bluuebunny/crossref_metadata_2025 huggingface dataset:
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

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

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

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

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

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< 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.
NIMG-42. MP-MRI-BASED TUMOR PROBABILITY MAPS TRAINED USING AUTOPSY TISSUE SAMPLES AS GROUND TRUTH NON-INVASIVELY PREDICT INFILTRATIVE TUMOR BEYOND THE CONTRAST ENHANCING REGION
Authors: Bobholz Samuel , Lowman Allison , Brehler Michael , Sherman John , Duenweg Savannah , Kyereme Fitzgerald , Connelly Jennifer , Cochran Elizabeth , Mueller Wade , Banerjee Anjishnu , LaViolette Peter
Published: November 2021

Infiltrative glioma beyond contrast enhancement on MRI is often difficult to identify with conventional imaging. In this study, we use large-format autopsy samples aligned to multi-parametric MRI to test the hypothesis that radio-pathomic machine learning models are able to accurately identify areas of infiltrative tumor beyond the contrast enhancing region. At autopsy, 140 tissue samples from 62 brain cancer patients were collected from brain slices sectioned to align with the patients’ last clinical MRI prior to death. Cell, extra-cellular fluid (ECF), and cytoplasm densities were computed from digitized, hematoxylin and eosin-stained samples, and a subset of 20 slides from 9 patients were annotated for tumor presence by a pathologist-trained technician. In-house custom software was used to align the tissue samples to the patients’ last clinical imaging, which included pre- and post-contrast T1, FLAIR, and ADC images. Bagging random forest models were then trained to predict cellularity, ECF, and cytoplasm density using 5-by-5 voxel tiles from each MRI as input. A 2/3-1/3 train-test split was used to validate model generalizability. A naïve Bayes classifier was trained to predict tumor class using cellularity, ECF, and cytoplasm segmentations within the annotation data set, again using a 2/3-1/3 train-test split to validate performance. The random forest models each accurately predicted cellularity, ECF, and cytoplasm density within the test data set, with root-mean-squared error values for each falling within one standard deviation of the ground truth. The histology-based tumor prediction model accurately predicted tumor, with a test set ROC AUC of 0.86. When using whole brain cellularity, ECF, and cytoplasm predictions from the random forest models as inputs for the naïve Bayes classifier, tumor probability maps identified regions of infiltrative tumor beyond contrast enhancement. Our results suggest that radio-pathomic maps of tumor probability accurately identify regions of infiltrative tumor beyond currently accepted MRI signatures.
NIMG-46. SYSTEMATIC LITERATURE REVIEW OF ARTIFICIAL INTELLIGENCE ALGORITHMS USING PRE-THERAPY MR IMAGING FOR GLIOMA MOLECULAR SUBTYPE CLASSIFICATION
Authors: Lost Jan , Verma Tej , Tillmanns Niklas , Brim W R , Subramanian Harry , Ikuta Ichiro , Bronen Richard , Zucconi William , Lin Ming , Bousabarah Khaled , Johnson Michele , Cui Jin , Malhotra Ajay , Sabel Michael , Aboian Mariam
Published: November 2021

PURPOSE

Identifying molecular subtypes in gliomas has prognostic and therapeutic value, traditionally after invasive neurosurgical tumor resection or biopsy. Recent advances using artificial intelligence (AI) show promise in using pre-therapy imaging for predicting molecular subtype. We performed a systematic review of recent literature on AI methods used to predict molecular subtypes of gliomas.

METHODS

Literature review conforming to PRSIMA guidelines was performed for publications prior to February 2021 using 4 databases: Ovid Embase, Ovid MEDLINE, Cochrane trials (CENTRAL), and Web of Science core-collection. Keywords included: artificial intelligence, machine learning, deep learning, radiomics, magnetic resonance imaging, glioma, and glioblastoma. Non-machine learning and non-human studies were excluded. Screening was performed using Covidence software. Bias analysis was done using TRIPOD guidelines.

RESULTS

11,727 abstracts were retrieved. After applying initial screening exclusion criteria, 1,135 full text reviews were performed, with 82 papers remaining for data extraction. 57% used retrospective single center hospital data, 31.6% used TCIA and BRATS, and 11.4% analyzed multicenter hospital data. An average of 146 patients (range 34-462 patients) were included. Algorithms predicting IDH status comprised 51.8% of studies, MGMT 18.1%, and 1p19q 6.0%. Machine learning methods were used in 71.4%, deep learning in 27.4%, and 1.2% directly compared both methods. The most common algorithm for machine learning were support vector machine (43.3%), and for deep learning convolutional neural network (68.4%). Mean prediction accuracy was 76.6%.

