bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-40. ROBUST MODALITY-AGNOSTIC SKULL-STRIPPING IN PRESENCE OF DIFFUSE GLIOMA: A MULTI-INSTITUTIONAL STUDY
Authors: Thakur Siddhesh , Doshi Jimit , Min Ha Sung , Shukla Gaurav , Kotrotsou Aikaterini , Talbar Sanjay , Kulkarni Uday , Marcus Daniel , Colen Rivka , Davatzikos Christos , Erus Guray , Bakas Spyridon 
Published: November 2019

Abstract

BACKGROUND
Skull-stripping describes essential pre-processing in neuro-imaging, directly impacting subsequent analyses. Existing skull-stripping algorithms are typically developed and validated only on T1-weighted MRI scans without apparent gliomas, hence may fail when applied on neuro-oncology scans. Furthermore, most algorithms have large computational footprint and lack generalization to different acquisition protocols, limiting their clinical use. We sought to identify a practical, generalizable, robust, and accurate solution to address all these limitations.

METHODS
We identified multi-institutional retrospective cohorts, describing pre-operative multi-parametric MRI modalities (T1,T1Gd,T2,T2-FLAIR) with distinct acquisition protocols (e.g., slice thickness, magnet strength), varying pre-applied image-based defacing techniques, and corresponding manually-delineated ground-truth brain masks. We developed a 3D fully convolutional deep learning architecture (3D-ResUNet). Following modality co-registration to a common anatomical template, the 3D-ResUNet was trained on 314 subjects from the University of Pennsylvania (UPenn), and evaluated on 91, 152, 25, and 29 unseen subjects from UPenn, Thomas Jefferson University (TJU), Washington University (WashU), and MD Anderson (MDACC), respectively. To achieve robustness against scanner/resolution variability and utilize all modalities, we introduced a novel “modality-agnostic” training approach, which allows application of the trained model on any single modality, without requiring a pre-determined modality as input. We calculate the final brain mask for any test subject by applying our trained modality-agnostic 3D-ResUNet model on the modality with the highest resolution.

RESULTS
The average(±stdDev) dice similarity coefficients achieved for our novel modality-agnostic model were equal to 97.81%+0.8, 95.59%+2.0, 91.61%+1.9, and 96.05%+1.4 for the unseen data from UPenn, TJU, WashU, and MDACC, respectively.

CONCLUSIONS
Our novel modality-agnostic skull-stripping approach produces robust near-human performance, generalizes across acquisition protocols, image-based defacing techniques, without requiring pre-determined input modalities or depending on the availability of a specific modality. Such an approach can facilitate tool standardization for harmonized pre-processing of neuro-oncology scans for multi-institutional collaborations, enabling further data sharing and computational analyses.
NIMG-50. DOES THE PRESENCE OF GADOLINIUM AFFECT T2 –WEIGHTED IMAGES OF GLIOMA?: IMPLICATIONS FOR MRI PROTOCOL STANDARDIZATION
Authors: Jackson Pamela , Zhou Yuxiang , Hoxworth Joseph , Swanson Kristin , Hu Leland 
Published: November 2019

Abstract
There have been increasing efforts to standardize MRI scanning protocols for both routine and clinical trial imaging of glioma. The recent BTIP guidelines propose acquisition of T2-weighted images after gadiolinium (Gd) based contrast injection and before post-contrast 3D T1-weighted (T1W) images to control timing of images after contrast administration. While Gd-contrast shortens T1W signal and appears hyperintense on magnetic resonance imaging (MRI), Gd-contrast also shortens T2W signal, which could result in unintended alternations in T2W signal intensities. This has potential implications for model-based approaches for GBM diagnosis and the differential diagnosis of other tumor types (e.g., lymphoma) that rely on T2W signal characteristics. To our knowledge, no neuroimaging studies have formally evaluated the impact of Gd contrast on T2W imaging. We acquired multi-echo T2 sequences (TE=14, 28, 41, 55, 69 ms) both prior to and after the administration of Gd for 5 patients suspected of having a brain tumor. Quantitative T2 maps were created by fitting the signal decay to a signal exponential with the Levenburg-Marqurdt algorithm. Percent difference maps were calculated to compare the quantitative T2 changes between the pre- and post-Gd T2 maps. Additionally, we performed a Wilcoxon Rank-Sum test comparing T2 values within regions of interest (ROIs) representing the tumor and normal appearing white matter (NAWM). Significant differences between the T2 values in the tumor and NAWM ROIs were seen for 4 and 3 of the patients, respectively (all p-values < 0.01). Our initial data suggests that the administration of Gd contrast may affect T2 values in both the tumor and NAWM, which could impact quantitative image-based models and diagnoses in glioma. We are currently replicating these preliminary results in an expanded cohort.
NIMG-55. TO ASSESS THE UTILITY OF ADVANCED QUANTITATIVE DIFFUSION MRI DERIVED FROM MULTI B VALUE ACQUISITIONS IN THE ASSESSMENT OF TREATMENT RESPONSE, USING A SPATIALLY-INDEPENDENT APPROACH
Authors: Islam Shah , Morrison Melanie , Waldman Adam 
Published: November 2019

