Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computational Radiomics For Cancer Characterization PDF full book. Access full book title Computational Radiomics For Cancer Characterization.
| Author | : Omar Sultan Al-Kadi |
| Publisher | : Frontiers Media SA |
| Total Pages | : 409 |
| Release | : 2022-10-21 |
| Genre | : Medical |
| ISBN | : 2832503152 |
| Author | : Thierry Lefebvre |
| Publisher | : |
| Total Pages | : |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
"Imaging-based quantification and characterization of tumor phenotypes has been the main goal of numerous efforts in recent years for developing and integrating precision oncology in clinical practice. Identifying optimal quantitative image features and machine learning pipelines for computer-aided diagnosis constitute crucial steps towards the development of reproducible, standardized, and clinically relevant imaging biomarkers of cancer phenotypic characteristics. An “image feature” can be understood as an image-derived descriptor of intensity, shape, texture, etc. In radiomics studies, the main hypothesis is that combining many of these quantitative features extracted from tumor regions in medical images can predict underlying genetic or pathological changes occurring in response to disease activity. Given the high variability of processing pipelines in radiomics studies, we first aimed to develop and validate a standardized, IBSI-compliant, and evidence-based processing pipeline for radiomics studies. Second, we aimed to evaluate the diagnostic performance of the well-established robust set of rotationally invariant features from spherical harmonics (SPHARM) decompositions in predicting outcomes from volumetric medical images and compare it to radiomics. Pipelines for these two methods were built and validated on synthetic 3D texture datasets and in two distinct dual-centre diagnostic retrospective studies: i) a study on identifying renal cysts malignancy on contrast-enhanced CT, and ii) a study on identifying histopathological features of endometrial cancer on multi-parametric MRI.For distinguishing benign from malignant renal cysts, a random forest model based on a set of five most discriminative and reproducible radiomics features resulted in high diagnostic performance (testing area under the receiver operating characteristic curve [AUC] = 0.91). Similarly, for SPHARM decomposition coefficients, a tensor logistic regressor resulted in good diagnostic performance for predicting malignancy of renal cysts (testing AUC = 0.83). For detecting histopathological deep myometrial invasion in endometrial cancer on multi-parametric MRI, a random forest model based on our set of five most discriminative and reproducible radiomics features resulted in good diagnostic performance (testing AUC = 0.81). For SPHARM decomposition coefficients, a tensor logistic regressor resulted in higher diagnostic performance using only dynamic-contrast-enhanced MRI images (testing AUC = 0.86). Furthermore, we show that in specific situations, approximate spherical tumor segmentations can rival or even outperform painstakingly obtained but accurate tumor segmentations. Both radiomics features and SPHARM descriptors show promise as reproducible surrogate biomarkers of histopathological features of cancer activity on CT and MRI. Implementing such computational pipelines in clinical practice could improve and accelerate patients’ stratification and decision-making for radiologists and radio-oncologists in cancer diagnosis or treatment"--
| Author | : Hassan Mohy-ud-Din |
| Publisher | : Springer Nature |
| Total Pages | : 100 |
| Release | : 2020-02-24 |
| Genre | : Computers |
| ISBN | : 3030401243 |
This book constitutes the proceedings of the First International Workshop on Radiomics and Radiogenomics in Neuro-oncology, RNO-AI 2019, which was held in conjunction with MICCAI in Shenzhen, China, in October 2019. The 10 full papers presented in this volume were carefully reviewed and selected from 15 submissions. They deal with the development of tools that can automate the analysis and synthesis of neuro-oncologic imaging.
| Author | : Ruijiang Li |
| Publisher | : CRC Press |
| Total Pages | : 420 |
| Release | : 2019-07-09 |
| Genre | : Science |
| ISBN | : 1351208268 |
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
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 142 |
| Release | : 2012-01-16 |
| Genre | : Medical |
| ISBN | : 0309222222 |
Motivated by the explosion of molecular data on humans-particularly data associated with individual patients-and the sense that there are large, as-yet-untapped opportunities to use this data to improve health outcomes, Toward Precision Medicine explores the feasibility and need for "a new taxonomy of human disease based on molecular biology" and develops a potential framework for creating one. The book says that a new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of diseases and ultimately enhance diagnosis and treatment. The "new taxonomy" that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms. The book adds that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights. This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia. Toward Precision Medicine notes that moving toward individualized medicine requires that researchers and health care providers have access to very large sets of health- and disease-related data linked to individual patients. These data are also critical for developing the information commons, the knowledge network of disease, and ultimately the new taxonomy.
