bluuebunny/crossref_metadata_2025_split huggingface dataset:
PATH-64. PROSPECTIVE, BLINDED PLASMA BASED ANALYSIS FOR DIAGNOSIS OF NEWLY DIAGNOSED GLIOMA
Authors: Lee Ian , Scarpace Lisa , Hunt Rachel , Nelson Kevin , Patil Darshana , Datta Vineet , Akolkar Dadasaheb , Apurva Sachin , Fulmali Pooja , Puranik Sneha , Snyder James , Noushmehr Houtan , Devhare Pradeep , deCarvalho Ana , Datar Rajan , Walbert Tobias , Kalkanis Steven 
Published: November 2019

Abstract

INTRODUCTION
In patients with newly diagnosed intracerebral lesions, gliomas are often suspected. However, other conditions such as multiple sclerosis, abscess or lymphoma are possible, as well. Furthermore, biopsy can be challenging due to eloquent and/or deep location within the brain. In this prospective, blinded study, analysis of plasma isolated cell-free DNA and exosome mRNA and miRNA from newly diagnosed glioma patients and from cancer-free volunteers was used to predict disease.

METHODS
Plasma was drawn from 52 patients with newly diagnosed gliomas (28 high grade glioma (HGG), 10 low grade (LGG)) and 14 patients without documented history of cancer and recent MRI brain which was negative for brain tumor. High quality DNA and RNA was isolated and sequenced using Next Generation Sequencing and Digital Droplet PCR was used for detection and verification of trace molecular artifacts. Multianalyte processing yielded data that was harmonized and interpreted through an Artificial Intelligence based algorithm to assess for possible glioma and to assign grade in a blinded fashion. EGFRvIII, TP53 and IDH1 mutations were also analyzed and compared to molecular testing from tumor specimens.

RESULTS
66% (25 of 38) of glioma patients were correctly diagnosed as having a malignancy. 43% of HGG and 60% of LGG patients were correctly graded. Of the 14 normal controls, 6 were concluded to be cancer-free. IDH1, EGFRvIII, and TP53 mutation had concordance of 64% (21/33), 82% (14/17) and 36% (5/14), respectively.

CONCLUSIONS
Analysis of plasma cell free tumor derived DNA and RNA was relatively sensitive for detecting glioma in treatment naïve patients. In contrast, this analysis was not specific in ruling out malignancy in the normal control patients. Given this profile, in patients with newly diagnosed intracerebral lesions suspicious for glioma, this may be a useful screening test to determine the need for more invasive testing, i.e. biopsy/resection.
NIMG-07. LACUNARITY AND FRACTAL DIMENSION ACT AS PRETREATMENT IMAGING BIOMARKERS FOR SURVIVAL IN GLIOBLASTOMA PATIENTS
Authors: Curtin Lee , Whitmire Paula , White Haylye , Mrugala Maciej , Hu Leland , Swanson Kristin 
Published: November 2019

Abstract
Glioblastoma (GBM) is the most aggressive primary brain tumor with a median survival of only 15 months with standard of care treatment. Lacunarity, a quantitative morphological measure of how shapes fill space, and fractal dimension, another morphological measure of the complexity of pixel arrangement, of segmented necrotic regions on gadolinium-enhanced T1 weighted (T1gd) MRI have previously been shown to distinguish both overall survival (OS) and progression free survival (PFS) in GBM (n = 95). In our larger patient cohort (n = 389), we sought to validate or refute previously published results connecting morphological metrics and patient survival. We identified pretreatment necrotic regions of our retrospective first-diagnosis GBM patient cohort using segmented T1gd MRI enhancing regions. We calculated lacunarity and fractal dimension across all T1gd MRI slices with enhancing tumor, and used the median lacunarity and fractal dimension values for our analysis. We find that a lacunarity threshold can significantly distinguish OS (14 months vs 19 months median, log-rank p = 0.015, n = 389) and a fractal dimension threshold can significantly distinguish PFS (8 months vs 11 months median, log-rank p = 0.015, n = 123). We believe that morphological metrics such as lacunarity and fractal dimension could play a role in standard-of-care prognostic considerations at tumor presentation. This link between morphological and survival metrics could be driven by underlying biological phenomena or microenvironmental factors that should be further explored.
NIMG-10. BRAIN TUMOR MRI STRUCTURED REPORTING ALLOWS CALCULATION OF INTERRATER AGREEMENT OF PATIENTS REVIEWED AT TUMOR BOARD
Authors: Cooper Maxwell , Hoch Michael , Abidi Syed , Hu Ranliang , Shim Hyunsuk , Weinberg Brent 
Published: November 2019

