Browsing by Author "Rao, Arvind"
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Item CGAT: Cell Graph ATtention Network for Grading of Pancreatic Disease Histology Images(Frontiers Media S.A., 2021) Baranwal, Mayank; Krishnan, Santhoshi; Oneka, Morgan; Frankel, Timothy; Rao, ArvindEarly detection of Pancreatic Ductal Adenocarcinoma (PDAC), one of the most aggressive malignancies of the pancreas, is crucial to avoid metastatic spread to other body regions. Detection of pancreatic cancer is typically carried out by assessing the distribution and arrangement of tumor and immune cells in histology images. This is further complicated due to morphological similarities with chronic pancreatitis (CP), and the co-occurrence of precursor lesions in the same tissue. Most of the current automated methods for grading pancreatic cancers rely on extensive feature engineering involving accurate identification of cell features or utilising single number spatially informed indices for grading purposes. Moreover, sophisticated methods involving black-box approaches, such as neural networks, do not offer insights into the model’s ability to accurately identify the correct disease grade. In this paper, we develop a novel cell-graph based Cell-Graph Attention (CGAT) network for the precise classification of pancreatic cancer and its precursors from multiplexed immunofluorescence histology images into the six different types of pancreatic diseases. The issue of class imbalance is addressed through bootstrapping multiple CGAT-nets, while the self-attention mechanism facilitates visualization of cell-cell features that are likely responsible for the predictive capabilities of the model. It is also shown that the model significantly outperforms the decision tree classifiers built using spatially informed metric, such as the Morisita-Horn (MH) indices.Item Computed Tomography Radiomics Kinetics as Early Imaging Correlates of Osteoradionecrosis in Oropharyngeal Cancer Patients(Frontiers Media S.A., 2021) Barua, Souptik; Elhalawani, Hesham; Volpe, Stefania; Al Feghali, Karine A.; Yang, Pei; Ng, Sweet Ping; Elgohari, Baher; Granberry, Robin C.; Mackin, Dennis S.; Gunn, G. Brandon; Hutcheson, Katherine A.; Chambers, Mark S.; Court, Laurence E.; Mohamed, Abdallah S.R.; Fuller, Clifton D.; Lai, Stephen Y.; Rao, ArvindOsteoradionecrosis (ORN) is a major side-effect of radiation therapy in oropharyngeal cancer (OPC) patients. In this study, we demonstrate that early prediction of ORN is possible by analyzing the temporal evolution of mandibular subvolumes receiving radiation. For our analysis, we use computed tomography (CT) scans from 21 OPC patients treated with Intensity Modulated Radiation Therapy (IMRT) with subsequent radiographically-proven ≥ grade II ORN, at three different time points: pre-IMRT, 2-months, and 6-months post-IMRT. For each patient, radiomic features were extracted from a mandibular subvolume that developed ORN and a control subvolume that received the same dose but did not develop ORN. We used a Multivariate Functional Principal Component Analysis (MFPCA) approach to characterize the temporal trajectories of these features. The proposed MFPCA model performs the best at classifying ORN vs. Control subvolumes with an area under curve (AUC) = 0.74 [95% confidence interval (C.I.): 0.61–0.90], significantly outperforming existing approaches such as a pre-IMRT features model or a delta model based on changes at intermediate time points, i.e., at 2- and 6-month follow-up. This suggests that temporal trajectories of radiomics features derived from sequential pre- and post-RT CT scans can provide markers that are correlates of RT-induced mandibular injury, and consequently aid in earlier management of ORN.Item Identifying and Predicting Molecular Signatures in Glioblastoma Using Imaging-Derived Phenotypic Traits(2017-06-23) Yang, Dalu; Rao, Arvind; Veeraraghavan, AshokThis thesis addresses the problem of linking molecular status of glioblastoma patients with imaging-derived phenotypic traits. Glioblastoma (GBM) is the most common and aggressive type of malignant brain tumor, with a median survival of only 12-15 months. Due to GBM’s complex heterogeneity in gene expression, the responses to current treatment strategy varies considerably among different patients. There is an urgent need for a deeper understanding of tumor biology and alternative personalized therapeutic intervention. Magnetic Resonance Imaging (MRI) and histologic images are routinely used for GBM diagnosis. A natural question to ask is that if the phenotypic tumor traits from these images can be linked to tumor molecular signatures. In this thesis, we explore the imaging-genomic relationship in GBM via three approaches. The first approach aims to find texture features extracted from the MRI images that best discriminate GBM molecular subtypes. The second approach aims to find gene networks that determines the radiologically-defined tumor sub-compartment volumes. The third approach aims to quantify GBM histologic hallmarks and correlate them with biological pathway activities. Our study shows that linking imaging traits with tumor molecular status can lead to discoveries that have potential clinical relevance and provide biological insight.Item Leveraging structure in cancer imaging data using data-driven frameworks to predict clinical outcomes(2019-04-17) Barua, Souptik; Rao, Arvind; Veeraraghavan, AshokImmunotherapy and radiation therapy are two of the most prominent strategies used to treat cancer. While both these strategies have succeeded in treating the disease in many patients and cancer types, they are known to not work well for all patients, sometimes even leading to adverse side effects. There is thus a critical need to be able to predict how patients might respond to these therapies and accordingly design optimal treatment plans. In this thesis, I have built data-driven