| Author | : Leonardo Rundo |
| Publisher | : Mdpi AG |
| Total Pages | : 262 |
| Release | : 2021-12-06 |
| Genre | : Science |
| ISBN | : 9783036525549 |
Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians' unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations-such as segmentation, co-registration, classification, and dimensionality reduction-and multi-omics data integration.
| Author | : Giuliano Mariani |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 319 |
| Release | : 2010-05-10 |
| Genre | : Medical |
| ISBN | : 0387383271 |
This multidisciplinary textbook is designed to be the standard on the subject and is geared for use by physicians who are involved in the care and/or diagnosis of cancer patients. Comprehensive coverage is provided on all aspects of radioguided surgery. Practical information is readily accessible and throughout there is an emphasis on improved decision making. Tables present the indications, performance, and interpretation of procedures at a glance. A wealth of illustrations, including a full-color insert, enhances the application of new concepts.
| Author | : Erik R. Ranschaert |
| Publisher | : Springer |
| Total Pages | : 373 |
| Release | : 2019-01-29 |
| Genre | : Medical |
| ISBN | : 3319948784 |
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.
| Author | : Nikos Paragios |
| Publisher | : Springer |
| Total Pages | : 0 |
| Release | : 2016-10-05 |
| Genre | : Computers |
| ISBN | : 9781489977755 |
This book offers a unique guide to the entire chain of biomedical imaging, explaining how image formation is done, and how the most appropriate algorithms are used to address demands and diagnoses. It is an exceptional tool for radiologists, research scientists, senior undergraduate and graduate students in health sciences and engineering, and university professors.
| Author | : Jun Deng |
| Publisher | : CRC Press |
| Total Pages | : 289 |
| Release | : 2019-03-07 |
| Genre | : Science |
| ISBN | : 1351801120 |
Big Data in Radiation Oncology gives readers an in-depth look into how big data is having an impact on the clinical care of cancer patients. While basic principles and key analytical and processing techniques are introduced in the early chapters, the rest of the book turns to clinical applications, in particular for cancer registries, informatics, radiomics, radiogenomics, patient safety and quality of care, patient-reported outcomes, comparative effectiveness, treatment planning, and clinical decision-making. More features of the book are: Offers the first focused treatment of the role of big data in the clinic and its impact on radiation therapy. Covers applications in cancer registry, radiomics, patient safety, quality of care, treatment planning, decision making, and other key areas. Discusses the fundamental principles and techniques for processing and analysis of big data. Address the use of big data in cancer prevention, detection, prognosis, and management. Provides practical guidance on implementation for clinicians and other stakeholders. Dr. Jun Deng is a professor at the Department of Therapeutic Radiology of Yale University School of Medicine and an ABR board certified medical physicist at Yale-New Haven Hospital. He has received numerous honors and awards such as Fellow of Institute of Physics in 2004, AAPM Medical Physics Travel Grant in 2008, ASTRO IGRT Symposium Travel Grant in 2009, AAPM-IPEM Medical Physics Travel Grant in 2011, and Fellow of AAPM in 2013. Lei Xing, Ph.D., is the Jacob Haimson Professor of Medical Physics and Director of Medical Physics Division of Radiation Oncology Department at Stanford University. His research has been focused on inverse treatment planning, tomographic image reconstruction, CT, optical and PET imaging instrumentations, image guided interventions, nanomedicine, and applications of molecular imaging in radiation oncology. Dr. Xing is on the editorial boards of a number of journals in radiation physics and medical imaging, and is recipient of numerous awards, including the American Cancer Society Research Scholar Award, The Whitaker Foundation Grant Award, and a Max Planck Institute Fellowship.
