Abstract
Precision medicine promises better healthcare delivery by improving clinical practice. Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the specific needs of each patient. The wealth of today's healthcare data, often characterized as big data, provides invaluable resources toward new knowledge discovery that has the potential to advance precision medicine. The latter requires interdisciplinary efforts that will capitalize the information, know-how, and medical data of newly formed groups fusing different backgrounds and expertise. The objective of this paper is to provide insights with respect to the state-of-the-art research in precision medicine. More specifically, our goal is to highlight the fundamental challenges in emerging fields of radiomics and radiogenomics by reviewing the case studies of Cancer and Alzheimer's disease, describe the computational challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.
Highlights
P RECISION medicine aspires to leverage new knowledge emanating from heterogeneous imaging, genomic, environmental, and clinical data analysis, facilitating increasedManuscript received February 15, 2018; revised June 13, 2018 and September 17, 2018; accepted October 15, 2018
Such a radical shift in clinical care practice requires fundamental advances across a number of inter- and multi- disciplinary and cross- sectoral fields. Such advances range from the development of new big data analytics tools and research in precision medicine, to standardization of acquisition, storage, and open sharing of de-identified patients’ electronic health records (EHR) and research data, as well as significant patient involvement and supportive policies
Large-scale datasets for radiogenomics research are currently available through open research data initiatives, such as the Cancer Imaging Project (CIP) [27], [28] and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [29]
Summary
P RECISION medicine aspires to leverage new knowledge emanating from heterogeneous imaging, genomic, environmental, and clinical data analysis, facilitating increased. Such a radical shift in clinical care practice requires fundamental advances across a number of inter- and multi- disciplinary and cross- sectoral fields. Large-scale datasets for radiogenomics research are currently available through open research data initiatives, such as the Cancer Imaging Project (CIP) [27], [28] and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) [29]. These datasets provide multi-modal imaging, genomic, and clinical data.
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