CONCLUSION

Machine learning is the predominant method for image-based prediction of glioma molecular subtypes. Major limitations include limited datasets (60.2% with under 150 patients) and thus limited generalizability of findings. We recommend using larger annotated datasets for AI network training and testing in order to create more robust AI algorithms, which will provide better prediction accuracy to real world clinical datasets and provide tools that can be translated to clinical practice.
NIMG-52. RADIOGENOMICS SIGNATURES IN KEY DRIVER GENES IN GLIOBLASTOMA EVALUATED WITH AND WITHOUT THE PRESENCE OF CO-OCCURRING MUTATIONS
Authors: Kazerooni Anahita Fathi , Akbari Hamed , Bakas Spyridon , Toorens Erik , Sako Chiharu , Mamourian Elizabeth , Bommineni Vikas , Thakur Nina , Koumenis Costas , Bagley Stephen J , Lustig Robert A , O’Rourke Donald M , Ganguly Tapan , Nasrallah MacLean , Davatzikos Christos
Published: November 2021

PURPOSE

Glioblastomas display significant heterogeneity on the molecular level, typically harboring several co-occurring mutations, which likely contributes to failure of molecularly targeted therapeutic approaches. Radiogenomics has emerged as a promising tool for in vivo characterization of this heterogeneity. We derive radiogenomic signatures of four mutations via machine learning (ML) analysis of multiparametric MRI (mpMRI) and evaluate them in the presence and absence of other co-occurring mutations.

METHODS

We identified a retrospective cohort of 359 IDH-wildtype glioblastoma patients, with available pre-operative mpMRI (T1, T1Gd, T2, T2-FLAIR) scans and targeted next generation sequencing (NGS) data. Radiomic features, including morphologic, histogram, texture, and Gabor wavelet descriptors, were extracted from the mpMRI. Multivariate predictive models were trained using cross-validated SVM with LASSO feature selection to predict mutation status in key driver genes, EGFR, PTEN, TP53, and NF1. ML models and spatial population atlases of genetic mutations were generated for stratification of the tumors (1) with co-occurring mutations versus wildtypes, (2) with exclusive mutations in each driver gene versus the tumors without any mutations in the pathways associated with these genes.

RESULTS

ML models yielded AUCs of 0.75 (95%CI:0.62-0.88) / 0.87 (95%CI:0.70-1) for co-occurring / exclusive EGFR mutations, 0.69 (95%CI:0.58-0.80) / 0.80 (95%CI:0.61-0.99) for co-occurring / exclusive PTEN mutations, and 0.77 (95%CI:0.65-0.88) / 0.86 (95%CI:0.69-1) for co-occurring / exclusive TP53 cases. Spatial atlases revealed a predisposition of left temporal lobe for NF1 and right frontotemporal region for TP53 in mutually exclusive tumors, which was not observed in the co-occurring mutation atlases.

CONCLUSION

Our results suggest the presence of distinct radiogenomic signatures of several glioblastoma mutations, which become even more pronounced when respective mutations do not co-occur with other mutations. These in vivo signatures can contribute to pre-operative stratification of patients for molecular targeted therapies, and potentially longitudinal monitoring of mutational changes during treatment.
NIMG-55. AUGMENTED INTELLIGENCE IS SUPERIOR TO ARTIFICIAL INTELLIGENCE! HUMAN-COMPUTER SYNERGY FOR GENERATING HIGH QUALITY GLIOBLASTOMA SUB-REGION SEGMENTATIONS
Authors: Ghodasara Satyam , Baid Ujjwal , Bakas Spyridon , Bilello Michel , Mohan Suyash
Published: November 2021

PURPOSE

Artificial intelligence (AI) is poised to improve diagnostic methods in neuro-oncologic imaging and contribute to patient management by analyzing pre-operative MRI scans. AI results are better interpreted by compartmentalizing glioblastoma into distinct sub-regions, i.e., necrotic core, enhancing tumor, peritumoral T2/FLAIR signal abnormality (ED). Manual delineation of these sub-regions by expert neuroradiologists is impractical, requiring hours for intricate cases. Computer-aided segmentation (CAS) can mitigate this issue but is limited in the quality of the produced segmentations. We hypothesize that CAS followed by expert refinements is more practical/time-efficient.