Abstract

PURPOSE
To assess the utility of advanced diffusion MRI derived from multi b value acquisitions in the assessment of treatment response, using a spatially-independent approach.

METHOD AND MATERIALS
13 GBM patients were enrolled into our multicentre study. All patients completed RT with TMZ. Imaging was performed pre-RT and mid RT. The MRI protocol included a ‘low’ b value acquisition (b= 0s/mm, 50s/mm, 150s/mm, 200s/mm, 500s/mm, 1000s/mm) from which monoexponential diffusion indices ADC and biexponential indices, IVIM parameters D*, D and f were calculated. A ‘high b value’ acquisition (b=0 s/mm, 500s/mm, 1000s/mm, 1500s/mm, 2000s/mm, 2500s/mm, 3000s/mm, 3500s/mm, 4000s/mm) was acquired to allow stretched exponential diffusion indices, DDC and alpha to be derived. FLAIR sequences were used to define ROI and clinical assessment of mid-treatment and end-treatment response using RANO criteria. Histograms were generated from voxels located within manually segmented ROIs defined by increased signal on T2 FLAIR images. Changes in histogram percentile profiles were evaluated across the two timepoints and compared with RANO assessment at the mid treatment and end treatment timepoints.

RESULTS
Following completion of treatment, 5 patients had PD, 4 SD and 4 CR. Patients with PD showed a histogram shift to the left across all diffusion models, in keeping with increasing diffusion restriction and implying increased cellularity. Patients with SD or CR showed little or no shift in the histogram. DDC and f are the most predictive of progression against RANO assessment, and appear superior to routine ADC. Reduction in 75th centile (f) and 95th centile (DDC) are the most sensitive histogram metrics for predicting early progressive disease.

CONCLUSION
Results suggest association between early changes in specific diffusion components and subsequent treatment response. Spatially-independent diffusion parameter comparisons provide unbiased sampling of tumour heterogeneity and abrogate the confound of voxel-to-voxel misregistration due to tumour growth/shrinkage.
NIMG-63. LONGITUDINAL ANALYSIS OF OLIGODENDROGLIOMA GROWTH PATTERN REVEALED SPATIAL HETEROGENEITY AND DIVERSE TREATMENT RESPONSE
Authors: Heiland Dieter , Ohle Robin , Behringer Simon , Beck Juergen , Schnell Oliver 
Published: November 2019

Abstract
Oligodendroglioma are defined by a distinct molecular phenotype marked by 1p19q co-deletion and simultaneous presence of an IDH1/2 mutation. These tumors showed a favorable clinical course and long-term survival of around 15 years. Due to the long course of the disease, prospective studies to determine the effectiveness of different therapeutic strategies are difficult, since the percentage of patients with multiple therapies is high. Here we report a computational approach to map the longitudinal growth pattern, to quantify the effect of therapies on tumor growth and to identify similarities and spatial heterogeneity of oligodendroglioma growth. In our study, we included a cohort of 44 histopathologically and molecularly stratified oligodendrogliomas WHO°II (n=23) and WHO°III (n=21). We started our investigation with the longitudinal tumor segmentation. All volumetric data were pinpointed to the times of tumor therapy within all patients. Next, we extracted first-order features of tumor growth and response to chemo- or radiotherapy as well as resection, resulting in a total number of 98 features. An unsupervised cluster was used to identify similarities between patients, which revealed 3 subgroups. The first subgroup contained patients with predominantly frontal oligodendrogliomas marked by increased response to radiotherapy. The second subgroup included temporal oligodendrogliomas with high response rate to PC/PCV chemotherapy and flagged by epilepsy. The third group was heterogeneous with varying growth behaviors. A survival analysis showed a better separation between low- and high-risk patients based on the growth pattern model, in contrast to the WHO grading system. Taken together, our analysis revealed a novel classification of oligodendroglioma based on the longitudinal growth pattern and therapeutical response. We also detected a spatial difference between frontally or temporally localized oligodendrogliomas. We plan to further investigate molecular data that explain these spatial differences, which also may uncover novel therapeutic strategies.
NIMG-67. DEVELOPMENT OF VERSATILE MACHINE-LEARNING APPROACHES FOR RADIOGENOMICS OF GLIOMA IN DIFFERENT COHORTS
Authors: Takahashi Masamichi , Kawaguchi Risa , Takahashi Satoshi , Miyake Mototaka , Kinoshita Manabu , Ichimura Koichi , Hamamoto Ryuji , Narita Yoshitaka , Sese Jun 
Published: November 2019

