Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease

INTRODUCTION

Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression.

METHODS

We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes.

RESULTS

We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression.

DISCUSSION

Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems.

HIGHLIGHTS

NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.

Ketogenic diet therapy for pediatric epilepsy is associated with alterations in the human gut microbiome that confer seizure resistance in mice

The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid β-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.

Olfactory ensheathing cells are hybrid glial cells that promote neural repair

Olfactory ensheathing cells (OECs) are unique glial cells found in both the central and peripheral nervous systems where they support the continuous axonal outgrowth of immature olfactory sensory neurons to their targets. Here we show that following severe spinal cord injury, olfactory bulb-derived OECs transplanted near the injury site modify the normally inhibitory glial scar and facilitate axon regeneration past the scar border and into the lesion center. To understand the mechanisms underlying the reparative properties of such transplanted OECs, we used single-cell RNA-sequencing to study their gene expression programs. Our analyses revealed five diverse subtypes of OECs, each expressing novel marker genes and pathways indicative of progenitor, axonal regeneration and repair, secreted molecules, or microglia-like functions. As expected, we found substantial overlap of OEC genes with those of Schwann cells, but also with astrocytes, oligodendrocytes and microglia. We confirmed established markers on cultured OECs, and then localized select top genes of OEC subtypes in rat olfactory bulb tissue. In addition, we present evidence that OECs secrete both Reelin and Connective tissue growth factor, extracellular matrix molecules which are important for neural repair and axonal outgrowth. Our results support that adult OECs are a unique hybrid glia, some with progenitor characteristics, and that their gene expression patterns indicate diverse functions related to wound healing, injury repair and axonal regeneration.

Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes

IMPORTANCE

Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.

Prostate lineage-specific metabolism governs luminal differentiation and response to antiandrogen treatment

Lineage transitions are a central feature of prostate development, tumourigenesis and treatment resistance. While epigenetic changes are well known to drive prostate lineage transitions, it remains unclear how upstream metabolic signalling contributes to the regulation of prostate epithelial identity. To fill this gap, we developed an approach to perform metabolomics on primary prostate epithelial cells. Using this approach, we discovered that the basal and luminal cells of the prostate exhibit distinct metabolomes and nutrient utilization patterns. Furthermore, basal-to-luminal differentiation is accompanied by increased pyruvate oxidation. We establish the mitochondrial pyruvate carrier and subsequent lactate accumulation as regulators of prostate luminal identity. Inhibition of the mitochondrial pyruvate carrier or supplementation with exogenous lactate results in large-scale chromatin remodelling, influencing both lineage-specific transcription factors and response to antiandrogen treatment. These results establish reciprocal regulation of metabolism and prostate epithelial lineage identity.

Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia

In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease.

Highlights

▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Scalable NMF via linearly optimized data compression

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

Neurodevelopmental patterns of early postnatal white matter maturation represent distinct underlying microstructure and histology

Cerebral white matter undergoes a rapid and complex maturation during the early postnatal period. Prior magnetic resonance imaging (MRI) studies of early postnatal development have often been limited by small sample size, single-modality imaging, and univariate analytics. Here, we applied nonnegative matrix factorization, an unsupervised multivariate pattern analysis technique, to T2w/T1w signal ratio maps from the Developing Human Connectome Project (n = 342 newborns) revealing patterns of coordinated white matter maturation. These patterns showed divergent age-related maturational trajectories, which were replicated in another independent cohort (n = 239). Furthermore, we showed that T2w/T1w signal variations in these maturational patterns are explained by differential contributions of white matter microstructural indices derived from diffusion-weighted MRI. Finally, we demonstrated how white matter maturation patterns relate to distinct histological features by comparing our findings with postmortem late fetal/early postnatal brain tissue staining. Together, these results delineate concise and effective representation of early postnatal white matter reorganization.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset

PURPOSE

The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying "reproducible" radiomic features that are robust to segmentation variability across readers/sites.

ACQUISITION AND VALIDATION METHODS

From TCIA's Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman's rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families.

DATA FORMAT AND USAGE NOTES

We make publicly available on TCIA's Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis.

POTENTIAL APPLICATIONS

The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM.

NIMG-40. RADIOGENOMIC SIGNATURES OF DRIVER GENES IN NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS BASED ON PRE-OPERATIVE MULTI-PARAMETRIC MRI

PURPOSE

Non-invasive and quantitative biomarkers of somatic mutations derived from multi-parametric MRI (MP-MRI) could potentially help in predicting the response of patients to therapy, leading to development of targeted and personalized treatments. In this study, we developed radiogenomic signatures of multiple driver genes using artificial intelligence (AI) methods.

