bluuebunny/crossref_metadata_2025_split huggingface dataset:
NIMG-05. DIFFERENCES IN VASCULARITY BETWEEN RECURRENT GLIOBLASTOMA AND BRAIN METASTASIS USING DYNAMIC CONTRAST ENHANCED MRI
Authors: Bashat Dafna Ben , Blumenthal Deborah , Limon Dror , Aizenstein Orna , Bressler Idan , Bokstein Felix , Artzi Moran 
Published: November 2021

Abstract
In recent years, several automatic methods have been proposed to differentiate between brain tumor recurrence and treatment-related changes following radiotherapy, based on conventional and advanced MRI methods. Vascular parameters extracted from dynamic contrast-enhanced (DCE) were suggested as potential markers for disease progression. Yet, most studies that proposed machine-learning methods or relayed on threshold values of DCE for classification, did not analyze separately Glioblastoma (GBM) and brain metastasis, and often offer the same method/threshold values, neglecting the different vascularity of these two tumor types. Understanding and quantifying the differences between tumors type can improve early diagnosis of recurrence and may improve the reliability of identifying treatment-related changes. The aim of this study was to quantify vascular parameters of tumor recurrence and to assess differences between GBM and brain metastasis, specifically metastasis of breast cancer. METHOD: 41 MRI scans were included: 24 from patients with GBM: 20 with recurrence and 4 with treatment-related changes, and 17 scans from patients with brain metastasis of breast cancer: 10 with recurrence and 7 with treatment-related changes (diagnosis were based on radiological follow-up/histopathology, when available). MRIs were performed on a 3.0 Tesla MRI scanner (Siemens MAGNETOM Prisma) and included T1, T1+contrast, and DCE images. DCE analysis was performed using DUSTER and Ktrans, Vp and Kep pharmacokinetic parameters were compared between groups. RESULTS: Patients with GBM often demonstrated both tumor recurrence and areas with treatment-related changes. Significantly differences were detected between tumor recurrence and treatment-related changes, and higher Vp and Ktrans were detected in recurrent GBM as compared to recurrent breast cancer metastasis. Cutoff for differentiation between tumor recurrence and treatment-related changes will be presented specifically for each tumor type. To conclude, when defining quantitative threshold values for clinical decision-making, the inherent differences between primary and metastatic brain tumors should be taken into account.
NIMG-12. MR SUSCEPTIBILITY IMAGING FOR DETECTION OF TUMOR-ASSOCIATED MACROPHAGES IN GLIOBLASTOMA
Authors: Nazem Amir , Guiry Samantha , Pourfathi Mehrdad , Ware Jeffrey B , Anderson Hannah , Iyer Srikant Kamesh , Moon Brianna F , Fan Yi , Witschey Walter R , Rizi Rahim , Bagley Stephen J , Desai Arati , O’Rourke Donald M , Brem Steven , Nasrallah MacLean , Nabavizadeh S Ali 
Published: November 2021

Abstract

BACKGROUND
Tumor-associated macrophages (TAMs) are a key component of glioblastoma (GBM) tumor microenvironment. Considering the differential role of different TAM phenotypes in iron metabolism with the M1 phenotype storing intracellular iron, and M2 phenotype releasing iron in the tumor microenvironment, here we investigated non-invasive quantitative susceptibility mapping (QSM) and T2* MRI relaxometry to quantify iron as imaging biomarkers for TAMs in adult patients with GBM.

METHODS
In this prospective study, 21 adult patients with GBM were enrolled between 2016 to 2019. Patients underwent a 3D single echo gradient echo sequence in addition to standard anatomical sequences on a 3 Tesla MRI. QSM images were reconstructed using the morphology-enabled dipole inversion (MEDI) algorithm. In 3 subjects, ex vivo imaging of surgical specimens was performed on a Bruker 9.4 Tesla 8.9 cm vertical bore MR using 3D multi-echo GRE scans, and R2* (1/T2*) maps were generated by a pixel-wise monoexponential fitting. Each specimen was stained with hematoxylin and eosin, as well as with CD68, CD86, CD206, and L-Ferritin.

