Browsing by Author "Radiology & Imaging Sciences, IU School of Medicine"

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    Identifying progressive imaging genetic patterns via multi-task sparse canonical correlation analysis: a longitudinal study of the ADNI cohort
    (Oxford University Press, 2019-07) Du, Lei; Liu, Kefei; Zhu, Lei; Yao, Xiaohui; Risacher, Shannon L.; Guo, Lei; Saykin, Andrew J.; Shen, Li; Radiology & Imaging Sciences, IU School of Medicine
    Motivation Identifying the genetic basis of the brain structure, function and disorder by using the imaging quantitative traits (QTs) as endophenotypes is an important task in brain science. Brain QTs often change over time while the disorder progresses and thus understanding how the genetic factors play roles on the progressive brain QT changes is of great importance and meaning. Most existing imaging genetics methods only analyze the baseline neuroimaging data, and thus those longitudinal imaging data across multiple time points containing important disease progression information are omitted. Results We propose a novel temporal imaging genetic model which performs the multi-task sparse canonical correlation analysis (T-MTSCCA). Our model uses longitudinal neuroimaging data to uncover that how single nucleotide polymorphisms (SNPs) play roles on affecting brain QTs over the time. Incorporating the relationship of the longitudinal imaging data and that within SNPs, T-MTSCCA could identify a trajectory of progressive imaging genetic patterns over the time. We propose an efficient algorithm to solve the problem and show its convergence. We evaluate T-MTSCCA on 408 subjects from the Alzheimer’s Disease Neuroimaging Initiative database with longitudinal magnetic resonance imaging data and genetic data available. The experimental results show that T-MTSCCA performs either better than or equally to the state-of-the-art methods. In particular, T-MTSCCA could identify higher canonical correlation coefficients and capture clearer canonical weight patterns. This suggests that T-MTSCCA identifies time-consistent and time-dependent SNPs and imaging QTs, which further help understand the genetic basis of the brain QT changes over the time during the disease progression. Availability and implementation The software and simulation data are publicly available at https://github.com/dulei323/TMTSCCA. Supplementary information Supplementary data are available at Bioinformatics online.
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    International Cognition and Cancer Task Force Recommendations for Neuroimaging Methods in the Study of Cognitive Impairment in Non-CNS Cancer Patients
    (Oxford University Press, 2018-03) Deprez, Sabine; Kesler, Shelli R.; Saykin, Andrew J.; Silverman, Daniel H. S.; de Ruiter, Michiel B.; McDonald, Brenna C.; Radiology & Imaging Sciences, IU School of Medicine
    Cancer- and treatment-related cognitive changes have been a focus of increasing research since the early 1980s, with meta-analyses demonstrating poorer performance in cancer patients in cognitive domains including executive functions, processing speed, and memory. To facilitate collaborative efforts, in 2011 the International Cognition and Cancer Task Force (ICCTF) published consensus recommendations for core neuropsychological tests for studies of cancer populations. Over the past decade, studies have used neuroimaging techniques, including structural and functional magnetic resonance imaging (fMRI) and positron emission tomography, to examine the underlying brain basis for cancer- and treatment-related cognitive declines. As yet, however, there have been no consensus recommendations to guide researchers new to this field or to promote the ability to combine data sets. We first discuss important methodological issues with regard to neuroimaging study design, scanner considerations, and sequence selection, focusing on concerns relevant to cancer populations. We propose a minimum recommended set of sequences, including a high-resolution T1-weighted volume and a resting state fMRI scan. Additional advanced imaging sequences are discussed for consideration when feasible, including task-based fMRI and diffusion tensor imaging. Important image data processing and analytic considerations are also reviewed. These recommendations are offered to facilitate increased use of neuroimaging in studies of cancer- and treatment-related cognitive dysfunction. They are not intended to discourage investigator-initiated efforts to develop cutting-edge techniques, which will be helpful in advancing the state of the knowledge. Use of common imaging protocols will facilitate multicenter and data-pooling initiatives, which are needed to address critical mechanistic research questions.
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    MildInt: Deep Learning-Based Multimodal Longitudinal Data Integration Framework
    (Frontiers, 2019-06-28) Lee, Garam; Kang, Byungkon; Nho, Kwangsik; Sohn, Kyung-Ah; Kim, Dokyoon; Radiology & Imaging Sciences, IU School of Medicine
    As large amounts of heterogeneous biomedical data become available, numerous methods for integrating such datasets have been developed to extract complementary knowledge from multiple domains of sources. Recently, a deep learning approach has shown promising results in a variety of research areas. However, applying the deep learning approach requires expertise for constructing a deep architecture that can take multimodal longitudinal data. Thus, in this paper, a deep learning-based python package for data integration is developed. The python package deep learning-based multimodal longitudinal data integration framework (MildInt) provides the preconstructed deep learning architecture for a classification task. MildInt contains two learning phases: learning feature representation from each modality of data and training a classifier for the final decision. Adopting deep architecture in the first phase leads to learning more task-relevant feature representation than a linear model. In the second phase, linear regression classifier is used for detecting and investigating biomarkers from multimodal data. Thus, by combining the linear model and the deep learning model, higher accuracy and better interpretability can be achieved. We validated the performance of our package using simulation data and real data. For the real data, as a pilot study, we used clinical and multimodal neuroimaging datasets in Alzheimer's disease to predict the disease progression. MildInt is capable of integrating multiple forms of numerical data including time series and non-time series data for extracting complementary features from the multimodal dataset.
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    Multidisciplinary Management of Patients with Unresectable Hepatocellular Carcinoma: A Critical Appraisal of Current Evidence
    (MDPI, 2019-06-22) Gholam, Pierre M.; Iyer, Renuka; Johnson, Matthew S.; Radiology & Imaging Sciences, IU School of Medicine
    Hepatocellular carcinoma (HCC) is a leading cause of new cancer diagnoses in the United States, with an incidence that is expected to rise. The etiology of HCC is varied and can lead to differences between patients in terms of presentation and natural history. Subsequently, physicians treating these patients need to consider a variety of disease and patient characteristics when they select from the many different treatment options that are available for these patients. At the same time, the treatment landscape for patients with HCC, particularly those with unresectable HCC, has been rapidly evolving as new, evidence-based options become available. The treatment plan for patients with HCC can include surgery, transplant, ablation, transarterial chemoembolization, transarterial radioembolization, radiation therapy, and/or systemic therapies. Implementing these different modalities, where the optimal sequence and/or combination has not been defined, requires coordination between physicians with different specialties, including interventional radiologists, hepatologists, and surgical and medical oncologists. As such, the implementation of a multidisciplinary team is necessary to develop a comprehensive care plan for patients, especially those with unresectable HCC.