METHODS

In this study, 2740 radiomic features, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM), were extracted from pre-operative MP-MRI (T1, T1Gd, T2, T2-FLAIR, DTI, and DSC-MRI) scans of 161 patients with newly diagnosed glioblastoma. The tumor samples, collected surgically from these patients, were sequenced using an in-house targeted next generation sequencing (NGS) panel of genes. We constructed quantitative imaging signatures of somatic mutations in several genes from 161 IDH-wildtype glioblastoma patients, including ATRX, FGFR2, EGFR, MET, NF1, PDGFRA, PIK3CA, PTEN, RB1, TP53, using cross-validated SVM classifiers.

RESULTS

The cross-validated classification performance for each signature was assessed by area under the receiver operating characteristic (ROC) curve (AUC), indicating the following results: PTEN (n = 69, AUC = 0.64), EGFR (n = 52, AUC = 0.72), TP53 (n = 51, AUC = 0.67), NF1 (n = 33, AUC = 0.74), ATRX (n = 22; AUC = 0.74), FGFR2 (n = 6, AUC = 0.82), MET (n = 26, AUC = 0.77), PDGFRA (n = 14, AUC = 0.82), PIK3CA (n = 14, AUC = 0.78), RB1 (n = 14, AUC = 0.81).

CONCLUSION

Using multi-parametric MRI, we developed quantitative non-invasive in vivo signatures with the potential for pre-operative assessment of a glioblastoma’s molecular characteristics. These non-invasive radiogenomic biomarkers may be useful for understanding the molecular composition of a glioblastoma prior to surgical resection, thus enabling earlier selection of patients for targeted therapy trials and possible neoadjuvant treatment.

Cancer Imaging Phenomics via CaPTk: Multi-Institutional Prediction of Progression-Free Survival and Pattern of Recurrence in Glioblastoma

PURPOSE

To construct a multi-institutional radiomic model that supports upfront prediction of progression-free survival (PFS) and recurrence pattern (RP) in patients diagnosed with glioblastoma multiforme (GBM) at the time of initial diagnosis.

PATIENTS AND METHODS

We retrospectively identified data for patients with newly diagnosed GBM from two institutions (institution 1, n = 65; institution 2, n = 15) who underwent gross total resection followed by standard adjuvant chemoradiation therapy, with pathologically confirmed recurrence, sufficient follow-up magnetic resonance imaging (MRI) scans to reliably determine PFS, and available presurgical multiparametric MRI (MP-MRI). The advanced software suite Cancer Imaging Phenomics Toolkit (CaPTk) was leveraged to analyze standard clinical brain MP-MRI scans. A rich set of imaging features was extracted from the MP-MRI scans acquired before the initial resection and was integrated into two distinct imaging signatures for predicting mean shorter or longer PFS and near or distant RP. The predictive signatures for PFS and RP were evaluated on the basis of different classification schemes: single-institutional analysis, multi-institutional analysis with random partitioning of the data into discovery and replication cohorts, and multi-institutional assessment with data from institution 1 as the discovery cohort and data from institution 2 as the replication cohort.

RESULTS

These predictors achieved cross-validated classification performance (ie, area under the receiver operating characteristic curve) of 0.88 (single-institution analysis) and 0.82 to 0.83 (multi-institution analysis) for prediction of PFS and 0.88 (single-institution analysis) and 0.56 to 0.71 (multi-institution analysis) for prediction of RP.

CONCLUSION

Imaging signatures of presurgical MP-MRI scans reveal relatively high predictability of time and location of GBM recurrence, subject to the patients receiving standard first-line chemoradiation therapy. Through its graphical user interface, CaPTk offers easy accessibility to advanced computational algorithms for deriving imaging signatures predictive of clinical outcome and could similarly be used for a variety of radiomic and radiogenomic analyses.