RESULTS
Significant positive correlation was observed between mean susceptibility for the tumor enhancing zone and the L-ferritin positivity percent (r =0.56, p=0.018) and the combination of tumor’s enhancing zone and necrotic core and the L-Ferritin positivity percent (r=0.72; p=0.001). Moreover, the mean susceptibility significantly correlated with positivity percent for CD68 (ρ=0.52, p=0.034) and CD86 (r=0.7 p=0.001), but not for CD206 (ρ=0.09; p=0.7). There was a positive correlation between mean R2* values and CD68 positive cell counts (r =0.6, p=0.016). Similarly, mean R2* values significantly correlated with CD86 (r=0.54, p=0.03) but not with CD206 (r=0.15, p=0.5). High mean susceptibility in the necrotic core was associated with inferior PFS (hazard ratio,4.5, p=0.016).

CONCLUSION
MR Susceptibility Imaging can quantify the iron content of GBM and provide a non-invasive method for TAM quantification and phenotyping.
NIMG-16. DEEP LEARNING SUPER-RESOLUTION MR SPECTROSCOPIC IMAGING TO MAP TUMOR METABOLISM IN MUTANT IDH GLIOMA PATIENTS
Authors: Li Xianqi , Andronesi Ovidiu 
Published: November 2021

Abstract
Metabolic imaging can map spatially abnormal molecular pathways with higher specificity for cancer compared to anatomical imaging. However, acquiring high resolution metabolic maps similar to anatomical MRI is challenging in patients due to low metabolite concentrations, and alternative approaches that increase resolution by post-acquisition image processing can mitigate this limitation. We developed deep learning super-resolution MR spectroscopic imaging (MRSI) to map tumor metabolism in patients with mutant IDH glioma. We used a generative adversarial network (GAN) architecture comprised of a UNet neural network as the generator network and a discriminator network for adversarial training. For initial training we simulated a large data set of 9600 images with realistic quality for acquired MRSI to effectively train the deep learning model to upsample by a factor of four. Two types of training were performed: 1) using only the MRSI data, and 2) using MRSI and prior information from anatomical MRI to further enhance structural details. The performance of super-resolution methods was evaluated by peak SNR (PSNR), structure similarity index (SSIM), and feature similarity index (FSIM). After training on simulations, GAN was evaluated on measured MRSI metabolic maps acquired with resolution 5.2×5.2 mm2 and upsampled to 1.3×1.3 mm2. The GAN trained only on MRSI achieved PSNR = 27.94, SSIM = 0.88, FSIM = 0.89. Using prior anatomical MRI improved GAN performance to PSNR = 30.75, SSIM = 0.90, FSIM = 0.92. In the patient measured data, GAN super-resolution metabolic images provided clearer tumor margins and made apparent the tumor metabolic heterogeneity. Compared to conventional image interpolation such as bicubic or total variation, deep learning methods provided sharper edges and less blurring of structural details. Our results indicate that the proposed deep learning method is effective in enhancing the spatial resolution of metabolite maps which may better guide treatment in mutant IDH glioma patients.
NIMG-19. SYNTHETIC ISOCITRATE DEHYDROGENASE-MUTANT GLIOBLASTOMAS FROM GENERATIVE ADVERSARIAL NETWORK PROVIDE MORPHOLOGIC VARIABILITY AND DIAGNOSTIC PERFORMANCE SIMILAR TO REAL DATA: DEVELOPMENT AND VALIDATION
Authors: Park Ji Eun , Kim Ho Sung , Eun Dain 
Published: November 2021

Abstract

BACKGROUND
Generative adversarial network (GAN) creates synthetic MRI data that may provide morphologic variability to assess molecular characteristics of glioblastomas.