frameworks that leverage different types of structure in cancer imaging data to predict clinical outcomes of interest. I demonstrate my findings using two kinds of cancer image data: multipexed Immuno-Fluorescence (mIF) images from the field of pathology, and Computed Tomography (CT) from radiology. In mIF images, I quantify spatial structure in terms of proximities of cancer cells and various immune cells by developing ideas from spatial statistics to estimate the nearest neighbor distribution function of a spatial point process. I demonstrate that this quantity, called the G-function, can be used as a visual signature of the infiltration patterns of different cell types of interest. I compute summary metrics from the G-function which are prognostic of clinical outcome in multiple cancer types such as pancreatic, breast, and skin cancer. I design a functional analysis pipeline to more efficiently summarize the G-function and predict the risk of progression in pancreatic cysts. Finally, I propose approaches to capture the heterogeneity in the tumor microenvironment and demonstrate the importance of quantifying spatial structure separately in different tumor regions through a case study in lung cancer. In CT images acquired at different time points, I capture the temporal evolution of a specific class of image features called radiomic features using ideas from functional analysis. I then show that the temporal dynamics of radiomic features can be used to predict clinical outcomes such as the likelihood of complete response to radiation therapy and the risk of developing long-term radiation injuries such as osteoradionecrosis. Overall, I envisage these data-driven frameworks can be incorporated into a clinical setting not only as a tool for prognosis and treatment design but also in basic science research to understand the biological underpinnings of cancer development.Item Mapping the (un)known: Spatially-driven Frameworks for characterization of Disease Heterogeneity(2023-08-10) Krishnan, Santhoshi Navaneetha; Patel, Ankit; Rao, ArvindIn recent years, novel treatment approaches such as immunotherapy, where we boost an individual's immune system for better cancer targeting, are being increasingly trialed and adopted for various cancer pathologies. However, immune system function response is highly variable, especially in cancers such as Pancreatic Ductal Adenocarcinoma(PDAC), with many patients either not responding or suffering from adverse effects. Recent work has highlighted the importance of spatial quantification of tumor and immune cell subtypes to provide insights into treatment prognosis. My proposed work involves leveraging machine learning and spatial statistical methods to build frameworks that better characterize the (un)known tumor microenvironment, utilizing the inherent spatial structure extracted from imaging data. We validate these frameworks using commonly accessible histopathological imaging data such as hematoxylin and eosin(H&E)-stained whole slide images and multiplexed immunofluorescence(mIF) images. For this purpose, I explore three distinct methodological approaches to quantify tumor-immune interactions in a patient cohort consisting of 6 different pancreatic diseases, including PDAC. In the first approach, I utilize the concepts of geographically weighted regression (GWR) and a density function-based classification model to compute a spatially salient construction that can discriminate the six classes of lesions. This framework, GaWRDenMap, showed significant discriminant ability in multiple pairwise comparisons compared to abundance-based metrics, like the Morisita-Horn index. Our framework was able to highly discriminate between PDAC and non-cancerous chronic pancreatitis(CP), which is challenging for pathologists to discern based on the arrangement and structure of the cells under the microscope. Next, I repurpose an existing discriminative feature-based dictionary learning method to identify sub-regions of the tumor microenvironment representative of the disease. Initial results on mIF images from CP and PDAC patients from the same pancreatic cohort point to the excellent discriminant capabilities of the model while providing a visually interpretable dictionary output representative of the given cohort. Finally, I propose a novel cell-graph-based Cell-Graph ATtention (CGAT) network for precisely classifying PDAC and its precursor lesions only utilizing available spatial and phenotypic information as input features. The self-attention mechanism facilitates the identification of tissue regions and novel cell-cell interaction patterns characteristic of the disease. The proposed methods move away from single-number summaries by providing a more comprehensive representation of the state of the microenvironment. This thesis lays the groundwork for adopting these tools into not just exploratory biological research but also as diagnostic tools in the clinical setting.Item MHC Class II Expression Influences the Composition and Distribution of Immune Cells in the Metastatic Colorectal Cancer Microenvironment(MDPI, 2022) Griffith, Brian D.; Turcotte, Simon; Lazarus, Jenny; Lima, Fatima; Bell, Samantha; Delrosario, Lawrence; McGue, Jake; Krishnan, Santhoshi; Oneka, Morgan D.; Nathan, Hari; Smith, J. Joshua; D’Angelica, Michael I.; Shia, Jinru; Di Magliano, Marina Pasca; Rao, Arvind; Frankel, Timothy L.Despite advances in therapy over the past decades, metastatic colorectal cancer (mCRC) remains a highly morbid disease. While the impact of MHC-I on immune infiltration in mCRC has been well studied, data on the consequences of MHC-II loss are lacking. Multiplex fluorescent immunohistochemistry (mfIHC) was performed on 149 patients undergoing curative intent resection for mCRC and stratified into high and low human leukocyte antigen isotype DR (HLA-DR) expressing tumors. Intratumoral HLA-DR expression was found in stromal