bluuebunny/crossref_metadata_2025_split huggingface dataset:
NCOG-70. RELIABILITY AND VALIDITY OF A NEW SELF-REPORT INDEX OF COGNITIVE CONCERNS IN BRAIN TUMOR PATIENTS
Authors: Zarrella Giuliana , Perez Alice , Dietrich Jorg , Parsons Michael 
Published: December 2020

Abstract

INTRODUCTION
Subjective cognitive dysfunction is an important outcome measure in neuro-oncology and may provide additional information beyond performance-based neuropsychological testing. The Functional Assessment of Cancer Therapy-Brain (FACT-Br) is a frequently used quality of life (QoL) measure that includes indices of physical, emotional, social, and neurologic aspects of disease, but does not measure cognitive concerns. This study seeks to develop and validate an index of self-reported cognition derived from existing items on the FACT-Br.

METHODS
145 patients (Mage=51.08, Medu=15.63) with heterogeneous brain tumor diagnoses completed neuropsychological evaluation including cognitive testing and self-report measures. Nine FACT-Br items regarding cognition were combined to form the Cognitive Index (CI). Reliability of the CI was measured with Cronbach’s alpha. Concurrent validity was assessed by correlating the CI with the Patient-Reported Outcomes Measurement Information System (PROMIS) Cognitive Abilities-8 or PROMIS Cognitive Concerns-8. Discriminant validity was assessed by correlation of the CI with other FACT-Br indices and the Beck Depression and Anxiety Inventories (BDI, BAI).

RESULTS
Internal consistency within the CI was high (Cronbach’s a 0.864). The CI correlated strongly with the PROMIS-Abilities (r =.680; p&lt; 0.001) and PROMIS-Concerns (r=.780; p&lt; 0.001) indicating high convergent validity. Moderate correlations were observed between the CI and the physical and functional subscales of the FACT (r=.453 and .555), whereas correlations with the social and emotional functioning subscales were weaker (r=.381 and .325). The FACT-Br-CI correlated strongly with BDI (r=-.622) and more weakly with the BAI (r=-.344). Consistent with prior literature, the CI showed modest correlations with neuropsychological measures, including verbal memory encoding (r=.300), verbal fluency (r=.252) and a composite measure of cognition (r=.249; all p’s&lt; .01).

CONCLUSIONS
The FACT-Br-CI is a reliable and valid measure of self-reported cognition. Studies that include the FACT-Br could be retrospectively analyzed to assess self-reported cognitive outcomes, enriching the information gained from prior research.
NCOG-72. DIFFERENTIAL EFFECTS OF SURGERY AND CHEMOTHERAPY ON CHILDREN WITH POSTERIOR FOSSA BRAIN TUMORS
Authors: Dhanani Sofia , O’Neil Sharon , Tanedo Jeffrey , Malvar Jemily , Tamrazi Benita , Finlay Jonathan , Trane Francesca , Rajagopalan Vidya , Lepore Natasha , Nelson Mary Baron 
Published: December 2020

Abstract

BACKGROUND
Few neuroimaging studies of children with brain tumors treated with chemotherapy without radiation exist, and the neuropsychological effects of chemotherapy remain unclear. We aimed to differentiate the effects of surgery and chemotherapy on brain microstructure and cognition.

METHODS
Twenty-eight children with a history of posterior fossa tumor (17 treated with surgery alone; 11 treated with surgery + chemotherapy), and 21 sibling controls (n= 49) underwent diffusion tensor imaging (DTI) and neuropsychological assessment a mean of 4.5 (surgery group) to 9 years (surgery + chemotherapy group) after treatment. Psychometric measures focused on general intelligence, executive functions, processing speed, learning and memory, and social-emotional functioning. Age at diagnosis and time since diagnosis were covariates in the analyses. Group differences in DTI findings and psychometric scores, and correlations between psychometric scores and DTI results were examined.

RESULTS
Mean fractional anisotropy (FA) in the prefrontal cortex, white matter tracts, hippocampus, putamen, globus pallidus, thalamus, and pons were significantly (z≥ 2SD) lower in children treated with surgery + chemotherapy compared to those treated with surgery alone. In neuropsychological evaluation, the patient groups differed only in receptive vocabulary (p= 0.05), with children treated with surgery + chemotherapy scoring lower. Both patient groups scored lower than healthy sibling controls on measures of visuoconstructional reasoning (p= 0.02) and delayed visual (p= 0.02) and spatial memory (p= 0.01). Lower FA in the uncinate fasciculus and higher FA in the right thalamus associated with higher scores on general intelligence (p= 0.003, p= 0.002), and higher FA in the right thalamus associated with higher scores on spatial learning (p= 0.01) and memory (p= 0.01) in children treated with surgery + chemotherapy.

