The aim of this book is to present statistical problems and methods in a friendly way to radiologists, emphasizing statistical issues and methods most frequently used in radiological studies (e.g., nonparametric tests, analysis of intra- and interobserver reproducibility, comparison of sensitivity and specificity among different imaging modality, difference between clinical and screening application of diagnostic tests, ect.). The tests will be presented starting from a radiological "problem" and all examples of statistical methods applications will be "radiological".
Research Methods in Radiology provides concise, practical insights on how to design clinical and experimental studies in diagnostic imaging. This unique resource encompasses contributions from leaders in academic radiology as well as top epidemiologists, biostatisticians, and librarians with vast multidisciplinary and radiology research experience. The material reflects years of expertise teaching core biostatistics in radiology principles to residents, fellows, radiologists, and epidemiologists. Given the vast amount of published information on research methodology and statistics in radiology, the authors' goal was to write a high-yield review and study tool rather than a comprehensive book. Key topics are succinctly addressed in each chapter, including measurements in radiology; decision analysis in radiology; and systemic reviews, evidence-based imaging, and knowledge translation. Online exercises related to each topic enable residents to prepare for radiology board examinations and research radiologists to apply knowledge to clinical studies. Key Highlights Introductory chapters on analysis of diagnostic tests, linear and logistic regression, meta-analysis, statistical inference, and economic evaluation provide easy-to-follow tutorials Each chapter includes learning objectives, basic concepts, supplementary tables, and ancillary online material Case studies with images, graphs, and tables highlight primary "take home" points Sample size calculations are illustrated for a wide range of research questions Code is included for use in R, free open-source software for statistical analysis This book is an indispensable review of research methodology for radiology students and residents. Practicing clinicians will also benefit from this precisely focused reference tool on clinical and experimental research.
Statistical evaluation of diagnostic performance in general and Receiver Operating Characteristic (ROC) analysis in particular are important for assessing the performance of medical tests and statistical classifiers, as well as for evaluating predictive models or algorithms. This book presents innovative approaches in ROC analysis, which are releva
There are numerous advantages to using Bayesian methods in diagnostic medicine, which is why they are employed more and more today in clinical studies. Exploring Bayesian statistics at an introductory level, Bayesian Biostatistics and Diagnostic Medicine illustrates how to apply these methods to solve important problems in medicine and biology.
Advancing the development, validation, and use of patient-reported outcome (PRO) measures, Patient-Reported Outcomes: Measurement, Implementation and Interpretation helps readers develop and enrich their understanding of PRO methodology, particularly from a quantitative perspective. Designed for biopharmaceutical researchers and others in the health sciences community, it provides an up-to-date volume on conceptual and analytical issues of PRO measures. The book discusses key concepts relating to the measurement, implementation, and interpretation of PRO measures. It covers both introductory and advanced psychometric and biostatistical methods for constructing and analyzing PRO measures. The authors include many relevant real-life applications based on their extensive first-hand experiences in the pharmaceutical industry. They implement a wealth of simulated datasets to illustrate concepts and heighten understanding based on practical scenarios. For readers interested in conducting statistical analyses of PRO measures and delving more deeply into the analytic details, most chapters contain SAS code and output that illustrate the methodology. Along with providing numerous references, the book highlights current regulatory guidelines.
Radiomics and Radiogenomics: Technical Basis and Clinical Applications provides a first summary of the overlapping fields of radiomics and radiogenomics, showcasing how they are being used to evaluate disease characteristics and correlate with treatment response and patient prognosis. It explains the fundamental principles, technical bases, and clinical applications with a focus on oncology. The book’s expert authors present computational approaches for extracting imaging features that help to detect and characterize disease tissues for improving diagnosis, prognosis, and evaluation of therapy response. This book is intended for audiences including imaging scientists, medical physicists, as well as medical professionals and specialists such as diagnostic radiologists, radiation oncologists, and medical oncologists. Features Provides a first complete overview of the technical underpinnings and clinical applications of radiomics and radiogenomics Shows how they are improving diagnostic and prognostic decisions with greater efficacy Discusses the image informatics, quantitative imaging, feature extraction, predictive modeling, software tools, and other key areas Covers applications in oncology and beyond, covering all major disease sites in separate chapters Includes an introduction to basic principles and discussion of emerging research directions with a roadmap to clinical translation
This book is a comprehensive guide to contrast-enhanced mammography (CEM), a novel advanced mammography technique using dual-energy mammography in combination with intravenous contrast administration in order to increase the diagnostic performance of digital mammography. Readers will find helpful information on the principles of CEM and indications for the technique. Detailed attention is devoted to image interpretation, with presentation of case examples and highlighting of pitfalls and artifacts. Other topics to be addressed include the establishment of a CEM program, the comparative merits of CEM and MRI, and the roles of CEM in screening populations and monitoring of response to neoadjuvant chemotherapy. CEM became commercially available in 2011 and is increasingly being used in clinical practice owing to its superiority over full-field digital mammography. This book will be an ideal source of knowledge and guidance for all who wish to start using the technique or to learn more about it.
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
Radiology has been transformed by new imaging advances and a greater demand for imaging, along with a much lower tolerance for error as part of the Quality & Safety revolution in healthcare. With a greater emphasis on patient safety and quality in imaging practice, imaging specialists are increasingly charged with ensuring patient safety and demonstrating that everything done for patients in their care meets the highest quality and safety standards. This book offers practical guidance on understanding, creating, and implementing quality management programs in Radiology. Chapters are comprehensive, detailed, and organized into three sections: Core Concepts, Management Concepts, and Educational & Special Concepts. Discussions are applicable to all practice settings: community hospitals, private practice, academic radiology, and government/military practice, as well as to those preparing for the quality and safety questions on the American Board of Radiology's "Maintenance of Certification" or initial Board Certification Examinations. Bringing together the various elements that comprise the quality and safety agenda for Radiology, this book serves as a thorough roadmap and resource for radiologists, technicians, and radiology managers and administrators.
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process.The author employs a Bayesian approach to provide statistical inferences