Brain extraction on MRI scans in presence of diffuse glioma: Multi-institutional performance evaluation of deep learning methods and robust modality-agnostic training

Brain extraction, or skull-stripping, is an essential pre-processing step in neuro-imaging that has a direct impact on the quality of all subsequent processing and analyses steps. It is also a key requirement in multi-institutional collaborations to comply with privacy-preserving regulations. Existing automated methods, including Deep Learning (DL) based methods that have obtained state-of-the-art results in recent years, have primarily targeted brain extraction without considering pathologically-affected brains. Accordingly, they perform sub-optimally when applied on magnetic resonance imaging (MRI) brain scans with apparent pathologies such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. In this study, we present a comprehensive performance evaluation of recent deep learning architectures for brain extraction, training models on mpMRI scans of pathologically-affected brains, with a particular focus on seeking a practically-applicable, low computational footprint approach, generalizable across multiple institutions, further facilitating collaborations. We identified a large retrospective multi-institutional dataset of n=3340 mpMRI brain tumor scans, with manually-inspected and approved gold-standard segmentations, acquired during standard clinical practice under varying acquisition protocols, both from private institutional data and public (TCIA) collections. To facilitate optimal utilization of rich mpMRI data, we further introduce and evaluate a novel ''modality-agnostic training'' technique that can be applied using any available modality, without need for model retraining. Our results indicate that the modality-agnostic approach1 obtains accurate results, providing a generic and practical tool for brain extraction on scans with brain tumors.

Overall survival prediction in glioblastoma patients using structural magnetic resonance imaging (MRI): advanced radiomic features may compensate for lack of advanced MRI modalities

Purpose: Glioblastoma, the most common and aggressive adult brain tumor, is considered noncurative at diagnosis, with 14 to 16 months median survival following treatment. There is increasing evidence that noninvasive integrative analysis of radiomic features can predict overall and progression-free survival, using advanced multiparametric magnetic resonance imaging (Adv-mpMRI). If successfully applicable, such noninvasive markers can considerably influence patient management. However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1, T1-Gd, T2, and T2-fluid-attenuated inversion recovery) preoperatively, rather than Adv-mpMRI that provides additional vascularization (dynamic susceptibility contrast-MRI) and cell-density (diffusion tensor imaging) related information. Approach: We assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP, i.e., intensity, volume, location, and growth model parameters) extracted from Adv-mpMRI can yield accurate overall survival stratification. We focus on demonstrating that equally accurate prediction models can be constructed using augmented radiomic feature panels (ARFPs, i.e., integrating morphology and textural descriptors) extracted solely from widely available Bas-mpMRI, obviating the need for using Adv-mpMRI. We extracted 1612 radiomic features from distinct tumor subregions to build multivariate models that stratified patients as long-, intermediate-, or short-survivors. Results: The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77% and degraded to 60.89% when using only Bas-mpMRI. However, utilizing the ARFP on Bas-mpMRI improved the accuracy to 74.26%. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using ARFP extracted from Bas-mpMRI. Conclusions: This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible using solely Bas-mpMRI and integrative advanced radiomic features, which can compensate for the lack of Adv-mpMRI. Our finding holds promise for generalization across multiple institutions that may not have access to Adv-mpMRI and to better inform clinical decision-making about aggressive interventions and clinical trials.

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

The purpose of this manuscript is to provide an overview of the technical specifications and architecture of the Cancer imaging Phenomics Toolkit (CaPTk www.cbica.upenn.edu/captk), a cross-platform, open-source, easy-to-use, and extensible software platform for analyzing 2D and 3D images, currently focusing on radiographic scans of brain, breast, and lung cancer. The primary aim of this platform is to enable swift and efficient translation of cutting-edge academic research into clinically useful tools relating to clinical quantification, analysis, predictive modeling, decision-making, and reporting workflow. CaPTk builds upon established open-source software toolkits, such as the Insight Toolkit (ITK) and OpenCV, to bring together advanced computational functionality. This functionality describes specialized, as well as general-purpose, image analysis algorithms developed during active multi-disciplinary collaborative research studies to address real clinical requirements. The target audience of CaPTk consists of both computational scientists and clinical experts. For the former it provides i) an efficient image viewer offering the ability of integrating new algorithms, and ii) a library of readily-available clinically-relevant algorithms, allowing batch-processing of multiple subjects. For the latter it facilitates the use of complex algorithms for clinically-relevant studies through a user-friendly interface, eliminating the prerequisite of a substantial computational background. CaPTk's long-term goal is to provide widely-used technology to make use of advanced quantitative imaging analytics in cancer prediction, diagnosis and prognosis, leading toward a better understanding of the biological mechanisms of cancer development.