Abstract

INTRODUCTION
Longitudinal imaging evaluation of brain tumor patients with MRI is a challenging endeavor, as there is considerable overlap between imaging findings of tumor progression and treatment related changes. There is felt to be a high degree of variation between individual interpreters, particularly in complex cases. Structured reporting systems, such as the recently described brain tumor reporting and data system (BT-RADS), attempt to maximize consistency between reading radiologists, although how successful they are at achieving this is unknown.

METHODS
Patients with a diagnosis of a primary brain tumor with imaging presented at adult brain tumor board between October 2017 and March 2019 were reviewed. The most recent follow-up MRI was scored on an 8-point scale as described by BT-RADS, ranging from 0 to 4 with increasing suspicion for worsening disease, as described in the original radiologist report. Secondary review of MRI was performed by one of three neuroradiologists routinely participating in brain tumor conference. Interrater agreement between primary and secondary review were calculated using rates of exact agreement and linear weighted kappa.

RESULTS
Of studies reviewed, 270 out of 275 had imaging and reports suitable for repeat structured scoring. Grade 4 astrocytoma (glioblastoma) was the most common diagnosis, followed by grade 2 oligodendroglioma and grade 2 astrocytoma. The overall agreement rate between initial and secondary review was 83.0% with kappa of 0.89 +/- .05. There was perfect agreement on studies with improvement (1a or 1b) with lower levels of agreement (63.6–82.4%) for studies with worsening imaging findings (3a-4).

CONCLUSIONS
Using a structured reporting system to categorize MRIs in brain tumor patients allows for precise characterization of variation between report conclusions on primary and secondary review. Overall agreement is good, but variation rates increase with worsening findings which may be harder to correctly interpret.
NIMG-18. EFFECT OF BEVACIZUMAB AGAINST CYSTIC PART OF BRAIN TUMOR
Authors: Yamasaki Fumiyuki , Takano Motoki , Yonezawa Ushio , Taguchi Akira , Onishi Shumpei , Sugiyama Kazuhiko , Kurisu Kaoru 
Published: November 2019

Abstract

BACKGROUND
Bevacizumab improves the symptom via reducing the peritumoral edema and/or normalizing BBB, and occasionally via reducing the tumor size. However, the effect against active cystic portion has not been documented yet. We focused on the effect of bevacizumab on cystic component of brain tumors.

MATERIALS AND METHODS
Between 2008 and 2018, 139 patients with primary or metastatic brain tumor were treated with bevacizumab in our institution. We defined cystic lesions as high intense lesion of size ≥1 cm on T2-weighted imaging, and excluded necrotizing cysts and cystic changes in surgical resection cavity. The images before and after administration of bevacizumab were examined, and changes in cyst size was evaluated as follows: CR, PR, MR (reduction by ≥25%), SD, PD. The effect of bevacizumab on tumor itself was determined according to RANO criteria.

RESULTS
Of the 139 patients, 212 (15.1%) had cystic component. Among these 21 tumors, 14 had progressively increasing cysts prior to bevacizumab administration. On the first imaging following administration of bevacizumab, the response of cysts were as follows: CR 2, PR 7, MR 7, SD 5. The best response of cyst to bevacizumab treatment were as follows: CR 6, PR 7, MR 4, SD 4. The group of patients with progressively increasing cysts prior to bevacizumab treatment had significant cyst size reduction compared to stable cyst size groups, at initial image after bevacizumab (mean 62.6% vs. 22.5%, P=0.0055) and at best response timing (mean 76.3% vs. 32.8%, P=0.0050). The effect on the tumor itself was not associated with cyst responses, and the response rate as per RANO was as follows: CR 1, PR 11, SD 9.