CONCLUSIONS
Chemotherapy is associated with injury to the microstructure of white and gray matter and neuropsychological deficits not seen in children with posterior fossa tumors treated with surgery alone.
NCOG-73. EFFECT OF TIME TO DIAGNOSIS IN CHILDREN WITH MALIGNANT CENTRAL NERVOUS SYSTEM TUMORS ON SURVIVAL OUTCOMES AND DISEASE-ASSOCIATED MORBIDITY: AN INSTITUTIONAL COHORT STUDY
Authors: Ronsley Rebecca , Irvine Mike , Kang Mehima , Goldman Ran , Cheng Sylvia 
Published: December 2020

Abstract

OBJECTIVE
To determine the impact of time to diagnosis on disease related morbidity and mortality.

METHODS
This is a retrospective review of all diagnoses of malignant CNS tumors in children presenting to BC Children’s Hospital (BCCH) from 2000-2019. Analyses were conducted to assess the relationship between time to diagnosis and overall survival as well as long-term morbidity; stratified by tumor type, age and presence of metastatic disease.

RESULTS
From 2000-2019, there were 116 children diagnosed with malignant CNS tumors at BCCH (mean age 9.5 years, 67% male). Tumors included medulloblastoma (27, 23.3%), ependymoma (14, 12.1%), high grade gliomas (19, 16.4%), CNS germ cell tumors (40, 34.4%), and other embryonal CNS tumors (16, 13.8%). Median time to diagnosis from first symptom onset was 63 days (IQR 26.5 – 237.5 days). 18% had metastatic disease. Within this cohort, 3-year EFS was 57%, and 5-year overall survival was 78.4%. 64% of patients were alive with no evidence of disease at median follow up from treatment completion of 4.31 years (IQR 1.77 – 6.15 years). Long-term morbidity included panhypopituitarism (20.7%), vision loss(22.4%), hearing loss(30.2%), learning disability(17.2%) and cognitive impairment (19.8%). In a cox proportional hazards model, overall survival was associated with time to diagnosis(estimate 0.0271;SE=1.6500;p=0.028), high grade glioma(estimate 8.3200; SE=0.6650; p=0.001), age at diagnosis(estimate 0.8590; SE=0.0532; p=0.004), relapse (estimate 4.9600; SE=0.6100; p=0.009) and metastatic disease (estimate 9.1700; SE=0.5660; p&lt; 0.001). Vision loss was also significantly correlated with time to diagnosis (estimate 0.542; SE=0.2940; p=0.038).

CONCLUSION
Median time to diagnosis is associated with overall survival and long-term morbidity in pediatric patients treated for malignant CNS tumors. These data highlight that knowledge translation to the public, primary care and emergency room professionals should be a priority in order to promote early connection with medical care and mitigate morbidity and mortality in those with pediatric CNS tumors.
NCOG-75. ESTIMATED PHYSICIAN COST OF A NEURO-ONCOLOGY MULTIDISCIPLINARY TEAM (MDT) TUMOR BOARD MEETING AT A SINGLE ACADEMIC CENTER
Authors: Schroeder Brett , Graber Jerome , Cuevas Emmanuel 
Published: December 2020

Abstract

BACKGROUND
Multidisciplinary teams (MDTs) to improve coordination across disciplines have become commonplace in oncology. Quantifying the impact of MDTs is challenging, and they carry significant costs. Weekly neuro-oncology tumor boards are attended by neuro-oncologists, neurosurgeons, radiation-oncologists, neuro-radiologists, neuro-pathologists, and support staff including mid-level practitioners, research coordinators, social workers, nurses and trainees. Our aim was to estimate costs associated with neuro-oncology MDTs.

METHODS
The estimated physician cost of MDT meetings were calculated from reported salaries of each physician specialty. Annual salaries from the Doximity 2019 Physician Compensation Report (PCR) included data for 4 neurosurgeons, 4 radiation-oncologists, 2 radiologists, 2 oncologists, and 2 neurologists. Medscape 2019 PCR data was compiled for 4 general surgeons, 2 radiologists, 2 oncologists, 2 pathologists, and 2 neurologists. The Physician Wages Across Specialties by Leigh in 2011 (JAMA) was utilized for 4 neurosurgeons, 4 radiation oncologists, 2 oncologists, and 2 neurologists. Annual salary data was divided by annual hours per specialty as reported by the Annual Work Hours Across Specialties, 2011. These values were then applied to an MDT for one patient, one hour, weekly, and annually.

