This book is designed to introduce biologists, clinicians and computational researchers to fundamental data analysis principles, techniques and tools for supporting the discovery of biomarkers and the implementation of diagnostic/prognostic systems. The focus of the book is on how fundamental statistical and data mining approaches can support biomarker discovery and evaluation, emphasising applications based on different types of "omic" data. The book also discusses design factors, requirements and techniques for disease screening, diagnostic and prognostic applications. Readers are provided with the knowledge needed to assess the requirements, computational approaches and outputs in disease biomarker research. Commentaries from guest experts are also included, containing detailed discussions of methodologies and applications based on specific types of "omic" data, as well as their integration. Covers the main range of data sources currently used for biomarker discovery Covers the main range of data sources currently used for biomarker discovery Puts emphasis on concepts, design principles and methodologies that can be extended or tailored to more specific applications Offers principles and methods for assessing the bioinformatic/biostatistic limitations, strengths and challenges in biomarker discovery studies Discusses systems biology approaches and applications Includes expert chapter commentaries to further discuss relevance of techniques, summarize biological/clinical implications and provide alternative interpretations

Much of the emphasis in discussions about personalized medicine has been focused on the molecular characterization of tissue samples using microarray technology. However, as genetic differ between and within tumors and are quite heterogeneous, these techniques are limited. Imaging is noninvasive and is often used in routine clinical practice for disease diagnosis, treatment, and prognosis. Imaging is useful to guide disease therapy by providing a more comprehensive view of the entire lesion and it can be used on an ongoing basis to monitor lesion growth and progression or its response to treatment. The imaging includes but not limited to ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET). Radiomics refers to the conversion of images to high dimensional data and the subsequent mining for characterization of biology and ultimately to improve disease management for patients. Radiogenomics investigates relationships between imaging features and genomics, which represents the correlation between the anatomical-histological level to the genomic level. With advanced artificial intelligence methods, especially deep learning, data processing, feature extraction and data integration have been greatly improved. The topic is about artificial intelligence methods in biomedical images and genomics data for disease diagnosis, treatment, and prognosis, as listed here: • Biomarker identification from biomedical images to predict disease diagnosis, treatment, and prognosis • Radiogenomics/image-omics in identifying imaging biomarkers associated with molecular characteristics of the disease. • Machine learning/deep learning methods in biomedical imaging or genomics for disease detection and precision medicine. • Prediction of histological characteristics of disease based on biomedical imaging. • Integration of radiomics and genomics features for disease diagnosis, prognosis, and prediction medicine • Multimodality images or multi-omics data integration methods

This is the first book to cover all aspects of the development of imaging biomarkers and their integration into clinical practice, from the conceptual basis through to the technical aspects that need to be considered in order to ensure that medical imaging can serve as a powerful quantification instrument capable of providing valuable information on organ and tissue properties. The process of imaging biomarker development is considered step by step, covering proof of concept, proof of mechanism, image acquisition, image preparation, imaging biomarker analysis and measurement, detection of measurement biases (proof of principle), proof of efficacy and effectiveness, and reporting of results. Sources of uncertainty in the accuracy and precision of measurements and pearls and pitfalls in gold standards and biological correlation are discussed. In addition, practical use cases are included on imaging biomarker implementation in brain, oncologic, cardiovascular, musculoskeletal, and abdominal diseases. The authors are a multidisciplinary team of expert radiologists and engineers, and the book will be of value to all with an interest in the quantitative imaging of biomarkers in personalized medicine.

The well-known Alzheimer's Disease Neuroimaging Initiative (ADNI) Center provides the most advanced, comprehensive, multiparametric and up-to-date biomarkers for mild cognitive impairment (MCI) and early Alzheimer's disease (AD) projects, including neuroimaging, clinical assessments, biospecimens and genetic data. Recent developments in imaging techniques, including new molecular tracers for imaging disease burden and systematic multi-modal integration, have emerged to overcome the limitations of each single modality and individual-dependent variability. The MRI-based high-resolution structural and morphological changes in the brain, such as atrophy, and the abnormal activity/connectivity patterns of the hippocampus subfields and default mode network (DMN) modulation, together with the amyloid and tau neuropathological quantification using PET molecular tracers, could be used to predict brain changes and cognitive performance declines in early AD, including transitional MCI. Finally, a generalized and integrative model with multiple biomarkers could be built to target disease progression and symptom prediction as well as to optimize patient management. Multiomics investigates metabolomic, lipidomic, genomic, transcriptomic and proteomic perspectives by presenting an accurate biochemical profile of the organism in health and disease. The Alzheimer's Disease Metabolomics Consortium (ADMC) in partnership with ADNI is creating a comprehensive biochemical database for patients in the ADNI1 cohort, consisting of eight metabolomics datasets. The vast majorities of biospecimen data provide rich biological information to the human brain at normal and dementia status. One of the purposes is to reveal the connections between disease and multiomics such as obesity, hypertension, cholesterol imbalance and inflammation risks that might lead to neurodegenerative disease. Multiomic biomarker developments in the dementia field have provided earlier clues to novel treatments that help correct metabolic dysfunction and delay disease progression. Furthermore, the assembling of multiomics-based biomarkers including metabolites and lipids, cholesterol biosynthesis, purine metabolism, lipoprotein, bile acids, and genetics as well as their relation to the pathological amyloid and tau network could improve disease diagnosis sensitivity and reveal more diverse and complementary molecular pathways to allow for the advancement of early AD diagnosis and therapeutic prevention. In this book, we report on the significant differences of multiple biomarkers from the ADNI database including neuroimaging, clinical assessments and multiomic biospecimen/genetic data in MCI and early probable AD (pAD), and elucidate the interconnections among different metrics at various domains. Classification results with high accuracies (0.95-1) for each early dementia subtype including early MCI (EMCI), late MCI (LMCI) and pAD, and better prediction of clinical symptoms is achieved with these comprehensive biomarkers. Further longitudinal changes of imaging and neuropsychological biomarkers, and inter-correlations with baseline parameters are examined for a better illustration of disease progression association. Additionally, an analysis of the post-traumatic stress disorder biomarkers is performed with high classification accuracy. With illustrative and rigorous data analyses and confirmative results, this book provides readers with a full spectrum of biomarker research for early dementia diagnosis and treatment, and helps convey the technical development and data evaluation perspectives in advanced medical imaging and various disease application fields.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

Built on a decade of experience with novel molecular diagnostics, this practice-oriented guide shows how to cope with validation issues during all stages of biomarker development, from the first clinical studies to the eventual commercialization of a new diagnostic test.