The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

Integrative radiomic analysis for pre-surgical prognostic stratification of glioblastoma patients: from advanced to basic MRI protocols

Glioblastoma, the most common and aggressive adult brain tumor, is considered non-curative at diagnosis. Current literature shows promise on imaging-based overall survival prediction for patients with glioblastoma while integrating advanced (structural, perfusion, and diffusion) multipara metric magnetic resonance imaging (Adv-mpMRI). However, most patients prior to initiation of therapy typically undergo only basic structural mpMRI (Bas-mpMRI, i.e., T1,T1-Gd,T2,T2-FLAIR) pre-operatively, rather than Adv-mpMRI. Here we assess a retrospective cohort of 101 glioblastoma patients with available Adv-mpMRI from a previous study, which has shown that an initial feature panel (IFP) extracted from Adv-mpMRI can yield accurate overall survival stratification. We further focus on demonstrating that equally accurate prediction models can be constructed using augmented feature panels (AFP) extracted solely from Bas-mpMRI, obviating the need for using Adv-mpMRI. The classification accuracy of the model utilizing Adv-mpMRI protocols and the IFP was 72.77%, and improved to 74.26% when utilizing the AFP on Bas-mpMRI. Furthermore, Kaplan-Meier analysis demonstrated superior classification of subjects into short-, intermediate-, and long-survivor classes when using AFPon Basic-mpMRI. This quantitative evaluation indicates that accurate survival prediction in glioblastoma patients is feasible by using solely Bas-mpMRI and integrative radiomic analysis can compensate for the lack of Adv-mpMRI. Our finding holds promise for predicting overall survival based on commonly-acquired Bas-mpMRI, and hence for potential generalization across multiple institutions that may not have access to Adv-mpMRI, facilitating better patient selection.

Application of the Whole Genome-Based Bacterial Identification System, TrueBac ID, Using Clinical Isolates That Were Not Identified With Three Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) Systems

BACKGROUND

Next-generation sequencing is increasingly used for taxonomic identification of pathogenic bacterial isolates. We evaluated the performance of a newly introduced whole genome-based bacterial identification system, TrueBac ID (ChunLab Inc., Seoul, Korea), using clinical isolates that were not identified by three matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) systems and 16S rRNA gene sequencing.

METHODS

Thirty-six bacterial isolates were selected from a university-affiliated hospital and a commercial clinical laboratory. Species was identified by three MALDI-TOF MS systems: Bruker Biotyper MS (Bruker Daltonics, Billerica, MA, USA), VITEK MS (bioMérieux, Marcy l'Étoile, France), and ASTA MicroIDSys (ASTA Inc., Suwon, Korea). Whole genome sequencing was conducted using the Illumina MiSeq system (Illumina, San Diego, CA, USA), and genome-based identification was performed using the TrueBac ID cloud system (www.truebacid.com).

RESULTS

TrueBac ID assigned 94% (34/36) of the isolates to known (N=25) or novel (N=4) species, genomospecies (N=3), or species group (N=2). The remaining two were identified at the genus level.

CONCLUSIONS

TrueBac ID successfully identified the majority of isolates that MALDI-TOF MS failed to identify. Genome-based identification can be a useful tool in clinical laboratories, with its superior accuracy and database-driven operations.

NIMG-35. QUANTITATIVE ESTIMATION OF PROGRESSION-FREE SURVIVAL BASED ON RADIOMICS ANALYSIS OF PREOPERATIVE MULTI-PARAMETRIC MRI IN PATIENTS WITH GLIOBLASTOMA

PURPOSE

Glioblastoma managed with maximal resection and adjuvant chemoradiation has large heterogeneity at the time of tumor recurrence (progression-free survival, PFS). Upfront identification of patients with shorter than median PFS, may facilitate better personalization of treatment, such as patient stratification into clinical trials for treatment intensification and/or supportive care. The aim of the current study was to build a radiomics model that can reliably predict PFS in patients with glioblastoma at the time of initial diagnosis. EXPERIMENTAL DESIGN: A total of 66 de novo glioblastoma patients in a single institution who underwent gross total resection followed by standard adjuvant chemoradiation, were found to have pathologically confirmed recurrence. Patients who had sufficient follow-up MRI to reliably determine PFS and available pre-surgical multi-parametric MRI (mpMRI) (T1, T1Gd, T2, T2-FLAIR, DTI, DSC-MR images) were included. The patients were stratified into two classes of “short PFS (≤7 months, n=35)” and “long PFS (≥9 months, n=19)”. An extensive set of features was extracted from preoperative mpMRI scans, including shape and volumetric measures computed for different tumorous regions, and characteristics derived from histograms and gray-level co-occurrence matrix (GLCM). Predictive modeling for discrimination of patients with short from those with long PFS was performed using a 10-fold cross-validated support vector machine classifier.

