findings warrant a swift implementation of equity and inclusion practices within the neuro-oncology field.
NCOG-02. SURVIVAL AND TREATMENT OUTCOMES OF NEOPLASTIC MENINGITIS (NM)
Authors: Perison Taylor , Wheat Kayla , Gnabode Salim , Price Lori Lyn , Jeyapalan Suriya 
Published: November 2021

Abstract

INTRODUCTION
NM occurs in 5 - 15% of cancer patients and Overall Survival (OS) in treated patients ranges from 2-6 months. The purpose of this study was to analyze the effect of prognostic factors on OS and calculate a graded prognostic assessment (GPA) score based on tissue type, similar to the index score created for cancer patients with brain metastases (BM).

METHODS
We conducted a single center, retrospective analysis of 118 patients diagnosed between 2006 and 2018 at TMC. The prognostic factors analyzed were: Age (&lt; = 50yo =1pt), Karnofsky Performance Status (KPS &gt; =60 =1 pt), and no extracranial metastases (1pt). The GPA score was calculated from 0.0 - 3.0 by adding the points together. Kaplan Meier curves were used to estimate OS for primary tumors with 10 or more patients (breast, lung, leukemia, lymphoma). Cox regression analysis was used to evaluate the association of the GPA with OS.

RESULTS
The GPA analysis by tumor type included 76% of the patient population. The median OS was 5 months (breast), 2 months (lung), 7 months (leukemia), and 2 months (lymphoma). We found that leukemia (p =0.008, N = 20) and lung cancer (p =0.002, N = 20) patients showed distinct separation between GPA groups on their Kaplan Meier curves. Higher KPS was associated with increased OS (p &lt; 0.0001) using Cox regression. DISCUSSION: The GPA algorithm was only partially successful in our NM population, which may reflect the smaller number of patients in our study compared to the studies used to create the BM GPA. TMC is a contributing institution to a large, multi-institutional, multi-national registry of patients with NM disease (Neoplastic Meningitis Registry - NeMeRe). We plan to use this larger dataset to validate our GPA score as a useful tool for predicting OS in NM patients.