METHODS

CAS was used on a total of 359 glioblastoma patients with four MRI sequences (T1, T1Gd, T2, T2-FLAIR) from each patient. All segmentations were sent to expert neuroradiologist annotators for manual refinements. Once refined, our team including two senior attending neuroradiologists with ≥13 years of experience each, reviewed and either approved or returned the segmentations to individual annotators for further refinements. Total time required to refine and review the finalized segmentations was measured.

RESULTS

Following one round of refinements by expert annotators, 244/359 (68%) segmentations were approved by our team while 115/359 (32%) segmentations contained a variety of errors that required a second round of refinements. The most common observed errors were 1) missed ED in the anterior/inferior temporal lobes and corpus callosum (37/115 cases, 32%) and 2) erroneous segmentation of normal choroid plexus and blood vessels (14/115 cases, 12%). The expert annotators required 120 hours to refine all 359 segmentations, and our team required 26 additional hours to review them, resulting in 24 minutes/segmentation following CAS.

CONCLUSION

Our findings support the value of a well-communicated annotation protocol to coordinate CAS and expert annotators. With CAS, our team and expert annotators rapidly finalized segmentations for 359 glioblastoma patients, demonstrating the value of a synergistic approach to creating high quality tumor sub-region segmentations.
NIMG-58. CANONICAL CORRELATION ANALYSIS IN GLIOBLASTOMA REVEALS ASSOCIATIONS BETWEEN EXPRESSION OF RADIOMIC SIGNATURES AND GENOMICS
Authors: Guo Jun , Kazerooni Anahita Fathi , Akbari Hamed , Toorens Erik , Sako Chiharu , Mamourian Elizabeth , Koumenis Costas , Bagley Stephen J , Lustig Robert A , O’Rourke Donald M , Ganguly Tapan , Bakas Spyridon , Nasrallah MacLean , Davatzikos Christos
Published: November 2021

PURPOSE

Understanding the molecular underpinnings of imaging signatures of glioblastoma can provide insights into the biologic basis of tumor formation and progression as well as in vivo surrogate markers of molecular events driving the tumor’s phenotype. Through machine learning (ML), this study demonstrates that distinct imaging subtypes of glioblastoma are related to specific molecular alterations.

METHODS

Pre-operative multi-parametric MRI (T1, T2, T1CE, T2-FLAIR, DSC-MRI, DTI-MRI) of 669 IDH-wildtype subjects with glioblastoma were retrospectively collected and radiomic features, including descriptors of morphology, intensity, histogram, and texture, were extracted. Imaging subtypes were identified by a feature selection and clustering approach. Genomic markers, obtained using next generation sequencing (NGS) panel of 27 key glioblastoma genes, were available in 358/669 patients. Canonical correlation analysis (CCA) was conducted within each imaging subtype between the selected imaging features and genetic variables to seek maximum correlations between combinations of variables in imaging and genomic sets, and hence elucidate the molecular drivers of respective subtypes.

RESULTS

Three distinct imaging subtypes were identified by clustering on 50 selected features, representing characteristics of morphology, tumor neo-angiogenesis (DSC-derived features), and cellular density (DTI-derived features). These subtypes yielded differentiable overall survival based on Kaplan-Meier analysis. The canonical coefficients of each subtype revealed the distinction of the underlying genomic characteristics: one exhibited frequently mutated [ARID2, NTRK1], another subtype showed increased frequency of mutation in [ATRX, EGFR, PIK3R1], while the third was associated with all these genes and [NF1, PIK3CA, RB1], additionally.