RESULTS

An accuracy of 79.6% was achieved for prediction of short and long PFS (sensitivity=83%, specificity=74%, AUC=0.86 [95%CI: 0.76–0.96]; short-PSF was considered the positive class). The patients with shorter PFS showed markedly higher cell density in the nonenhancing tumor (DTI), higher neo-angiogenesis in the enhancing tumor (DSC-MRI, T1Gd), lower water concentration in the enhancing tumor region (T2), and higher maximum enhancement (calculated by the subtraction of T1 from T1Gd) in the edema region.

CONCLUSION

Quantitative assessment of preoperative mpMRI provides accurate prediction of PFS for patients with glioblastoma, which can aid in personalized treatment planning.

TMOD-40. IN VIVO EVALUATION OF O6-METHYLGUANINE-DNA-METHYLTRANSFERASE (MGMT) PROMOTER METHYLATION STATUS FOR DE NOVO GLIOBLASTOMA PATIENTS USING DEEP LEARNING FEATURES

BACKGROUND

High expression of O6-methylguanine-DNA methyltransferase (MGMT) in glioblastoma is associated with resistance to temozolomide, as tumor cells lacking MGMT activity are significantly more sensitive to the cytotoxic effects of temozolomide. The MGMT promoter methylation status (MGMTpms) is typically determined as MGMT-methylated or MGMT-unmethylated. Some single-center studies have reported results ranging from 70–95% detection rates using MRI. We aim to further validate these findings using a multi-institutional data set. We hypothesize that transfer learning based features when integrated via machine learning may lead to non-invasive determination of MGMTpms.

METHODS

A total of 270 patients were included across the 3 institutions (Hospital of the University of Pennsylvania (HUP), Jefferson University Hospital (JUH); the TCIA). JUH and TCIA datasets comprised conventional modalities (T1,T2,T2-FLAIR,T1-Gd), whereas HUP dataset had additional modalities (DSC,DTI) as well. We used transfer learning and adapted a convolutional neural network (CNN) model pre-trained on 1.2 million 3-channel images of the ImageNet to extract deep learning features from the given images. A support vector machine multivariately integrated these features towards a non-invasive marker of MGMTpms.

RESULTS

The cross-validated accuracy of our MGMT marker in classifying the mutation status in individual patients was 86.95%, 81.56%, and 82.43%, respectively, in HUP, JUH, and TCIA. Our marker revealed MGMT-methylated tumors with lower neovascularization and cell density, when compared with MGMT-unmethylated tumors. MGMT-unmethylated tumors were found to be more lateralized to the right hemisphere, when compared with MGMT-methylated tumors.

CONCLUSION

Our findings suggest that transfer learning features when integrated via machine learning allow robust prediction of MGMTpms on mpMRI acquired within multiple institutions. The proposed non-invasive MGMT marker may contribute to (i) MGMTpms determination for patients with inadequate tissue/inoperable tumors, (ii) stratification of patients into clinical trials, (iii) patient selection for targeted therapy, and (iv) personalized treatment planning.

Skull-Stripping of Glioblastoma MRI Scans Using 3D Deep Learning

Skull-stripping is an essential pre-processing step in computational neuro-imaging directly impacting subsequent analyses. Existing skull-stripping methods have primarily targeted non-pathologicallyaffected brains. Accordingly, they may perform suboptimally when applied on brain Magnetic Resonance Imaging (MRI) scans that have clearly discernible pathologies, such as brain tumors. Furthermore, existing methods focus on using only T1-weighted MRI scans, even though multi-parametric MRI (mpMRI) scans are routinely acquired for patients with suspected brain tumors. Here we present a performance evaluation of publicly available implementations of established 3D Deep Learning architectures for semantic segmentation (namely DeepMedic, 3D U-Net, FCN), with a particular focus on identifying a skull-stripping approach that performs well on brain tumor scans, and also has a low computational footprint. We have identified a retrospective dataset of 1,796 mpMRI brain tumor scans, with corresponding manually-inspected and verified gold-standard brain tissue segmentations, acquired during standard clinical practice under varying acquisition protocols at the Hospital of the University of Pennsylvania. Our quantitative evaluation identified DeepMedic as the best performing method (Dice = 97.9, Hausdorf f 95 = 2.68). We release this pre-trained model through the Cancer Imaging Phenomics Toolkit (CaPTk) platform.

Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development

We explored the clinical and pathological impact of epidermal growth factor receptor (EGFR) extracellular domain missense mutations. Retrospective assessment of 260 de novo glioblastoma patients revealed a significant reduction in overall survival of patients having tumors with EGFR mutations at alanine 289 (EGFR^A289D/T/V). Quantitative multi-parametric magnetic resonance imaging analyses indicated increased tumor invasion for EGFR^A289D/T/V mutants, corroborated in mice bearing intracranial tumors expressing EGFR^A289V and dependent on ERK-mediated expression of matrix metalloproteinase-1. EGFR^A289V tumor growth was attenuated with an antibody against a cryptic epitope, based on in silico simulation. The findings of this study indicate a highly invasive phenotype associated with the EGFR^A289V mutation in glioblastoma, postulating EGFR^A289V as a molecular marker for responsiveness to therapy with EGFR-targeting antibodies.

Thermally conductive polyamide 6/carbon filler composites based on a hybrid filler system

We explored the use of a hybrid filler consisting of graphite nanoplatelets (GNPs) and single walled carbon nanotubes (SWCNTs) in a polyamide 6 (PA 6) matrix. The composites containing PA 6, powdered GNP, and SWCNT were melt-processed and the effect of filler content in the single filler and hybrid filler systems on the thermal conductivity of the composites was examined. The thermal diffusivities of the composites were measured by the standard laser flash method. Composites containing the hybrid filler system showed enhanced thermal conductivity with values as high as 8.8 W (m · K)^-1, which is a 35-fold increase compared to the thermal conductivity of pure PA 6. Thermographic images of heat conduction and heat release behaviors were consistent with the thermal conductivity results, and showed rapid temperature jumps and drops, respectively, for the composites. A composite model based on the Lewis-Nielsen theory was developed to treat GNP and SWCNT as two separate types of fillers. Two approaches, the additive and multiplicative approaches, give rather good quantitative agreement between the predicted values of thermal conductivity and those measured experimentally.

Thermal conductivity improvement of surface-enhanced polyetherimide (PEI) composites using polyimide-coated h-BN particles

In this study, we investigated the thermal conductivities and mechanical properties of polyetherimide (PEI) composites using polyimide (PI)-coated h-BN (PI-BN) particles. We found that PI-coated h-BN effectively increased adhesion with the PEI matrix, imparting enhanced mechanical and thermal stability and thermal conductivity with increasing BN content. The thermal conductivity of the PEI composite containing 60 wt% PI-BN was 3.3 W m(-1) K(-1), while the thermal conductivity of the PEI/BN composite without modification was 2.6 W m(-1) K(-1). The PEI/PI-BN composites show higher impact strengths than the PEI/BN composites because of less BN particle agglomeration and good wettability between PEI and h-BN. The results indicate that the PI-coated BN incorporated into the PEI matrix effectively enhances the thermal conductivity and mechanical properties of the PEI composites.

A Convenient Route to Diverse Heterocycles through an Addition of β-Amino Carbonyl Compounds to 3-Halogeno-4-methoxybenzynes

[reaction: see text] 3-Halogeno-4-methoxybenzynes 5 generated from 5-(3-halogeno-4-methoxyphenyl)thianthrenium perchlorates 1 and LDA in THF at reflux reacted with various beta-amino carbonyl compounds and 2-aminophenyl benzenesulfonate etc. to give diverse heterocyclic compounds.

Electroretinographic studies in rhegmatogenous retinal detachment before and after reattachment surgery

Changes in the scotopic electroretinogram (ERG) and oscillatory potentials (Ops) were examined in patients who had rhegmatogenous retinal detachment within 3 days before and also at regular intervals after a successful reattachment operation. Electroretinograms were recorded in 38 patients (age 10 to 62 years) for the recent 5 year period. The amplitudes of the scotopic a-wave and b-wave in the detached eye were significantly decreased compared to the fellow normal eye preoperatively. Statistically significant differences were found between the preoperative and the postoperative recordings of the diseased eye. However, postoperative interocular differences in the amplitude of the a-wave between the reattached and normal fellow eye were not statistically significant. The amplitudes of the a-wave and b-wave were inversely related to the extent of the retinal detachment area, the duration of the detachment, and the extent of the buckles that were applied. Significant interocular differences in the b/a wave amplitude ratio and the Ops amplitude were observed. These results strongly suggest that the retinal function, even after successful reattachment surgery, might be compromised mainly by an inner retinal malfunction rather than from a photoreceptor dysfunction.