bands, and its expression level was associated with different infiltrating immune cell makeup and distribution. Low HLA-DR expression was associated with increased intercellular distances and decreased population mixing of T helper cells and antigen-presenting cells (APC), suggestive of decreased interactions. This was associated with less co-localization of tumor cells and cytotoxic T lymphocytes (CTLs), which tended to be in a less activated state as determined by Ki67 and granzyme B expression. These findings suggest that low HLA-DR in the tumor microenvironment of mCRC may reflect a state of poor helper T-cell interactions with APCs and CTL-mediated anti-tumor activity. Efforts to restore/enhance MHC-II presentation may be a useful strategy to enhance checkpoint inhibition therapy in the future.Item SPARTIN: a Bayesian method for the quantification and characterization of cell type interactions in spatial pathology data(Frontiers Media S.A., 2023) Osher, Nathaniel; Kang, Jian; Krishnan, Santhoshi; Rao, Arvind; Baladandayuthapani, VeerabhadranIntroduction: The acquisition of high-resolution digital pathology imaging data has sparked the development of methods to extract context-specific features from such complex data. In the context of cancer, this has led to increased exploration of the tumor microenvironment with respect to the presence and spatial composition of immune cells. Spatial statistical modeling of the immune microenvironment may yield insights into the role played by the immune system in the natural development of cancer as well as downstream therapeutic interventions.Methods: In this paper, we present SPatial Analysis of paRtitioned Tumor-Immune imagiNg (SPARTIN), a Bayesian method for the spatial quantification of immune cell infiltration from pathology images. SPARTIN uses Bayesian point processes to characterize a novel measure of local tumor-immune cell interaction, Cell Type Interaction Probability (CTIP). CTIP allows rigorous incorporation of uncertainty and is highly interpretable, both within and across biopsies, and can be used to assess associations with genomic and clinical features.Results: Through simulations, we show SPARTIN can accurately distinguish various patterns of cellular interactions as compared to existing methods. Using SPARTIN, we characterized the local spatial immune cell infiltration within and across 335 melanoma biopsies and evaluated their association with genomic, phenotypic, and clinical outcomes. We found that CTIP was significantly (negatively) associated with deconvolved immune cell prevalence scores including CD8+ T-Cells and Natural Killer cells. Furthermore, average CTIP scores differed significantly across previously established transcriptomic classes and significantly associated with survival outcomes.Discussion: SPARTIN provides a general framework for investigating spatial cellular interactions in high-resolution digital histopathology imaging data and its associations with patient level characteristics. The results of our analysis have potential implications relevant to both treatment and prognosis in the context of Skin Cutaneous Melanoma. The R-package for SPARTIN is available at https://github.com/bayesrx/SPARTIN along with a visualization tool for the images and results at: https://nateosher.github.io/SPARTIN.Item Towards the characterization of the tumor microenvironment through dictionary learning-based interpretable classification of multiplexed immunofluorescence images(IOP Publishing, 2022) Krishnan, Santhoshi N.; Barua, Souptik; Frankel, Timothy L.; Rao, ArvindObjective. Histology image analysis is a crucial diagnostic step in staging and treatment planning, especially for cancerous lesions. With the increasing adoption of computational methods for image analysis, significant strides are being made to improve the performance metrics of image segmentation and classification frameworks. However, many developed frameworks effectively function as black boxes, granting minimal context to the decision-making process. Thus, there is a need to develop methods that offer reasonable discriminatory power and a biologically-informed intuition to the decision-making process. Approach. In this study, we utilized and modified a discriminative feature-based dictionary learning (DFDL) paradigm to generate a classification framework that allows for discrimination between two distinct clinical histologies. This framework allows us (i) to discriminate between 2 clinically distinct diseases or histologies and (ii) provides interpretable group-specific representative dictionary image patches, or ‘atoms’, generated during classifier training. This implementation is performed on multiplexed immunofluorescence images from two separate patient cohorts- a pancreatic cohort consisting of cancerous and non-cancerous tissues and a metastatic non-small cell lung cancer (mNSCLC) cohort of responders and non-responders to an immunotherapeutic treatment regimen. The analysis was done at both the image-level and subject-level. Five cell types were selected, namely, epithelial cells, cytotoxic lymphocytes, antigen presenting cells, HelperT cells, and T-regulatory cells, as our phenotypes of interest. Results. We showed that DFDL had significant discriminant capabilities for both the pancreatic pathologies cohort (subject-level AUC-0.8878) and the mNSCLC immunotherapy response cohort (subject-level AUC-0.7221). The secondary analysis also showed that more than 50% of the obtained dictionary atoms from the classifier contained biologically relevant information. Significance. Our method shows that the generated dictionary features can help distinguish patients presenting two different histologies with strong sensitivity and specificity metrics. These features allow for an additional layer of model interpretability, a highly desirable element in clinical applications for identifying novel biological phenomena.