CONCLUSION

We derived three distinct radiomic MRI subtypes for glioblastoma that highly correlate with the patients' survival and molecular genetic characteristics. Investigating the relationship between imaging and genomic information may enable identification of molecularly- and phenotypically-consistent tumor subtypes, which would offer non-invasive approaches for characterizing heterogeneity of glioblastoma, further facilitating patient stratification and treatment planning.
NIMG-63. MACHINE LEARNING USING MRI RADIOMIC ANALYSIS TO PREDICT KI-67 IN WHO GRADE I MENINGIOMAS
Authors: Khanna Omaditya , Kazerooni Anahita Fathi , Garcia Jose A , Sako Chiharu , Arif Sherjeel , Shi Wenyin , Davatzikos Christos
Published: November 2021

PURPOSE

Although WHO grade I meningiomas are considered ‘benign’ tumors, an elevated Ki-67 is one crucial factor that has been shown to influence clinical outcomes. In this study, we use standard pre-operative MRI and develop a machine learning (ML) model to predict the Ki-67 in WHO grade I meningiomas.

METHODS

A retrospective analysis was performed of 306 patients that underwent surgical resection. The mean and median Ki-67 of tumor specimens were 4.84 ± 4.03% (range: 0.3–33.6) and 3.7% (Q1:2.3%, Q3:6%), respectively. Pre-operative MRI was used to perform radiomic feature extraction (N=2,520) followed by ML modeling using least absolute shrinkage and selection operator (LASSO) wrapped with support vector machine (SVM) through nested cross-validation on a discovery cohort (N=230), to stratify tumors based on Ki-67 < 5% and ≥ 5%. A replication cohort (N=76) was kept ‘unseen’ in order to provide insights regarding the generalizability of our predictive model.

RESULTS

A total of 60 radiomic features extracted from seven different MRI sequences were used in the final model. With this model, an AUC of 0.84 (95% CI: 0.78-0.90), with associated sensitivity and specificity of 84.1% and 73.3%, respectively, were achieved in the discovery cohort. The selected features in the trained predictive model were then applied to the subjects of the replication cohort and the model was applied independently in this cohort. An AUC of 0.83 (95% CI: 0.73-0.94), with a sensitivity of 82.6% and specificity of 85.5% was obtained for this independent testing. Furthermore, the model performed commendably when applied to all skull base and non-skull base tumors in our patient cohort, evidenced by comparable AUC values of 0.86 and 0.83, respectively.

CONCLUSION

The results of this study may provide enhanced diagnostics to the surgeon pre-operatively such that it can guide surgical strategy and individual patient treatment paradigms.
NIMG-68. SARCOPENIA AS MEASURED BY TEMPORALIS MUSCLE WIDTH IS A PREDICTOR OF SURVIVAL IN PRIMARY CNS LYMPHOMA
Authors: Bonm Alipi , Menghini Anthony , Graber Jerome
Published: November 2021

Sarcopenia refers to a loss of skeletal muscle mass, which has been associated with increased risk of injury and decreased ability to perform activities of daily living. In multiple cancers including systemic lymphomas, sarcopenia has been strongly associated with survival and may be an important way to risk stratify patients in clinical trials as well as routine practice. Temporalis muscle width has been reported as an indicator of sarcopenia and independent predictor of outcomes in multiple settings including glioblastoma, brain metastases and subarachnoid hemorrhage. We evaluated temporalis width in primary CNS lymphoma (PCNSL) patients at presentation and outcomes. Using an institutional database of immunocompetent PCNSL patients treated at the University of Washington, two independent readers reviewed the initial MRIs for 104 patients who presented from 2011-2021 and measured the width of the temporalis muscle on axial T1 images. Median duration of follow up was 42.2 months (range 0.59-125.9 months). Median age at diagnosis was 65 (range 19-90 years), and patients were 42.8% male, 57.2% female. Interrater variability was acceptable with an average intraclass correlation coefficient of 0.934. Temporalis measurements were normally distributed, with mean 0.79 cm and standard deviation 0.18 cm. We divided patients into two groups, those with temporalis width less than or greater than 1 standard deviation below the mean (absolute value 0.60 cm). Temporalis width was strongly associated with survival among all patients (χ2=15.5, p< 0.001) as well as patients 65 years or older (χ2=4.5, p=0.03). We conclude that sarcopenia as measured by temporalis muscle thickness is associated with survival in PCNSL and may be an important variable to consider in clinical trials and routine practice.
NIMG-69. FREQUENCY OF TUMOR-RELATED VASCULATURE IN PRIMARY CNS LYMPHOMA ON ROUTINE MRI
Authors: Shah Trusha , Bonm Alipi , Graber Jerome
Published: November 2021