Abstract

BACKGROUND
Radiogenomics aims to analyze clinical images and information, and to predict key molecular profiles of tumors. However, imaging protocol is usually different in facilities, and it has been rarely examined whether the performance of developed methods in a dataset is robustly sustained even in other independent datasets. We explored machine learning and matrix decomposition methods using preoperative magnetic resonance images (MRIs) of glioma patients to establish versatile platform regardless of the heterogeneity of the datasets.

METHODS
Preoperative glioma MRIs and clinical information were obtained from public dataset of The Cancer Imaging Archive (TCIA, N=159) and National Cancer Center Hospital (NCC, N=166). More than 16,000 radiomic features were applied for the prediction of tumor grading and IDH mutation status. Accuracy of prediction was evaluated by AUROC (area under the receiver operating characteristic curves).

RESULTS
The performances were comparable between the image features regardless of dimension reduction methods (the best accuracy for tumor grading and IDH status prediction was 0.91 and 0.88, respectively), but they were drastically decreased in the transfer learning (0.70 and 0.69). On the other hand, they were successfully improved by applying matrix decomposition and brain embedding (0.86 and 0.79).

CONCLUSION
Our result and pipeline can be a global benchmark for future studies in heterogeneous datasets. Further evaluation in larger cohorts are planned.
NIMG-70. A LABEL-FREE APPROACH TO ASSESS CHEMOTHERAPY DRUG CONCENTRATION USING CHEMICAL EXCHANGE SATURATION TRANSFER MRI – A FEASIBILITY STUDY
Authors: Kamson David , Grossman Stuart , Han Zheng , Xu Xiang , van Zijl Peter , Liu Guanshu 
Published: November 2019

Abstract

BACKGROUND
Drug delivery is one of the most pressing problems in neuro-oncology as the blood-brain barrier (BBB) uniquely limits penetration of substances into the brain parenchyma. Plasma and CSF drug concentrations are relatively easy to measure, but do not necessarily reflect concentrations in the brain. Furthermore, invasive measurements may be inaccurate in regions of heterogeneous BBB integrity. Advanced non-invasive imaging approaches may help bridge this information gap without the added risk. Chemical exchange saturation transfer (CEST) is an MRI technique that allows in vivo detection of molecules with a suitable hydrogen exchange rate, such as methotrexate (MTX). High-dose MTX is commonly used in oncology and its concentrations in urine (>2000µM), plasma (>1000µM) and enhancing brain tumors (>350µM) are well above the theoretical threshold of CEST. AIM: We aimed to confirm the CEST detectability of MTX in vitro, and to estimate the currently lowest measurable concentrations in solutions mimicking bodily fluids.

METHODS
We used a 9.4T MRI to assess the spectra of MTX at 37°C at various concentrations (0.1–10.0 mM) in a phosphate-buffered saline (PBS) solution at various pH (6.0–8.0), as well as in synthetic urine at pH 6.2.

RESULTS
CEST signals attributable to MTX at 1.5 and 2.7ppm were successfully detected in PBS at concentrations as low as 500µM. The optimal field strength was 3.6µT. While increasing pH increased the detection threshold of the 2.7ppm signal, the 1.5ppm signal was minimally affected by pH within physiologic ranges. In synthetic urine, the MTX CEST signal was well-detectable even at concentrations as low as 200µM.

CONCLUSIONS
These preliminary results suggest that MTX-CEST may be feasible at field strengths achievable in clinical scanners and at MTX concentrations previously measured in enhancing brain tumors treated with high-dose MTX. Further optimization of the technique for in human use is under way.
NIMG-72. PROGNOSTIC VALUE OF [18F]-FDG POSITRON EMISSION TOMOGRAPHY IN PATIENTS WITH RECURRENT GLIOBLASTOMA RECEIVING BEVACIZUMAB
Authors: Graham Maya , Krebs Simone , Bale Tejus , Domfe Kwaku , Lobaugh Stephanie , Zhang Zhigang , Dunphy Mark , Kaley Thomas , Young Robert 
Published: November 2019

Abstract

BACKGROUND
Bevacizumab is used extensively for the treatment of recurrent glioblastoma, and its modification of contrast-enhanced MRI creates a need for alternate prognostic read-outs. We sought to determine the prognostic value of [18F]-FDG (FDG) PET in glioblastoma patients performed during treatment with bevacizumab.