RESULTS
The Doximity 2019 PCR yielded a per meeting cost of $2,520.84, and an annual cost of $131,083.68. The Medscape 2019 PCR produced a cost of $1,570.60 weekly, and $81,671.20 annually. JAMA data estimated a per meeting cost of $1,448.06, and $75.299.12. The mean per meeting and annual costs were $1846.50, and $96,018.00, respectively. With 6-10 cases per MDT, the mean costs per patient were $184.65 to $307.75.

CONCLUSIONS
Costs of MDT are not negligible. The impact of MDTs on patient outcomes are harder to quantify, but evidence exists that organized MDTs improve patient prognosis, and unorganized MDTs may negatively affect prognosis. Processes to streamline MDTs could help answer outcomes research questions, improve efficiency, and generate clinically relevant performance metrics.
NIMG-03. DEEP LEARNING SURVIVAL ANALYSIS FOR MULTIPLE BRAIN METASTASES
Authors: Chang Enoch , Joel Marina , Chang Hannah , Du Justin , Yu James , An Yi , Hansen James , Omuro Antonio , Chiang Veronica , Aneja Sanjay 
Published: December 2020

Abstract

PURPOSE
Deep learning survival models show promise for outcome prediction by leveraging the ability to model non-linear relationships between pixel-level imaging predictors and survival data. We hypothesized that a deep learning survival model derived from quantitative imaging predictors would be more effective than traditional models of survival in patients with brain metastases.

METHODS
We analyzed 831 patients with 3596 total brain metastases treated with primary stereotactic radiosurgery at our institution between 2000-2018. The primary outcome of interest was overall survival following treatment. 851 3D radiomic features were extracted from T1 post contrast MRI images of each brain metastasis and aggregated per patient. Minimum redundancy maximum relevance was used for dimensionality reduction. Relevant features were trained on DeepSurv (Cox proportional hazard neural network architecture) to model overall survival. The disease-specific Graded Prognostic Assessment which uses age, Karnofsky performance status, presence of extracranial metastases, number of brain metastases, and disease-specific molecular characteristics was used as our traditional model. Discriminatory ability between models was assessed with concordance indices (c-index) using 100 bootstrapped samples of 415 patients and evaluated for statistical significance in difference with the 2-sample t-test.

RESULTS
Median overall survival was 13 months, median age was 63 years, and the most common primary sites were NSCLC (38.5%), melanoma (18.9%), breast (14.9%), SCLC (7.2%), renal (5.3%), and GI (4.7%). The deep learning model using radiomic imaging features (c-index: 0.848, 95% CI [0.811, 0.877]) had superior discriminatory ability compared to the GPA (c-index: 0.380, 95% CI [0.356, 0.403]). Performance was significantly improved with p&lt; 0.001 on the 2-sample t-test.

CONCLUSIONS
A deep learning model using quantitative radiomic imaging features performed better than a traditional linear model using clinical predictors at modeling survival in patients with multiple brain metastases. This represents a promising method to improve prognostication for patients diagnosed with brain metastases.
NIMG-04. MRI EVALUATION OF THE EFFECT OF WHOLE BRAIN RADIOTHERAPY ON BRAIN METASTASIS
Authors: Crowe William , Zhao Dawen , Zhang Zhongwei , Wang Lulu , Bourland John 
Published: December 2020