Primary central nervous system lymphoma (PCNSL) is known to express angiogenic factors and can have spontaneous or biopsy-related hemorrhage. Radiographically, PCNSL is characterized by homogenous enhancement and diffusion restriction, as well as T2/FLAIR abnormalities. A higher frequency of tumor-related vessels has been reported in gliomas compared to brain metastases, but this has not been assessed in PCNSL. We used a clinical database of patients with PCNSL treated at the University of Washington and reviewed pretreatment MRIs for 48 patients treated between 2014-2021 for whom susceptibility sequences were available. Median age at diagnosis was 64 years (range 35-90 years). Patients were 27.1% male and 72.9% female. Each lesion was scored by at least two independent reviewers for the presence of a prominent vessel located either centrally or eccentrically, and for the presence of hemorrhage. Lesions that were scored differently by the two reviewers (n=26) were then scored by a third independent reviewer. In several cases the findings were felt to be indeterminate. Lesions were considered measurable if they were greater than 5 mm in two perpendicular planes, and a total of 93 measurable lesions were categorized. Prominent vessels were seen in 56/93 (60.2%) lesions, with 26/56 (46.4%) being central and 30/56 (55.4%) being eccentric. An additional 7/93 (7.5%) lesions were indeterminate. Hemorrhage was seen in 11/93 (11.8%) lesions. We conclude that prominent vessels can be seen in a majority of PCNSL lesions on standard MRI.
NIMG-75. ANALYZING THE INTERFACE BETWEEN MRI AND DRUG DISTRIBUTION USING ORTHOTOPIC GBM-DERIVED XENOGRAFT (PDX) MODELS
Authors: Channar Sameer , Ranjbar Sara , Jackson Pamela , Hu Leland , Regan Michael , Abdelmoula Walid , Lopez Begona , Macura Slobodan , Agar Jefferey , Sarkaria Jann , Agar Nathalie , Swanson Kristin
Published: November 2021

INTRODUCTION

Glioblastoma (GBM) is a diffusely invasive primary brain tumor with significant spread of tumor cells to the periphery of visible image abnormality. Enhancement of Gadolinium (Gd) contrast agent on magnetic resonance imaging (MRI) has historically been considered a confirmation of local breakdown of the blood brain barrier (BBB) and sufficient drug delivery to the bulk of tumors. In this work, we used GBM-derived xenograft (PDX) models to compare drug delivery in GBM brain for high and low BBB-permeable drugs.

MATERIALS AND METHODS

Five patient-derived orthotopic xenograft models from two GBM cell lines (GBM39 and GBM12) were co-dosed with erlotinib and osimertinib, two drugs with low and high BBB-permeability, respectively. T1Gd and T2-weighted MRIs were acquired from all animals prior to model sacrifice. Tumors were manually segmented on denoised and standardized MRIs and intensity patterns were captured using first and second order statistical features in the moving 3x3 kernel. We compared drug levels found in Matrix Assisted Laser Desorption Ionization (MALDI) in T1Gd enhancement, T2 enhancement, and normal brain. We also performed linear regression modeling to predict drug levels using MRI features. Model performance was measured using root mean squared error (RMSE).

RESULTS

Our analysis showed correlations between imaging features and MALDI drug levels. Osimertinib had a uniform distribution across the brain for all animals and all cell lines, consistent with our expectation for a high BBB-penetrant drug. Erlotinib showed the highest drug levels in T2 for GBM39 and in T1Gd for GBM12. Regression models showed promising results for predicting Erlotinib with a low RMSE of 0.037.

CONCLUSION

Our preliminary results suggest MRI can be predictive of drug levels for low-BBB penetrant drugs. Understanding the relationship between MRIs and drug distribution in diffuse tumors can be beneficial to developing effective treatment.
NCMP-03. ANATOMIC AND SURGICAL FACTORS PREDICT DEVELOPMENT OF LEPTOMENINGEAL DISEASE IN PATIENTS WITH METASTATIC MELANOMA
Authors: Lowe Stephen , Wang Christopher , Brisco Amanda , Arrington John , Ahmed Kamran , Vogelbaum Michael , Liu James
Published: November 2021

