"Frameworks for Radiology Reporting outlines methodical systems to aid image analysis for commonly encountered radiological examinations. These systems will help facilitate a reproducible process of image interpretation and avoid the common pitfalls of reporting. The book will not only be of value to trainee radiologists but also to physicians and radiographers with an interest in the process of image interpretation." --Book Jacket.
A practical guide for radiologists on providing high yield disease-specific reports Multiple studies show that referring physicians have a clear preference for structured radiology reports due to clarity and ease of interpretation, yet a one-size-fits all approach does not address disease complexities. Concurrently, the use of structured radiology templates has increased, driven in part by the need to comply with big data and artificial intelligence as well as reimbursement. Standardization of reporting is one of the first essential steps in the transformation of radiology from "the art of imaging" to a robust data science. Radiology Structured Reporting Handbook: Disease-Specific Templates and Interpretation Pearls by Professors Olga R. Brook, Wieland H. Sommer, and esteemed colleagues is a highly practical guide on structured reporting for every major area of radiology. Featuring disease-specific templates, the book is organized in six sections and 53 chapters. Section one covers core foundation topics, from different definitions of structured reporting and pros and cons to change management and how to build templates. Five disease-specific sections encompass specific cancers and a variety of abdominal, thoracic, neurological, and cardiovascular diseases and conditions. Key Highlights Downloadable disease-specific templates for a variety of clinical entities including cardiovascular, thoracic, abdominal, oncological, and neuroradiology Essential interpretation pearls for specific diseases from top experts in a bullet format, accompanied by relevant figures and tables Together, the templates and pearls provide an essential and unique practice resource for optimal and clinically relevant reporting. The book also serves as a succinct educational tool for radiology trainees and practicing radiologists who may not interpret specific highly specialized types of studies on a daily basis.
Radiology is a powerful tool to detect and diagnose abnormalities by allowing doctors to visually inspect internal pathology that could not otherwise be seen. However, assessing radiological images is limited by variations among practitioners, including deficiencies in their reporting of these imaging examinations as well as in their interpretations. Three main sources of these variations in interpretation are incorrectness of observations in the images, incompleteness of the radiological observations reported to characterize the abnormalities, and inconsistency of these observations with respect to the radiologists' overall impression. I hypothesized that delivering decision support during reporting time to improve completeness and correctness of reports would improve consistency, and therefore, diagnostic performance To test this hypothesis, I formulated a decision support framework that provides feedback to radiologists during the reporting of their radiological observations. I developed this system by creating novel statistical models to link radiological observations, computational imaging features, and disease to recognize incorrectness, incompleteness and inconsistency in reporting. I then harnessed these models to create a quantifiable metric of observation quality. In this dissertation, I describe this system with the following specific aims: (1) developed methods to assess completeness and correctness of radiology reports, (2) evaluated these methods in two important radiological domains (mammography and liver CT), and (3) developed framework to provide feedback to radiologist to ensure consistency between report and diagnosis, improving diagnostic performance. I performed three major experiments to verify these aims. In my first project, I developed an image annotation classifier that predicts the descriptors a radiologist would use in a report to describe a liver lesion on a CT scan. I predicted 30 different types of descriptors and had a mean AUC of 0.816 ± 0.141 with a misclassification rate of 0.1443 ± 0.0881. These results showed that the image annotation framework could be used as a second reader for radiological reports. In my next project, I developed a novel metric to measure whether reports had enough information to justify the radiologist's diagnosis. I found that this measure could accurately predict when radiologists make errors based solely on the evidence they give to justify their diagnosis, with 82.6% classification accuracy. Finally, I created a framework to deliver feedback to the radiologist in order to complete their report in the most efficient manner. I found that using the aforementioned incompleteness score coupled with a myopic, mutual information descriptor selection criteria allows a decision support system to achieve 93.6% diagnostic classification accuracy with an average of 4 observations. This achieves better classification accuracy than the decision support system that uses all 20 descriptors by 1.5%. The results from these three experiments showed that it is feasible to deliver decision support to improve reports, and that improving reports improves diagnostic performance.
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.