PURPOSE
To investigate the ability of GAN-based generation of isocitrate dehydrogenase (IDH)-mutant glioblastomas to provide morphologic variability and improve molecular prediction.

METHODS
GAN was retrospectively trained on 110 IDH-mutant high-grade gliomas. Paired contrast-enhanced T1-weighted and FLAIR synthetic MRI data were generated. Diagnostic models were developed from 80 IDH-wild type glioblastomas and 38 IDH-mutant patients, (real model), 38 IDH-mutant GAN-generated synthetic data (synthetic model), or both combined (augmented model). Two neuroradiologists independently assessed real and morphologic characteristics of contrast-enhancement patterns, the presence of necrosis, and margins and type of non-enhancing region. Significant predictors of IDH mutation were selected from multivariable logistic regression, and diagnostic performance was validated in 44 separate patients, 33 with IDH-wild type and 11 with IDH-mutant glioblastomas.

RESULTS
Synthetic IDH-mutant glioblastomas were similar to real tumors on Turing tests, with an area under the curve (AUC) of 0.67–0.71. Significant predictors of a more frontal or insular location (odds ratio [OR], 1.34 vs. 1.52; highest P = .04) and distinct non-enhancing tumor margins (OR, 2.68 vs. 3.88; P &lt; .001) were similar for the real and synthetic models, with the synthetic (AUC, 0.958) and augmented (AUC, 0.899) models having higher diagnostic performance than the real model (AUC, 0.864) in the training set. The diagnostic accuracy was higher for the synthetic and augmented models (90.9% [40/44] each for both observers) than for the real model (84.1% [37/44] for one observer and 86.4% [38/44] for the other).

CONCLUSIONS
The GAN-based synthetic images yield morphologically variable IDH-mutant glioblastomas and may be useful as realistic training sets.
NIMG-23. MACHINE LEARNING METHODS IN GLIOMA GRADE PREDICTION: A SYSTEMATIC REVIEW
Authors: Bahar Ryan , Merkaj Sara , Brim W R , Subramanian Harry , Zeevi Tal , Kazarian Eve , Lin Ming , Bousabarah Khaled , Payabvash Sam , Ivanidze Jana , Cui Jin , Tocino Irena , Malhotra Ajay , Aboian Mariam 
Published: November 2021

Abstract

PURPOSE
Machine learning (ML) technologies have demonstrated highly accurate prediction of glioma grade, though it is unclear which methods and algorithms are superior. We have conducted a systematic review of the literature in order to identify the ML applications most promising for future research and clinical implementation.

MATERIALS AND METHODS
A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Screening of publications was done in Covidence, and TRIPOD was used for bias assessment.

RESULTS
The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles published since 1995 met the criteria for our study. 79% of the articles were published between 2018 and 2020. The average glioma prediction accuracy of the highest performing model in each study was 90% (range: 53% to 100%). The most common algorithm used for cML studies was Support Vector Machine (SVM) and for DL studies was Convolutional Neural Network (CNN). BRATS and TCIA datasets were used in 47% of the studies, with the average patient number of study datasets being 186 (range: 23 to 662). The average number of features used in machine learning prediction was 55 (range: 2 to 580). Classical machine learning (cML) was the primary machine learning model in 68% of studies, with deep learning (DL) used in 32%.

CONCLUSIONS
Using multimodal sequences in ML methods delivers significantly higher grading accuracies than single sequences. Potential areas of improvement for ML glioma grade prediction studies include increasing sample size, incorporating molecular subtypes, and validating on external datasets.
NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING
Authors: Cluceru Julia , Phillips Joanna , Molinaro Annette , Interian Yannet , Luks Tracy , Alcaide-Leon Paula , Nair Devika , LaFontaine Marisa , Shai Anny , Chunduru Pranathi , Pedoia Valentina , Villanueva-Meyer Javier , Chang Susan , Lupo Janine 
Published: November 2021