CONCLUSION
Bevacizumab is effective against progressively increasing cysts. Although cyst reduction effect and tumor response are independent, bevacizumab may be effective for patients who are symptomatic due to cystic enlargement.
NIMG-20. EVALUATION OF HEAD SEGMENTATION QUALITY FOR TREATMENT PLANNING OF TUMOR TREATING FIELDS IN BRAIN TUMORS
Authors: R Shamir Reuben , Bomzon Ze’ev 
Published: November 2019

Abstract
Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of glioblastoma multiform (GBM), malignant pleural mesothelioma (MPM), and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient’s shaved scalp. The positions of the TAs are associated with treatment outcomes via simulations of the electric fields. Therefore, we are currently developing a method for recommending optimal placement of TAs. A key step to achieve this goal is to correctly segment the head into tissues of similar electrical properties. Visual inspection of segmentation quality is invaluable but time-consuming. Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user and indicate if the segmented method is of sufficient accuracy for TTFields simulation. As a first step in this direction, we identified a set of features that are relevant to atlas-based segmentation and show that these are significantly correlated (p < 0.05) with a similarity measure between gold-standard and automatically computed segmentations. Furthermore, we incorporated these features in a decision tree regressor to predict the similarity of the gold-standard and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average absolute difference 3% (SD = 3%); r = 0.92, p < 0.001). We conclude that automatic quality estimation of segmentations is feasible by incorporating segmentation-relevant features with statistical and machine learning methods, such as decision tree regressor.
NIMG-22. DEVELOPMENT AND VALIDATION OF A RADIOMIC MODEL FOR RWDD3 EXPRESSION PREDICTION IN PATIENTS WITH ACROMEGALY
Authors: Fan Yanghua , Wang Renzhi , Feng Ming 
Published: November 2019

Abstract

BACKGROUND
The expression of RWDD3 is closely related to the prognosis of acromegaly. Therefore, this study aimed to investigate a radiomics method based on MRI to noninvasively evaluate RWDD3 expression in acromegaly.

MATERIAL AND METHODS
132 patients with acromegaly were enrolled and divided into primary (n=88) and validation cohorts (n=44) according. The expression of RWDD3 was determined by immunohistochemistry. Radiomic features were extracted from the MR images and determined using the ‘Elastic Net’ feature selection algorithm. A radiomic signature was built using a support vector machine. Subsequently, multivariable logistic regression analysis was used to select the most informative clinical features, and a radiomic model, incorporating the radiomic signature and selected clinical features, was constructed and used as the final predictive model. The performance of this radiomic model was validated using receiver operating characteristics analysis, and its calibration, discriminating ability, and clinical usefulness were assessed.

RESULTS
The radiomic signature, which was constructed with radiomic features selected using the primary cohort, showed a favorable discriminatory ability in the validation cohort. The radiomic model incorporating the radiomic signature and three selected clinical features showed good discrimination abilities and calibration, with an area under the curve (AUC) of 0.89 for the primary cohort and 0.84 for the validation cohort. The radiomic model better estimated the treatment responses of patients with acromegaly than did the clinical features. Decision curve analysis showed the radiomic model was clinically useful.

CONCLUSION
This radiomic model could aid neurosurgeons in the prediction of RWDD3 expression in patients with acromegaly, and could contribute to predicting of patient prognoses.
NIMG-26. EVALUATING THE DAYS GAINED RESPONSE METRIC IN CLINICAL TRIALS USING BEVACIZUMAB PLUS ADDITIONAL AGENTS FOR RECURRENT GLIOBLASTOMA
Authors: Singleton Kyle , Johnston Sandra , Rickertsen Cassandra , Keith Anderson S , De Leon Gustavo , Whitmire Scott , Clark-Swanson Kamala , Bendok Bernard , Mrugala Maciej , Porter Alyx , Galanis Evanthia , Swanson Kristin 
Published: November 2019