METHODS
We performed an institutional review board approved retrospective study on patients who underwent FDG PET within 4 weeks of a dose of bevacizumab for recurrent glioblastoma. Volumes of interest were placed over one reference lesion as defined by MRI. Maximum standardized uptake value (SUVmax) and peak standardized uptake value (SUVpeak) in this volume of interest were measured on PET, and metabolic tumor volume (MTV), total lesion glycolysis (TLG) and lesion-to-normal contralateral white matter ratios (SUVmax/cWM) were calculated. In addition, tumors were divided into 2 groups based on qualitative uptake pattern: (i) FDG non-avid and (ii) FDG avid. Statistical impact of PET parameters on overall survival (OS) was assessed, as was histopathological correlate of PET findings for patients with pathology available within 3 months of PET.

RESULTS
31 patients were included. Qualitative uptake pattern was significantly associated with OS (p = 0.005), with a median OS of 9.7 months in FDG non-avid patients versus 4.5 months in FDG avid patients. In univariate analysis, SUVmax, SUVpeak and T/N ratio were also significantly associated with OS (p < 0.001). Dichotomizing our sample using a SUVmax cut-off of 15.3 was also predictive of OS (adjusted p = 0.05). Pathology was available for 7 patients (22.6%) and confirmed PET findings.

CONCLUSION
FDG PET is a promising imaging biomarker to predict overall survival in recurrent glioblastoma patients on chronic antiangiogenic therapy, offering a tool for guiding assessment of treatment response in this patient population.
NIMG-74. MULTIPARAMETER MRI INVESTIGATION OF HIGH-GRADE GLIOMA RESPONSE TO CAR T CELL IMMUNOTHERAPY
Authors: Ravi Harshan , Stringfield Olya , De Leon Gustavo , Johnston Sandra , Rockne Russell , Badie Behnam , Brown Christine E , Swanson Kristin , Gatenby Robert , Raghunand Natarajan 
Published: November 2019

Abstract

INTRODUCTION
Immunotherapy with engineered CAR T cells is a promising new therapy for glioblastoma, for which predictive and prognostic biomarkers are needed to inform effective intervention. Recently, our group analyzed standard-of-care (SOC) MRI images of long-term and short-term glioblastoma survivors and identified six intratumoral “habitats” of which “Habitat 6” was correlated with survival at diagnosis in high-grade glioma. Based on the MRI characteristics of “Habitat 6”, viz. high enhancement and high edema, we hypothesized that it could be a marker of tumor immune infiltrates. We are studying longitudinal changes in tumor “habitat” composition on MRIs of subjects with recurrent high-grade glioma treated with CAR T cells engineered to target IL13Ra2.

METHODS
MRI scans of the brain were acquired in 6 subjects at 3.0 T at baseline and various times before and after initiation of CAR T cell therapy. FLAIR, T1W and T1W-CE MRI images were registered to T2W images and six intratumoral “habitats” were computed as per our recently published methodology. The six habitats generated at the end of the tumor segmentation process were: “Habitat 1” (low FLAIR, low enhancement), “Habitat 2” (high FLAIR, low enhancement), “Habitat 3” (low FLAIR, medium enhancement), “Habitat 4” (high FLAIR, medium enhancement), “Habitat 5” (low FLAIR, high enhancement), and “Habitat 6” (high FLAIR, high enhancement).

RESULTS
Analysis of temporal changes in the six “habitats” shows an initial increase in both “Habitat 4” and “Habitat 6” following CAR T cell therapy initiation. Subjects with higher absolute volumes of “Habitat 6” at the baseline (pre-treatment) showed longer overall survival. Overall survival is a function of absolute “Habitat 6” volume at baseline, its direction of change immediately post-therapy, the duration of any increase in “Habitat 6” post-treatment, and the “Habitat 6” to “Habitat 4” ratio. Additional subjects are being evaluated to further understand these preliminary observations.
NIMG-76. RADIOPATHOMICS: INTEGRATION OF RADIOGRAPHIC AND HISTOLOGIC CHARACTERISTICS FOR PROGNOSTICATION IN GLIOBLASTOMA
Authors: Rathore Saima , Nasrallah MacLean , Mourelatos Zissimos 
Published: November 2019

Abstract

INTRODUCTION
Large number of diverse imaging [e.g., multi-parametric MRI (mpMRI), and digital pathology images] and non-imaging (e.g., clinical) biomedical data streams are being routinely acquired as part of the standard clinical workflow for glioblastoma patients. However, under the current clinical practice, these data streams are not collectively used for diagnosis. We sought to assess the synergies between pathologic, and radiomic features by evaluating the predictive value of each group of features and their combinations through a prognostic classifier.

METHODS
The mpMRI (T1,T1-Gd,T2,T2-FLAIR) and corresponding digital pathology images for 135 de novo glioblastoma was acquired from TCIA. An extensive panel of handcrafted features, including shape, volume, intensity distributions, gray-level co-occurrence matrix based texture, was extracted from delineated tumor regions of mpMRI scans. A set of 100 region-of-interest each comprising 1024x1024 that contained viable tumor with descriptive histologic characteristics and that were free of artifacts were extracted from digital pathology images, and were quantified in terms of nuclear texture features, and nuclear intensity and gradient statistics. A support vector regression multivariately integrated these features towards a marker of overall-survival. The accuracy of the predictive model for each group of features, and their combinations, was determined via a 10-fold cross-validation scheme.

RESULTS
The Pearson correlation coefficient between the survival scores predicted by SVR and the actual survival scores was estimated to be 0.75 and 0.77 for radiographic and pathologic data, however, the integration of these data yielded a clear improvement in correlation (0.81), supporting the synergistic value of these features in the prognostic model.

CONCLUSION
Radiomic features extracted from preoperative mpMRI, when used together with digital pathology features, offer synergistic value in assessment of prognosis in individual patients. The proposed radiopathomics marker may contribute to (i) stratification of patients into clinical trials, (ii) patient selection for targeted therapy, and (iii) personalized treatment planning.
NCMP-08. CLINICAL FEATURES OF CAR T-CELL THERAPY NEUROTOXICITY AS POTENTIAL SURROGATE MARKERS FOR PROGRESSION AND OUTCOME
Authors: Yuen Carlen , Artz Andrew , Kelly Thomas , Wu Shasha , Reder Anthony , Rezania Kourosh , Soliven Betty , Xie Tao , Mastrianni James , Park Deric , Kosuri Satyajit , Riedell Peter , Klejch Wesley , Ali Saad , Bishop Michael 
Published: November 2019

Abstract

CAR T-cell therapy related neurotoxicity (NT) is common and can lead to fatalities. Early recognition may improve its management and treatment.

METHODS
Twenty-six patients with relapsed/refractory diffuse large B cell lymphoma who received commercial CAR T-cell therapy (25 axicabtagene ciloleucel [Yescarta®], 1 tisagenlecleucel [Kymriah®]) between December 2017 and September 2018 were assessed for NT based on CARTOX-10 score and Common Terminology Criteria for Adverse Events (CTCAE) version 4.

RESULTS
Twenty-three of 26 (88%) of patients developed NT, which was severe (Grade III-IV) in 8/26 (31%). Severe NT was associated with complete remission compared to progressive disease by both CTCAE (2.4 ±1.1 vs. 1.4 ± 1.3, p = 0.050) and CARTOX-10 score (8.3 ± 1.6 vs. 9.4 ± 1.5, p = 0.040). CARTOX-10 scores were lower for those transferred to the ICU compared to those transferred, respectively, on Days 8 (2.71 ± 4.78 vs. 9.28 ± 2.31, p = 0.010), 9 (2.71 ± 3.96 vs. 9.06 ± 2.50, p = 0.003) and 10 (2.86 ± 4.27 vs. 8.83 ± 3.37, p = 0.002). Depression was associated with poor overall survival (deceased 67% vs. alive 20%, p = 0.00004) and severe NT (severe NT 63% vs. non-severe NT 20%, p = 0.040). EEG findings of frontal intermittent rhythmic delta activity (FIRDA) in 22% (2/9) and triphasic waves in 33% (3/9) were exclusive found in severe NT. Tremulousness was present in 100% of severe NT compared to 67% of non-severe NT patients (p = 0.060). Delirium occurred in 2 patients, both of whom expired with severe NT.

CONCLUSION
Depression, tremor, delirium, encephalopathy, and EEG features of FIRDA and triphasic waves may represent early clinical signs that predict the development of severe NT in off-protocol CAR T-cell therapy. Paradoxically, severe NT is positively correlated with progression free survival and may indicate effective therapeutic response.