Abstract
In contrast to the WHO 2016 guidelines that use genetic alterations to further stratify patients within a designated grade, new recommendations suggest that IDH mutation status, followed by 1p19q-codeletion, should be used before grade when differentiating gliomas. Although most gliomas will be resected and their tissue evaluated with genetic profiling, non-invasive characterization of genetic subgroup can benefit patients where surgery is not otherwise advised or a fast turn-around is required for clinical trial eligibility. Prior studies have demonstrated the utility of using anatomical images and deep learning to distinguish either IDH-mutant from IDH-wildtype tumors or 1p19q-codeleted from non-codeleted lesions separately, but not combined or using the most recent recommendations for stratification. The goal of this study was to evaluate the effects of training strategy and incorporation of Apparent Diffusion Coefficient (ADC) maps from diffusion-weighted imaging on predicting new genetic subgroups with deep learning. Using 414 patients with newly-diagnosed glioma (split 285/50/49 training/validation/test) and optimized training hyperparameters, we found that a 3-class approach with T1-post-contrast, T2-FLAIR, and ADC maps as inputs achieved the best performance for molecular subgroup classification, with overall accuracies of 86.0%[CI:0.839,1.0], 80.0%[CI:0.720,1.0], and 85.7%[CI:0.771,1.0] on training, validation, and test sets, respectively, and final test class accuracies of 95.2%(IDH-wildtype), 88.9%(IDH-mutated,1p19qintact), and 60%(IDHmutated,1p19q-codeleted). Creating an RGB-color image from 3 MRI images and applying transfer learning with a residual network architecture pretrained on ImageNet resulted in an 8% averaged increase in overall accuracy. Although classifying both IDH and 1p19q mutations together was overall advantageous compared with a tiered structure that first classified IDH mutational status, the 2-tiered approach better generalized to an independent multi-site dataset when only anatomical images were used. Including biologically relevant ADC images improved model generalization to our test set regardless of modeling approach, highlighting the utility of incorporating diffusion-weighted imaging in future multi-site analyses of molecular subgroup.
NIMG-26. IMPROVING THE GENERALIZABILITY OF DEEP LEARNING FOR T2-LESION SEGMENTATION OF GLIOMAS IN THE POST-TREATMENT SETTING
Authors: Ellison Jacob , Caliva Francesco , Damasceno Pablo , Luks Tracy , LaFontaine Marisa , Li Yan , Pedoia Valentina , Villanueva-Meyer Javier , Lupo Janine 
Published: November 2021

Abstract
Although current advances for automated glioma lesion segmentation and volumetric measurements using deep learning have yielded high performance on newly-diagnosed patients, response assessment in neuro-oncology still relies on manually-drawn, cross-sectional areas of the tumor because these models do not generalize to patients in the post-treatment setting, where they are most needed in the clinic. Surgical resections, adjuvant treatment, or disease progression can alter the characteristics of these lesions on T2-weighted imaging, causing measures of segmentation accuracy, typically measured by Dice coefficients of overlap (DCs), to drop by ~15%. To improve the generalizability of T2-lesion segmentation to patients with glioma post-treatment, we evaluated the effects of: 1) training with different proportions of newly-diagnosed and treated gliomas, 2) applying transfer learning from pre- to post-treatment domains, and 3) incorporating a loss term that spatially weights the lesion boundaries with greater emphasis in training. Using 425 patients (208 newly-diagnosed, 217 post-Tx, with 25 treated patients withheld as a test set) and a top-performing model previously trained on newly-diagnosed gliomas, we found that DCs increased by 10% (to 0.84) then plateaued after including ~25% of post-treatment patients in training. Transfer learning (pre-training on newly-diagnosed and finetuning with post-treatment data) significantly improved Hausdorf distances (HDs), a measure more sensitive to changes at the lesion boundaries, by 17% after including 26% post-treatment images in training, while DCs remained similar. Although modifying our loss functions with boundary-weighted penalizations resulted in comparable DCs to using standard DC loss, HD measures were further reduced by 26%, suggesting that HDs may be a more sensitive metric to subtle changes in segmentation accuracy than DCs. Current work is evaluating their utility in providing accurate volumes for real-time response assessment in the clinic using workflows that have recently been deployed on our clinical PACs system.
NIMG-35. MACHINE LEARNING GLIOMA GRADE PREDICTION LITERATURE: A TRIPOD ANALYSIS OF REPORTING QUALITY
Authors: Merkaj Sara , Bahar Ryan , Brim W R , Subramanian Harry , Zeevi Tal , Kazarian Eve , Lin Ming , Bousabarah Khaled , Payabvash Sam , Ivanidze Jana , Cui Jin , Tocino Irena , Malhotra Ajay , Aboian Mariam 
Published: November 2021

