Multiscale, multimodal analysis of tumor heterogeneity in IDH1 mutant vs wild-type diffuse gliomas.

Glioma is recognized to be a highly heterogeneous CNS malignancy, whose diverse cellular composition and cellular interactions have not been well characterized. To gain new clinical- and biological-insights into the genetically-bifurcated IDH1 mutant (mt) vs wildtype (wt) forms of glioma, we integrated data from protein, genomic and MR imaging from 20 treatment-naïve glioma cases and 16 recurrent GBM cases. Multiplexed immunofluorescence (MxIF) was used to generate single cell data for 43 protein markers representing all cancer hallmarks, Genomic sequencing (exome and RNA (normal and tumor) and magnetic resonance imaging (MRI) quantitative features (protocols were T1-post, FLAIR and ADC) from whole tumor, peritumoral edema and enhancing core vs equivalent normal region were also collected from patients. Based on MxIF analysis, 85,767 cells (glioma cases) and 56,304 cells (GBM cases) were used to generate cell-level data for 24 biomarkers. K-means clustering was used to generate 7 distinct groups of cells with divergent biomarker profiles and deconvolution was used to assign RNA data into three classes. Spatial and molecular heterogeneity metrics were generated for the cell data. All features were compared between IDH mt and IDHwt patients and were finally combined to provide a holistic/integrated comparison. Protein expression by hallmark was generally lower in the IDHmt vs wt patients. Molecular and spatial heterogeneity scores for angiogenesis and cell invasion also differed between IDHmt and wt gliomas irrespective of prior treatment and tumor grade; these differences also persisted in the MR imaging features of peritumoral edema and contrast enhancement volumes. A coherent picture of enhanced angiogenesis in IDHwt tumors was derived from multiple platforms (genomic, proteomic and imaging) and scales from individual proteins to cell clusters and heterogeneity, as well as bulk tumor RNA and imaging features. Longer overall survival for IDH1mt glioma patients may reflect mutation-driven alterations in cellular, molecular, and spatial heterogeneity which manifest in discernable radiological manifestations

Longitudinal Brain Tumor Tracking, Tumor Grading, and Patient Survival Prediction Using MRI

This work aims to develop novel methods for brain tumor classification, longitudinal brain tumor tracking, and patient survival prediction. Consequently, this dissertation proposes three tasks. First, we develop a framework for brain tumor segmentation prediction in longitudinal multimodal magnetic resonance imaging (mMRI) scans, comprising two methods: feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density features, in order to obtain tumor segmentation predictions in follow-up scans from a baseline pre-operative timepoint. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). With the advantages of feature fusion and label fusion, we achieve state-of-the-art brain tumor segmentation prediction. Second, we propose a deep neural network (DNN) learning-based method for brain tumor type and subtype grading using phenotypic and genotypic data, following the World Health Organization (WHO) criteria. In addition, the classification method integrates a cellularity feature which is derived from the morphology of a pathology image to improve classification performance. The proposed method achieves state-of-the-art performance for tumor grading following the new CNS tumor grading criteria. Finally, we investigate brain tumor volume segmentation, tumor subtype classification, and overall patient survival prediction, and then we propose a new context- aware deep learning method, known as the Context Aware Convolutional Neural Network (CANet). Using the proposed method, we participated in the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) for brain tumor volume segmentation and overall survival prediction tasks. In addition, we also participated in the Radiology-Pathology Challenge 2019 (CPM-RadPath 2019) for Brain Tumor Subtype Classification, organized by the Medical Image Computing & Computer Assisted Intervention (MICCAI) Society. The online evaluation results show that the proposed methods offer competitive performance from their use of state-of-the-art methods in tumor volume segmentation, promising performance on overall survival prediction, and state-of-the-art performance on tumor subtype classification. Moreover, our result was ranked second place in the testing phase of the CPM-RadPath 2019

Imaging Based Prediction of Pathology in Adult Diffuse Glioma with Applications to Therapy and Prognosis

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known. In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the underlying biological heterogeneity via imaging. This provides an approach to answer questions about how tissue heterogeneity can affect prognosis. We estimated the local pathology within tumors using imaging data and stereotactically precise biopsy samples from an ongoing clinical imaging trial. From this data, we trained a random forest model to reliably predict tumor grade, proliferation, cellularity, and vascularity, representing tumor aggressiveness. We then made voxel-wise predictions to map the tumor heterogeneity and identify high-grade malignancy disease. Next, we used the previously trained models on a cohort of 1,850 glioma patients who previously underwent surgical resection. High contrast enhancement, proliferation, vascularity, and cellularity were associated with worse prognosis even after controlling for clinical factors. Patients that had substantial reduction in cellularity between preoperative and postoperative imaging (i.e. due to resection) also showed improved survival. We developed a clinically implementable model for predicting pathology and prognosis after surgery based on imaging. Results from imaging pathology correlations enhance our understanding of disease extent within glioma patients and the relationship between residual estimated pathology and outcome helps refine our knowledge of the interaction of tumor heterogeneity and prognosis

Ethnicity-dependent and -independent heterogeneity in healthy normal breast hierarchy impacts tumor characterization