Abstract
Determining effective therapies for patients with recurrent glioblastoma remains difficult especially in small early phase clinical trials. Currently utilized estimators of treatment response rely on two-dimensional measurements of tumor progression (e.g. RANO, RECIST, MacDonald) that have often fallen short in reliably connecting patients who respond with positive survival outcomes. This has prompted consideration of advanced imaging and volumetric measurements by the RANO working group and others. Our proposed approach, Days Gained (DG), uses volumetric measurements to calculate a personalized response metric based on change in tumor growth characteristics pre/post-treatment. DG has shown promise for connecting imageable treatment response with clinically meaningful (statistically significant) survival benefit when applied in upfront and recurrent therapy settings, even in the context of coincident bevacizumab treatment. In this study, we seek to consider a new cohort of patients receiving bevacizumab in combination with other novel therapies to distinguish any subpopulation of patients with meaningful response. In this work, we identified a subset of 16 patients from bevacizumab clinical trials with data available for DG analysis requiring serial imaging (T1Gd and FLAIR) prior to and after the first cycle of therapy. Each trial evaluated bevacizumab alone versus bevacizumab plus either dasatinib or TRC105 in recurrent disease. Discrimination of time to progression (TTP) was evaluated at DG thresholds identified from our prior analysis of patients treated with bevacizumab. Using previously identified DG thresholds for clinically significant treatment benefit, in our cohort, DG measured by serial FLAIR imaging was significant in predicting longer TTP (120 DG, p=0.014), but T1Gd DG scores did not attain significance (p=0.630). Given the limited insights in connecting response metrics to accurately predicted outcomes, these data further support the use of DG as a burgeoning marker of treatment response (or lack of response) that may be useful for more routine integration into clinical trials.
NIMG-38. RECOGNIZING DISSEMINATED NECROTIZING LEUKOENCEPHALOPATHY IN LEPTOMENINGEAL CARCINOMATOSIS PATIENTS TREATED WITH INTRATHECAL METHOTREXATE VIA OMMAYA RESERVOIR AFTER WHOLE BRAIN IRRADIATION
Authors: Conger Andrew , Scheeler Thomas , Leese Erika , Mongelluzzo Gino , Tosha Gatson Na 
Published: November 2019

Abstract

BACKGROUND
Disseminated necrotizing leukoencephalopathy (DNL) is a rare complication of intrathecal (IT) methotrexate administration with a previously reported incidence of 2.8%1 in patients undergoing treatment for central nervous system leukemia or lymphoma. The purpose of this study is to estimate the incidence of DNL in a series of patients undergoing treatment for leptomeningeal carcinomatosis (LMC) and to better characterize the radiographic findings associated with DNL.

METHODS
A retrospective review of a prospectively gathered database of 36 LMC patients was conducted. Patients were treated with whole brain radiation and a standardized regimen of twice-weekly IT methotrexate in the context of an established IT chemotherapy clinic. Data regarding the patients’ primary cancer pathology, time from initiation of IT methotrexate to onset of DNL, and radiographic characteristics of DNL were collected.

RESULTS
Six of 36 patients (16.7%) developed DNL. Mean time from initiation of IT treatment to development of DNL was 6.7 months. Primary pathologies of the DNL patients were breast (3), melanoma (1), GI (1), and pancreatic neuroendocrine (1). Radiologic presentation included areas of T2 hyperintensity, T1 hypointensity with contrast-enhancement, and restricted diffusion, all consistent with acute demyelination. These radiographic changes predominantly involved the catheter tract, periventricular and deep white matter, and grey-white interface. Interpretation of the magnetic resonance imaging by radiologists was highly inaccurate with the findings interpreted as catheter infection, progression of metastatic disease, stroke, or “possible treatment effect”, but never correctly identified as DNL.