Abstract
Poor prognosis of breast cancer brain metastasis partially results from the inability of therapeutics to penetrate the Blood-Brain-Barrier (BBB). Whole Brain Radiotherapy (WBRT) is standard-of-care for brain metastasis, however the early effects of fractionated WBRT on brain metastases have not been evaluated. Here, we applied longitudinal MRI to study WBRT’s effect on tumor volume, BBB permeability, and tumor-cell killing using anatomic, Dynamic Contrast Enhanced (DCE), and Diffusion-weighted (DW) MRI, evaluating tumor volume, permeability, and cytotoxic effect, respectively. The BCBM mouse model was established with left ventricle MDA-MB231-Br cell injection. MRI occurred before and 24h after 3 daily fractionated 4Gy WBRT (2.4Gy/min) or sham irradiation. In addition to anatomic MRI, DW MRI measured tissue ADC to study cytotoxic effect, and DCE MRI measured BBB permeability, quantified as the permeability variable Ktrans. Before treatment, T2-weighted images revealed hyperintense multifocal lesions in brains, and many had intact BBB indicated by no T1-w contrast-enhancement. WBRT tumors were smaller than sham irradiated tumors after 3 days (p&lt; 0.05), while no difference in the number of new lesions was observed. DW MRI demonstrated increased ADC in WBRT tumors (p&lt; 0.05), correlating well with elevated Caspase-3 staining of apoptotic cells. T1-w contrast enhanced images of tumors after treatment revealed no differences in the population of contrast-enhanced tumors between the WBRT and sham radiation groups. However, quantitative DCE MRI showed higher Ktrans in WBRT treated tumors relative to sham treated lesions, and an increase in Ktrans of WBRT tumors (p&lt; 0.05), despite high permeability heterogeneity both inter- and intra-tumorally, indicating remaining impermeable BBB. In addition to monitoring tumor volume, longitudinal MRI provided non-invasive assessments of temporal and spatial changes in cellularity and vascular permeability of brain metastasis in response to WBRT, which may prove to be useful for defining an ideal treatment window for potential combination BCBM treatments.
NIMG-11. IMPACT OF INVERSION TIME FOR FLAIR ACQUISITION ON THE T2-FLAIR MISMATCH DETECTABILITY FOR IDH-MUTANT, NON-CODEL ASTROCYTOMAS
Authors: Kinoshita Manabu , Arita Hideyuki , Takahashi Masamichi , Uda Takehiro , Fukai Junya , Ishibashi Kenichi , Kijima Noriyuki , Hirayama Ryuichi , Sakai Mio , Arisawa Astuko , Takahashi Hiroto , Nakanishi Katsuyuki , Kagawa Naoki , Ichimura Kouichi , Kanemura Yonehiro , Narita Yoshitaka , Kishima Haruhiko 
Published: December 2020

Abstract

PURPOSE
The current research tested the hypothesis that TI shorter than 2400 ms under 3T for FLAIR can improve the diagnostic accuracy of the T2-FLAIR mismatch sign for identifying IDHmt, non-CODEL astrocytomas.

EXPERIMENTAL DESIGN
We prepared three different cohorts; 94 MRI from 76 IDHmt, non-CODEL LrGGs, 33 MRI from 31 LrGG under the restriction of FLAIR being acquired with TI &lt; 2400 ms for 3T or 2016 ms for 1.5T, and 103 MRI from 103 patients from the TCIA/TCGA dataset for LrGG. The presence or absence of the “T2-FLAIR mismatch sign” was evaluated, and we compared diagnostic accuracies according to TI used for FLAIR acquisition.

RESULTS
The T2-FLAIR mismatch sign was more frequently positive when TI was shorter than 2400 ms under 3T for FLAIR acquisition (p = 0.0009, Fisher’s exact test). The T2-FLAIR mismatch sign was positive only for IDHmt, non-CODEL astrocytomas even if we confined the cohort with FLAIR acquired with shorter TI (p = 0.0001, Fisher’s exact test). TCIA/TCGA dataset validated that the sensitivity, specificity, PPV, and NPV of the T2-FLAIR mismatch sign to identify IDHmt, non-CODEL astrocytomas improved from 31%, 90%, 79%, and 51% to 67%, 94%, 92%, and 74%, respectively if we acquired FLAIR with TI shorter than 2400 ms.

CONCLUSIONS
We revealed that TI for FLAIR impacts diagnostic accuracy of the T2-FLAIR mismatch sign and that FLAIR scanned with TI &lt; 2400 ms in 3T is necessary for LrGG imaging.
NIMG-14. MACHINE LEARNING-BASED EVALUATION OF STATIC AND DYNAMIC FET-PET FOR THE DETECTION OF PSEUDOPROGRESSION IN PATIENTS WITH IDH-WILDTYPE GLIOBLASTOMA
Authors: Kebir Sied , Schmidt Teresa , Weber Matthias , Lazaridis Lazaros , Galldiks Norbert , Langen Karl-Josef , Kleinschnitz Christoph , Hattingen Elke , Herrlinger Ulrich , Lohmann Philipp , Glas Martin 
Published: December 2020

Abstract

BACKGROUND
Pseudoprogression (PSP) detection in glioblastoma has important clinical implications and remains a challenging task. With the significant advances provided by machine learning (ML) in health care, we investigated the potential of ML in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma.

METHODS
We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. All patients presented imaging findings suspected of PSP/TP on contrast-enhanced MRI. For further diagnostic evaluation, patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. To develop a robust ML model, we trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a training cohort and validated the results on a separate test cohort. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve.