Abstract

PURPOSE
Reporting guidelines are crucial in model development studies to ensure the quality, transparency and objectivity of reporting. While machine learning (ML) models have proven themselves effective in predicting glioma grade, their potential use can only be determined if they are clearly and comprehensively reported. Reporting quality has not yet been evaluated for ML glioma grade prediction studies, to our knowledge. We measured published literature against the TRIPOD Statement, a checklist of items considered essential for the reporting of diagnostic studies.

MATERIALS AND METHODS
A literature review, in agreement with PRISMA, was conducted by a university librarian in October 2020 and verified by a second librarian in February 2021 using four databases: Cochrane trials (CENTRAL), Ovid Embase, Ovid MEDLINE, and Web of Science core-collection. Keywords and controlled vocabulary included artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Publications were screened in Covidence and scored against the 27 items in the TRIPOD Statement that were relevant and applicable.

RESULTS
The search identified 11,727 candidate articles with 1,135 articles undergoing full text review. 86 articles met the criteria for our study. The mean adherence rate to TRIPOD was 44.4% (range: 22.2% - 66.7%), with poor reporting adherence in categories including abstract (0%), model performance (0%), title (1.2%), justification of sample size (2.3%), full model specification (2.3%), participant demographics and missing data (7%). Studies had high reporting adherence in categories including results interpretation (100%), background (98.8%), study design/source of data (96.5%), and objectives (95.3%).

CONCLUSION
Existing publications on the use of ML in glioma grade prediction have a low overall quality of reporting. Improvements can be made in the reporting of titles and abstracts, justification of sample size, and model specification and performance.
NIMG-36. VISUALIZATION OF TUMOR HETEROGENEITY AND PREDICTION OF ISOCITRATE DEHYDROGENASE MUTATION STATUS FOR HUMAN GLIOMAS BY USING MULTIPARAMETRIC PHYSIOLOGIC AND METABOLIC MRI
Authors: Hagiwara Akifumi , Tatekawa Hiroyuki , Jingwen Yao , Raymond Catalina , Everson Richard , Patel Kunal , Mareninov Sergey , Yong William , Salamon Noriko , Pope Whitney , Nghiemphu Phioanh , Liau Linda , Cloughesy Timothy , Ellingson Benjamin 
Published: November 2021

Abstract
Preoperative prediction of isocitrate dehydrogenase mutation status is clinically meaningful, but remains challenging. This study aimed to predict the isocitrate dehydrogenase (IDH) status of gliomas by using the machine learning voxel-wise clustering method of multiparametric physiologic and metabolic magnetic resonance imaging (MRI) and to show the association of the created cluster labels with the glucose metabolism status of the tumors. Sixty-nine patients with diffuse glioma were scanned by pH-sensitive MRI, diffusion-weighted imaging, fluid-attenuated inversion recovery, and contrast-enhanced T1-weighted imaging at 3 T. An unsupervised two-level clustering approach, including the generation of a self-organizing map followed by the K-means clustering, was used for voxel-wise feature extraction from the acquired images. The logarithmic ratio of the labels in each class within tumor regions was applied to a support vector machine to differentiate IDH mutation status. Bootstrapping and leave-one-out cross-validation were used to calculate the area under the curve (AUC) of receiver operating characteristic curves, accuracy, sensitivity, and specificity for evaluating performance. Targeted biopsies were performed for 14 patients to explore the relationship between clustered labels and the expression of key glycolytic proteins determined using immunohistochemistry. The highest prediction performance to differentiate IDH status was found for 10-class clustering, with a mean AUC, accuracy, sensitivity, and specificity of 0.94, 0.91, 0.90, and 0.91, respectively. The tissues with labels 7 + 8 + 9 + 10 showed high expression levels of hypoxia-inducible factor 1-alpha, glucose transporter 3, and hexokinase 2, which are typical of IDH wild-type glioma, whereas those with labels 1 showed low expression of these proteins. Our machine learning model successfully predicted the IDH mutation status of gliomas, and the resulting clusters properly reflected the metabolic status of the tumors.
NIMG-41. PH-WEIGHTED MOLECULAR MRI AS AN EARLY BIOMARKER OF METABOLIC RESPONSE TO IDH INHIBITION IN IDH MUTANT GLIOMAS
Authors: Ellingson Benjamin , Jingwen Yao , Hagiwara Akifumi , Nathanson David , Oughourlian Talia , Wang Chencai , Raymond Catalina , Pope Whitney , Salamon Noriko , Lai Albert , Nghiemphu Phioanh , Everson Richard , Kim Won , Liau Linda , Pandya Susan , Steelman Lori , Hassan Islam , Cloughesy Timothy 
Published: November 2021

Abstract
The current study tested the hypotheses that (1) pH-weighted MRI measurements of tumor acidity reflect glycolytic activity in human gliomas, (2) that tumor acidity, and thus glycolytic activity, differs between human IDH mutant (mIDH) and wild type gliomas, and (3) that an increase in tumor acidity, suggestive of increased glycolytic activity, occurs after inhibition of mutant IDH enzyme activity and reduction in 2HG, an oncometabolite. To test these hypotheses, we employed a custom pH-weighted amine chemical exchange saturation transfer echoplanar (CEST-EPI) technique in 152 patients with IDH mutant or wildtype glioma prior to surgery and 11 patients before and after treatment with AG120 or AG881 enrolled at our institution in a phase 1 perioperative study in patients with recurrent, non-enhancing, IDH mutant low-grade gliomas (NCT03343197). Results from image-guided biopsies in more than 100 patients demonstrated a significant correlation between MTRasym at 3ppm, a measure of tumor acidity from pH-weighted amine CEST-EPI, and expression of key glycolytic proteins including GLUT3 (R2=0.2188,P=0.0105), HK2 (R2=0.1788,P=0.0314), LDHA (R2=0.1111,P=0.0071), and MCT1 (R2=0.1228,P=0.0039) as well as ex vivo extracellular flux analysis estimates of ATP consumption from glycolysis (R2=0.6684,P=0.0021). Data suggests a significantly lower acidity (MTRasym@3ppm) within non-enhancing tumor in IDH mutant gliomas when compared to IDH wild type gliomas (P&lt; 0.0001). Patients in a phase 1 perioperative study showed a shift toward higher tumor acidity (i.e. higher glycolytic activity) following inhibition of IDH based on 2HG suppression in resected tumors, but at levels below that of IDH wild type gliomas. Levels of 2HG within the tumor after IDH inhibition were inversely correlated with post-treatment tumor acidity (R2=0.6342, P=0.0180). Overall, results suggest mIDH gliomas have low levels of glycolytic activity, and successful inhibition of the mutant IDH enzyme results in reduction in 2HG and a measurable metabolic shift toward elevated glycolysis as evidenced using pH-weighted molecular MRI.