Leptomeningeal disease (LMD) is a devastating complication of systemic malignancy, portending a poor prognosis with an estimated median survival of 4-6 weeks if left untreated. Several reports have suggested surgical resection as a potential causative factor. Herein, we explore if surgical and anatomical factors are correlated with development of LMD in patients with melanoma brain metastases. METHODS: Patients treated at our institution between 1999-2019 for primary melanoma with brain metastasis were compiled into a database based on ICD9/10 coding. 1,079 patients with melanoma brain metastases and appropriate imaging were identified, and 834 patients with a minimum of 3 months’ follow up were included. Patients were dichotomized by development of LMD or lack thereof, and categorized into an overall cohort, and surgical and non-surgical cohorts. Anatomic factors and ventricular access during surgery were investigated as possible correlative factors for the development of LMD. RESULTS: In the overall cohort, female gender(p=0.033), presence of dural metastasis(p=0.018), presence of periventricular lesions(p< .001), presence of intraventricular lesions(p< .001), and ventricular access during surgery(p< .001) were significantly associated with LMD. Patients undergoing surgery, or those undergoing surgery without ventricular access, were not at higher risk of LMD. On multivariate analysis, female gender(p=.033), presence of periventricular lesions (p< .001), presence of intraventricular lesions(p< .002), and presence of dural metastasis(p=0.032) were significantly associated with development of LMD. In patients who had surgery, iatrogenic ventricular access(p< .001) was significantly correlated with LMD. In the group of patients without surgery, those with periventricular lesions had significantly higher odds of LMD(p< .001). CONCLUSIONS: In a retrospective cohort of patients with melanoma metastatic to the brain, surgical intervention does not increase odds of LMD; however, iatrogenic access to the CSF space during surgery is highly correlated with LMD development. Anatomic contact with the CSF space predicts LMD regardless of surgical status.
NCMP-19. RADIATION-INDUCED CRANIAL NEUROPATHY PRESENTING AS PROGRESSIVE BULBAR SYNDROME
Authors: Correia Carlos Eduardo Silva , Chen Xi
Published: November 2021

Neuropathy is a well-known complication of radiation, especially when administered in proximity to the brachial plexus (BC), with symptoms usually appearing years after radiation. Pain is a less prominent feature when compared to neoplastic infiltration, with numbness and weakness being a more common and disabling complaint. Radiation-induced neuropathies tend to reach clinical stabilization after several months with por response to treatment. This is a case report of a 59 year-old Malaysian man who was diagnosed with nasopharyngeal carcinoma in 2002 treated with radiation to the nasopharynx in China. He has no evidence of disease to this date. In 2016 he started to develop progressive bulbar symptoms, such as hoarseness, hypophonia, and dysphagia. Symptoms worsened over the subsequent 18 months after presentation with clinical stabilization thereafter. Following the initial bulbar symptoms, he also developed bilateral hearing loss, atrophy of bilateral sternocleidomastoid muscles, significant tongue atrophy with fasciculations and tongue deviation. His clinical picture was consistent with bulbar amyotrophic lateral sclerosis. MRI brain and total spine did not show any intraparenchymal or nerve disease. An electromyography test was performed, which showed myokymia of the tongue and trapezius consistent with radiation-induced cranial neuropathy (CN). He received steroids without any clinical improvement, followed by hyperbaric oxygen with minimal improvement of his dysphagia, hoarseness and hypophonia. On last follow up his symptoms continued to be stable. Radiation to nasopharynx can cause radiation-induced CN with similar electromyographic findings to radiation-induced brachial plexopathy, such as myokymia. Symptoms can appear more than 10 years from radiotherapy, with progression over several months followed by clinical stabilization. Treatment with steroids, bevacizumab or hyperbaric oxygen only provide minimal benefit on this population.
NCOG-10. RADIATION THERAPY CAN BE SAFELY DEFERRED IN OLIGODENDROGLIOMAS
Authors: Demopoulos Alexis , Knisely Jonathan
Published: November 2021