Recent reports of widespread genetic variation affecting regulation of gene expression raise the possibility of significant inter-individual differences in stem-progenitor-mature cell hierarchy in adult organs. This has not been explored because of paucity of methods to quantitatively assess subpopulation of normal epithelial cells on individual basis. We report the remarkable inter-individual differences in differentiation capabilities as documented by phenotypic heterogeneity in stem-progenitor-mature cell hierarchy of the normal breast. Ethnicity and genetic predisposition are partly responsible for this heterogeneity, evidenced by the finding that CD44+/CD24- and PROCR+/EpCAM- multi-potent stem cells were elevated significantly in African American women compared with Caucasians. ALDEFLUOR+ luminal stem/progenitor cells were lower in BRCA1-mutation carriers compared with cells from healthy donors (p = 0.0014). Moreover, tumor and adjoining-normal breast cells of the same patients showed distinct CD49f+/EpCAM+ progenitor, CD271+/EpCAM- basal, and ALDEFLUOR+ cell profiles. These inter-individual differences in the rate of differentiation in the normal breast may contribute to a substantial proportion of transcriptome, epigenome, and signaling pathway alterations and consequently has the potential to spuriously magnify the extent of documented tumor-specific gene expression. Therefore, comparative analysis of phenotypically defined subpopulations of normal and tumor cells on an individual basis may be required to identify cancer-specific aberrations

Radio-Pathomic Approaches in Pediatric Neurooncology: Opportunities and Challenges

With medical software platforms moving to cloud environments with scalable storage and computing, the translation of predictive artificial intelligence (AI) models to aid in clinical decision-making and facilitate personalized medicine for cancer patients is becoming a reality. Medical imaging, namely radiologic and histologic images, has immense analytical potential in neuro-oncology, and models utilizing integrated radiomic and pathomic data may yield a synergistic effect and provide a new modality for precision medicine. At the same time, the ability to harness multi-modal data is met with challenges in aggregating data across medical departments and institutions, as well as significant complexity in modeling the phenotypic and genotypic heterogeneity of pediatric brain tumors. In this paper, we review recent pathomic and integrated pathomic, radiomic, and genomic studies with clinical applications. We discuss current challenges limiting translational research on pediatric brain tumors and outline technical and analytical solutions. Overall, we propose that to empower the potential residing in radio-pathomics, systemic changes in cross-discipline data management and end-to-end software platforms to handle multi-modal data sets are needed, in addition to embracing modern AI-powered approaches. These changes can improve the performance of predictive models, and ultimately the ability to advance brain cancer treatments and patient outcomes through the development of such models

MRI Characterization of Radiation Necrosis in an Animal Model: Time to Onset, Progression, and Therapeutic Response

Radiation necrosis is a severe, but late occurring type of injury to normal tissue, within and surrounding a radiation treatment field, which can lead to significant complications for neurooncology patients. Radiation necrosis is difficult to distinguish from recurrent tumor by either neurologic examination or clinical imaging protocols. Concerns for the development of radiation necrosis often limit therapeutic radiation doses. Current treatment options for radiation necrosis are limited. The development of solutions to these clinical challenges has been hampered by an appropriate animal model of radiation necrosis. With a novel mouse model of radiation necrosis developed in our lab employing a Gamma Knife, which enables high-dose, fractionated, hemispherical irradiation in the mouse brain, the objectives were to i) optimise radiation dosing schemes: total dose, fractionation) for this Gamma-Knife mouse-model of radiation necrosis; ii) determine the efficacy of bevacizumab: Avastin) and its murine analog B20-4.1.1, both vascular endothelial growth factor: VEGF) inhibitors, as mitigators of radiation necrosis in mice; iii) validate the neuroprotective effect of SB 415286, an inhibitor of glycogen synthase kinase 3β;: GSK-3β), in mouse brain following high-dose radiation treatment; and iv) identify and validate the quantitative blood oxygen level dependent: qBOLD) method as an imaging marker of radiation necrosis. For these purposes, a series of experiments were performed, including monitoring the onset and progression of radiation necrosis in mice receiving different dose schedules, comparing the development of radiation necrosis in irradiated mice with or without treatments, and mapping the irradiated and non-irradiated mouse brains using qBOLD method. It was found that i) radiation dose schedules affect the onset and progression of radiation necrosis; ii) anti-VEGF antibodies slow the progression of radiation necrosis in irradiated brain tissue; iii) SB 415286 protects against and mitigates radiation necrosis in irradiated brain tissue; and iv) a high SNR: 400 at least) is required to decouple oxygen extraction fraction: OEF) and deoxyhemoglombin cerebral blood volume: dCBV) in mouse brain using qBOLD method. In qBOLD, the voxel spread function: VSF) reduces the effect of macroscopic magnetic field inhomogeneities. However, with current shimming methods, imaging parameters, and post-processing algorithms, the resulting OEF and dCBV maps in the mouse brain are not reliable. These results demonstrated that the development of radiation necrosis in this Gamma Knife mouse model can be characterized by both anatomic MR imaging and histology. Both anti-VEGF therapy and GSK-3β inhibition could be potential therapeutic managements for radiation necrosis, but further studies are needed to optimize dosing schemes and treatment periods and elucidate mechanisms of action. Characterizing radiation necrosis in mouse brain using qBOLD remains a challenge due to the imperfect correction for macro magnetic field inhomogeneities

Computational Pathology: A Survey Review and The Way Forward

Computational Pathology CPath is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath (https://github.com/AtlasAnalyticsLab/CPath_Survey).Comment: Accepted in Elsevier Journal of Pathology Informatics (JPI) 202

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph