bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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 &lt; 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

Abstract
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&lt; 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

Abstract
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

Abstract

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

Abstract
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&lt; .001), presence of intraventricular lesions(p&lt; .001), and ventricular access during surgery(p&lt; .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&lt; .001), presence of intraventricular lesions(p&lt; .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&lt; .001) was significantly correlated with LMD. In the group of patients without surgery, those with periventricular lesions had significantly higher odds of LMD(p&lt; .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.