Deferring multimodal aggressive therapies in young patients to delay treatment-induced toxicity without jeopardizing long-term outcome would be of great benefit to our patients. After IRB approval, we retrospectively reviewed 80 confirmed IDH mutant and 1p19q codeleted oligodendrogliomas treated at one institution between 2005 and 2020. Median follow-up was 5 (range 1-26) years. All patients underwent maximal safe resection, followed by observation with routine imaging (n=28), chemotherapy alone (n=27), or radiation with chemotherapy (n=25) as initial upfront therapy. Median progression free survival was 36 (range 1-203), 54 (range 1-306), and 57 (range 4-281) months, respectively. Median overall survival was not reached, with 85% (67/80) alive, 8 on treatment and 59 stable off therapy. Among 35 patients who died or were followed for 10 years, median PFS was 12, 15, and 10 years for observation (n=9), chemotherapy (n=11) and chemoradiotherapy (n=15), with deaths or KPS below 50 in 3, 6, and 8, respectively. Three deaths in the observation group occurred 12, 12 and 17 years after diagnosis; one at 95 years old and another tumor-unrelated. Among 44 patients eventually receiving radiation, 15 suffered toxicity, including pathologically proven necrosis (n=6), cognitive decline with KPS< 50 (n=5), memory loss with KPS > 50 (n=3), and optic neuropathy (n=1). Myelosuppression from PCV was more pronounced after chemoradiation than in the upfront setting. Temozolomide after PCV chemotherapy was well tolerated. Long term follow-up of oligodendroglioma patients is challenging, but essential in determining late toxicities and treatment efficacy. Long-term results of European and North American multicenter cooperative group trials contradicted earlier publications reporting no benefit from early chemotherapy. Some management practice guidelines established a half-century ago persist (i.e., administer radiation therapy early), despite potentially crippling late effects. Deferring upfront radiation therapy is safe, less toxic, and equally efficacious in codeleted oligodendrogliomas.
NCOG-36. LONG-TERM SURVIVAL AND COGNITIVE FUNCTION FOLLOW UP OF PRIMARY CNS LYMPHOMA TREATED WITH R-MVP FOLLOWED BY HIGH DOSE CHEMOTHERAPY WITH AUTOLOGOUS STEM CELL TRANSPLANT CONSOLIDATION
Authors: Therkelsen Kate , Grommes Christian
Published: November 2021

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a rare central nervous system malignancy, and long-term follow up studies are uncommon. First line therapy is based on high-dose methotrexate and different consolidation therapy options. This is a long-term follow up study of PCNSL patients enrolled in a prospective trial using R-MPV chemotherapy regimen followed by high dose chemotherapy and autologous stem cell rescue to determine long-term survival and cognitive effects.

METHODS

From June 2005 to September 2011, 32 newly diagnosed immunocompentent PCNSL were enrolled. Patients received 5-7 doses of rituximab (500mg/m2), methotrexate (3.5 gm/m2), procarbazine (100mg/m2), and vincristine (1.4mg/m2) (R-MVP). Consolidation therapy consisted of high dose chemotherapy (HDC) with thiotepa (250 mg/m2), busulfan (3.2 mg/kg), and cyclophosphamide (60 mg/kg), followed by autologous stem cell rescue (ASCT) in those with partial or complete response to R-MVP. Long-term follow-up status including disease status, cognitive status (KPS, NANO score), and leukoencephalopathy (modified Fazkas Scale) were collected.

RESULTS

26 of 36 underwent HDC and ACST. Of those, 3 died due to treatment related effects; 2 died of disease progression within two years after ASCT. After a median follow-up of 10.5 years, none of the remaining 21 patients progressed. At the time of last follow up, the median KPS was 90, compared to 80 at time of ASCT. The median NANO score and leukoencephalopathy score post ASCT and at follow-up did not change. Of note, 2 of 4 patients with a partial or complete response to R-MVP that elected not to proceed with HDC-ASCT consolidation, experienced progression at a mean of 52 months.

CONCLUSION

Long-term follow up demonstrates that treatment was tolerated well with stable leukoencephalopathy on MRI and good performance status. Disease recurrence 2 years after HDC with ASCT consolidation was not observed.
NCOG-37. THE UTILITY OF NEUROPSYCHOLOGICAL SCREENING IN MULTIDISCIPLINARY CLINICS FOR PEDIATRIC NEURO-ONCOLOGY POPULATIONS
Authors: Mietchen Jonathan , Kessler-Jones Alanna , Damodharan Shawn , Puccetti Diane
Published: November 2021

BACKGROUND

The purpose of this project was to examine the utility of neuropsychological screening in multidisciplinary clinics for pediatric neuro-oncology populations and determine whether a brief evaluation can adequately screen children for cognitive dysfunction or psychiatric comorbidities.