A practical guide for radiologists on providing high yield disease-specific reports Multiple studies show that referring physicians have a clear preference for structured radiology reports due to clarity and ease of interpretation, yet a one-size-fits all approach does not address disease complexities. Concurrently, the use of structured radiology templates has increased, driven in part by the need to comply with big data and artificial intelligence as well as reimbursement. Standardization of reporting is one of the first essential steps in the transformation of radiology from "the art of imaging" to a robust data science. Radiology Structured Reporting Handbook: Disease-Specific Templates and Interpretation Pearls by Professors Olga R. Brook, Wieland H. Sommer, and esteemed colleagues is a highly practical guide on structured reporting for every major area of radiology. Featuring disease-specific templates, the book is organized in six sections and 53 chapters. Section one covers core foundation topics, from different definitions of structured reporting and pros and cons to change management and how to build templates. Five disease-specific sections encompass specific cancers and a variety of abdominal, thoracic, neurological, and cardiovascular diseases and conditions. Key Highlights Downloadable disease-specific templates for a variety of clinical entities including cardiovascular, thoracic, abdominal, oncological, and neuroradiology Essential interpretation pearls for specific diseases from top experts in a bullet format, accompanied by relevant figures and tables Together, the templates and pearls provide an essential and unique practice resource for optimal and clinically relevant reporting. The book also serves as a succinct educational tool for radiology trainees and practicing radiologists who may not interpret specific highly specialized types of studies on a daily basis.
This book provides practitioners and scientists with insights into diverse aspects of structured reporting to allow them to develop tools and a knowledge base to ensure that this electronic reporting trend is widely applied. After an introduction to reporting in radiology, various parts of structured reporting are discussed in detail, including an overview of standardized reporting systems, standardized reporting language, DICOM structured reporting, template based structured reporting, and modular reporting. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology. The last chapter addresses the interaction of structured reporting with artificial intelligence and its impact on the future of radiology. Endorsed by the European Society of Medical Imaging Informatics (EuSoMII), the scope of the book is based on the Medical Imaging Informatics sub-sections of the European Society of Radiology (ESR) European Training Curriculum Level I and II. It is a valuable resource for residents, radiologists and students.
An essential resource for medical imaging professionals, this book provides everything you need to create exceptional radiology reports. In an accessible and informal style, one of the foremost experts on radiology reporting gives you practical tips for precise image interpretation and clear communication. This book should be required reading for radiologists in training, and is destined to become an indispensable part of every radiologist's library. Topics include: * The virtues of "normal" * How to say "I don't know" * Building a rhetorical foundation * Spatial relationships * Making recommendations * Suggesting clinical correlation * The hedge * Severity straddling * Size matters * Eponyms in radiology * A summary of reporting best practices * How speech recognition works * Optimizing your speech recognition * Templates and macros * The history of radiology reporting * Structured reporting case study * Structured reporting: what you can do today * Standard terminology for the radiology report * How to think about imaging information * Logic, probability, and the radiology report * Decision making in radiology * The radiology report in 2025
Douglas P. Beall, MD, summarizes the early experiences of established clinicians to create a compendium of everything you need to know during your formative years in radiology. Written for radiology residents and fellows and newly minted radiologists, the Radiology Sourcebook provides vital professional information and sound guidance on such critical issues as resident employment, Board examinations and test results, review courses, fellowships, and CAQs, as well as practical advice on finding a job and what you should know about your professional contract. The book also offers the radiology trainee a proven framework for performing basic procedures in general radiology, understanding the tools and instruments essential to those procedures, obtaining the images needed to make a diagnosis, and reporting the examination once they have been obtained.
The World Health Organization stated that approximately two-thirds of the world’s population lacks adequate access to medical imaging. The scarcity of imaging services in developing regions contributes to a widening disparity of health care and limits global public health programs that require imaging. Radiology is an important component of many global health programs, including those that address tuberculosis, AIDS-related disease, trauma, occupational and environmental exposures, breast cancer screening, and maternal-infant health care. There is a growing need for medical imaging in global health efforts and humanitarian outreach, particularly as an increasing number of academic, government, and non-governmental organizations expand delivery of health care to disadvantaged people worldwide. To systematically deploy clinical imaging services to low-resource settings requires contributions from a variety of disciplines such as clinical radiology, epidemiology, public health, finance, radiation physics, information technology, engineering, and others. This book will review critical concepts for those interested in managing, establishing, or participating in a medical imaging program for resource-limited environments and diverse cross-cultural contexts undergoing imaging technology adaptation.