RESULTS
Of the 44 patients included in this study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p=0.010 and p=0.047, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax.

CONCLUSIONS
This study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance compared to conventional ROC analysis.
NIMG-15. EVALUATING FLUCTUATING ENHANCEMENT IN OLIGODENDROGLIOMAS ON MAGNETIC RESONANCE IMAGING
Authors: Barbaro Marissa , Pan Peter , Pisapia David , Schwartz Theodore , Ramakrishna Rohan , Knisely Jonathan , Fine Howard , Chiang Gloria , Magge Rajiv 
Published: December 2020

Abstract

OBJECTIVE
To identify and characterize patterns of fluctuating contrast enhancement on magnetic resonance imaging (MRI) in patients with oligodendrogliomas.

INTRODUCTION
Gliomas, particularly oligodendrogliomas, can exhibit fluctuating enhancement (FE) on MRI that can make it difficult to differentiate between treatment effect and active tumor.

METHODS
We are conducting a single-center retrospective review of clinical and radiographic data for patients with oligodendrogliomas treated at Weill Cornell Medicine (WCM) from 2/2000-5/2018. We have identified patients with FE on MRI and tracked lesions &gt; 5mm in at least one dimension to the resolution of the lesion or last available MRI. We have recorded time from initial diagnosis to development of FE, time from radiation to development of FE, and time from development to resolution of FE as well as molecular characteristics of each tumor.

RESULTS
A total of 122 patients with oligodendrogliomas were identified. Thus far, fluctuating enhancement has been identified in 11 patients (5 men, 6 women) with 38 total fluctuating lesions. Isocitrate dehydrogenase-1 (IDH-1) mutation was present in 5 tumors, and 1p/19q co-deletion was present in 6. Mean time from initial diagnosis to development of FE was 44.6 months. In patients who developed FE after radiation, mean time from radiation to development of FE was 35.0 months. Twenty-seven lesions resolved, and mean time from onset to resolution of FE was 5.6 months, while mean time from start of radiation to resolution of FE was 41.0 months. Additionally, we will perform perfusion analysis on lesions &gt; 5mm and identify patients who underwent surgical biopsy of FE with pathologic diagnosis.

CONCLUSIONS
FE has been identified in 11 patients thus far. We are expanding our analysis to identify a larger cohort of patients with FE. Characterizing patterns of FE may aid clinicians in differentiating FE due to treatment effect from active tumor.
NIMG-21. DIFFERENTIATING TUMOR TYPES BASED ON THE PERITUMORAL MICROENVIRONMENT USING CONVOLUTIONAL NEURAL NETWORKS
Authors: Samani Zahra Riahi , Parker Drew , Alappatt Jacob Antony , Brem Steven , Verma Ragini 
Published: December 2020

Abstract

PURPOSE
The differential diagnosis of glioblastoma (GBM) versus single brain metastasis (Met) is clinically important, and is undertaken with a clinical reading of MR images and/or tumor biopsy. We investigate whether Mets and GBMs can be differentiated based on the microstructure of the FLAIR-hyperintense peritumoral region measured by diffusion tensor imaging (DTI). We hypothesize that the peritumoral microstructure differs in extracellular water content, based on whether it is vasogenic edema or infiltrative. We use deep learning trained on DTI-based free-water volume fraction maps to discriminate between the peritumoral regions of Met and GBM neoplasms. Our results are also compared with mean diffusivity (MD), the most commonly used DTI metric.

METHOD
dMRI data of 143 patients with brain tumors (89 glioblastomas and 54 metastases, ages 19-87 years, 77 females and 66 males) were included. Free-water volume fraction maps were computed for the peritumoral regions (demarcated automatically). We developed a 7-layer convolutional neural network (CNN) architecture to distinguish microstructural patterns of Met and GBM tumors using 32 x 32 mm patches placed at random in the peritumoral area. The CNN was trained on patches from a training set of 113 patients and tested on the remaining 30 patients, where majority voting was applied to predict the tumor type for each patient. Although MD has been previously used in both tumor and peritumoral area for discriminating tumor type, we replicated the same process with MD only in the peritumoral area to provide a stronger comparison.

RESULT
We predicted tumor type with 93% accuracy, outperforming MD with 84% accuracy.

CONCLUSION
Our results demonstrate that deep learning with CNN on DTI-based free-water volume fraction map can be a promising tool for automatic distinction of tumor types, and has potential as a tumor biomarker.