CONCLUSION
DNL is a rare complication of intrathecal chemotherapy but may have a higher incidence in patients undergoing aggressive management of LMC than in those being treated for central nervous system lymphoma or leukemia. Although the imaging in DNL often exhibits characteristic findings, the rarity of this pathology leads to frequent misinterpretation, potentially complicating management of LMC.
NIMG-41. RAPID AND ACCURATE CREATION OF PATIENT-SPECIFIC COMPUTATIONAL MODELS FOR GBM PATIENTS RECEIVING OPTUNE THERAPY WITH CONVENTIONAL IMAGING (T1w/PD)
Authors: Wenger Cornelia , Sara Hershkovich Hadas , Tempel-Brami Catherine , Giladi Moshe , Bomzon Ze’ev 
Published: November 2019

Abstract

BACKGROUND
For understanding the electric field distributions in glioblastoma (GBM) patients receiving OptuneTM therapy computational head models are employed. Accurate and fast model creation is of high importance to patient-specific treatment planning for improving efficacy, i.e., for maximizing intensity delivered to the tumor which depends on the tissues’ electric properties (EPs). Traditional model creation relies on time-consuming tissue segmentation and troublesome binary categorization of distinct tumor areas for assigning homogenous EPs. Here, we present a feasibility study of a new approach for fast model creation that uses individually created, heterogeneous EP maps from conventional MRIs.

METHODS
In a previous animal study we adapted water-content based electrical properties tomography (wEPT) for creating electrical conductivity (σ) maps at 200 kHz, the operating frequency of OptuneTM therapy. This adapted wEPT approach uses a T1w and a PD image to map the tissues’ water-content (WC) with a simple function. Subsequently the σ map is calculated as a function of WC based on Maxwell’s mixture theory. Three patients of the EF-14 trial were selected for calculating WC and σ maps. One patient was chosen to create a computational head model for simulating OptuneTM treatment.

RESULTS
The wEPT-estimated values of WC and σ in the healthy brain are accurate, homogenous and consistent among patients. Contrary, wEPT-estimates of WC and σ in tumor tissues are very heterogeneous and variable between patients. The patient-specific model with wEPT reveals more detailed current pathways during OptuneTM therapy.

CONCLUSIONS
The results emphasize the need for individual head model creation, since binary segmentation masks with pre-defined σ values are not recommended for the heterogeneous and variable tumor. The presented approach holds great promise for rapid creation of patient-specific computational models because only conventional MRIs are needed. However, this method needs to be validated and further established with analyzing more patients.
NIMG-49. THE ACCURACY EVALUATION OF ARCUATE FASCICULUS OF DIFFUSION TENSOR TRACTOGRAPHY BY FUSION OF REAL SPACE AND VIRTUAL SPACE
Authors: Koike Tsukasa , Kin Taichi , Takeda Yasuhiro , Uchikawa Hiroki , Shiode Taketo , Takayanagi Shunsaku , Tanaka Shota , Saito Nobuhito 
Published: November 2019

Abstract

PURPOSE
Diffusion tensor-based tractography (DTT) is a method to estimate the direction of white matter fibers, but it is difficult to verify the relationship with brain function spatially with high accuracy. We developed a registration method to fuse the real space (brain surface photograph) and preoperative fused 3D image (virtual space) using the landmark method and thin plate spline method. In our previous study, this method was able to achieve highly accurate alignment registration error 0.7±0.1mm (mean±SE) even after brain shift due to craniotomy. In this study, we proposed a method to examine spatial errors of DTT and direct cortical stimulation (DCS) and verify its accuracy.

METHODS
We included 7 gliomas performed awake surgery. We created the fused three – dimensional image before surgery and acquired the brain surface photograph immediately after craniotomy, then we aligned them using the proposed method. Sites that showed speech arrest by DCS were plotted on the fused image. A circle with a radius of 15 mm centered on the same site was taken as the range over which the current spreads. The surface area of each of the circles was calculated to make it true if there was arcuate fasciculus drawn with DTT in the circle, and false if it did not exist. By using this method, the accuracy of the DTT was verified. RESULT: In 7 cases, speech arrest was shown at 21 DCS plots. The probability of the presence of DTT within the current spread of DCS was 64.4%.

CONCLUSION
The proposed method indicates that DTT does not necessarily match the DCS results by verification using real space and virtual space. We present some illustrative cases.