METHODS

Children underwent neuropsychological screening as part of their multidisciplinary clinic visit. This screening evaluation consisted of clinical interview, neuropsychological testing, and the completion of emotional/behavioral checklists. Our neuropsychological test battery included the Kaufman Brief Intelligence Test-Second Edition (KBIT-2) and the NIH Toolbox Fluid Cognition Composite (consisting of 6 subtests).

RESULTS

Over a six-month period 40 children and adolescents with CNS tumors and/or NF1 were seen in our neuropsychology screening clinic. Females made up 37.5% of the sample. The average age was 11.1 years (range=5-25 years). The average time to complete neuropsychological testing was 56.8 minutes. On average, children in this sample met criteria for 1.8 neuropsychological diagnoses with an average of 1.2 new diagnoses that had not been given prior to their neuropsychological screening. The most common new diagnoses were major or mild neurocognitive disorder (mild=27.5%, major=20%), anxiety (27.5%), ADHD (15%), mood disorder (12.5%), and intellectual disability (10%). On average, these children performed lower than expectation on neurocognitive tests, including intellectual functioning (KBIT-2 Verbal IQ=93.1; KBIT-2 Nonverbal IQ=88.7; KBIT-2 Total IQ=90.1), attention (NIH Flanker T-Score=44.1), working memory (NIH Working Memory T-Score=42.9), processing speed (NIH Processing Speed T-Score=39.9), cognitive flexibility (NIH Card Sort T-Score=43.4), memory (NIH Picture Memory T-Score=44.5), and fluid cognition (NIH Fluid Cognition Composite T-Score=38.7). Behavioral health recommendations were needed for 72.5% of this sample. Educational recommendations were needed for 62.5%.

CONCLUSIONS

Brief neuropsychological screening completed as part of multidisciplinary clinics for pediatric neuro-oncology patients was efficient and useful in identifying neuropsychological diagnoses in children.
NCOG-41. HISTOLOGICAL ANALYSIS OF SLEEP AND CIRCADIAN BRAIN CIRCUITRY IN CRANIAL RADIATION-INDUCED HYPERSOMNOLENCE (C-RIH) MOUSE MODEL
Authors: Shuboni-Mulligan Dorela , Young Jr. Demarrius , Minyety Julianie De La Cruz , Briceno Nicole , King Amanda , Gilbert Mark , Smart DeeDee , Armstrong Terri
Published: November 2021

BACKGROUND

Disrupted sleep, including daytime hypersomnolence, is a core symptom reported by primary brain tumor patients and often manifests after radiotherapy. The biological mechanisms driving cranial radiation-induced hypersomnolence (C-RIH) remain unclear but we hypothesize this may result from damage to neural circuits controlling sleep behavior. We developed a mouse model of C-RIH to explore the impact of radiation on the brain: examining region-specific differences in acute DNA damage response and neuroanatomic structure.

METHODS

Mice received whole brain radiation then behaviors were monitored using PhenoTyper® cages to determine optimal dose and long-term effects. To test short-term neurologic effects, brains were collected 1hr post-radiation then stained for γH2AX, a signal for DNA damage. Long-term effects were quantified 1-month post-treatment using neuroimaging to determine brain volume and T1 mapping changes in regions associated with sleep, circadian rhythms, and cognition.

RESULTS

Mice displayed decreased general activity and increased daytime sleep in a dose-dependent and sustained manner. Histologic staining demonstrated that DNA damage following radiation varies across the brain, with homeostatic sleep regions and cognitive regions expressing higher levels of γH2AX than the circadian suprachiasmatic nucleus. These findings were supported by in vitro studies comparing radiation effects in SCN and cortical astrocytes using both trypan blue (F(1,18)=235.937, p< 0.001) and clonogenic assays (F(1,24)=40.796, < 0.001). Brain volumes were significantly smaller in irradiated than sham animals in the hippocampus (t(4)=3.833, p=0.019) and the pontine central grey (t(4)=3.504, p=0.025). T1 maps also showed significant changes in relaxation times in many cognitive regions but not sleep or circadian areas.

CONCLUSIONS

These findings suggest that the homeostatic sleep region and cognitive circuits are vulnerable to radiation and may be relevant to the development of treatment plans in patients. We plan to introduce intracranial tumor to the model to evaluate the impact of timing of treatment and C-RIH on survival.