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Radiomics And Its Clinical Application

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Radiomics and its Clinical Application

Radiomics and its Clinical Application Book
Author : Jie Tian,Di Dong,Zhenyu Liu,Jingwei Wei
Publisher : Academic Press
Release : 2021-05-15
ISBN : 9780128181010
Language : En, Es, Fr & De

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Book Description :

Radiomics and its Clinical Application: Artificial Intelligence and Medical Big Data describes the two key aspects of radiomic clinical practice, including precision diagnosis and the therapeutic effect and prognostic evaluation that make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, radiologists, pathologists, oncologists and surgeons wanting to understand radiomics and its potential in clinical practice. Provides an introduction to the concepts of radiomics Presents an in-depth discussion on core technologies and methods Summarizes current radiomics research, perspectives on the future of radiomics, and the challenges ahead Includes an introduction to several platforms that are planned to be built, including cooperation, data sharing, software and application platforms

Radiomics and Radiogenomics

Radiomics and Radiogenomics Book
Author : Ruijiang Li,Lei Xing,Sandy Napel,Daniel L. Rubin
Publisher : CRC Press
Release : 2019-07-09
ISBN : 1351208268
Language : En, Es, Fr & De

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Book Description :

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

Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging

Artificial Intelligence in Decision Support Systems for Diagnosis in Medical Imaging Book
Author : Kenji Suzuki,Yisong Chen
Publisher : Springer
Release : 2018-01-09
ISBN : 331968843X
Language : En, Es, Fr & De

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Book Description :

This book offers the first comprehensive overview of artificial intelligence (AI) technologies in decision support systems for diagnosis based on medical images, presenting cutting-edge insights from thirteen leading research groups around the world. Medical imaging offers essential information on patients’ medical condition, and clues to causes of their symptoms and diseases. Modern imaging modalities, however, also produce a large number of images that physicians have to accurately interpret. This can lead to an “information overload” for physicians, and can complicate their decision-making. As such, intelligent decision support systems have become a vital element in medical-image-based diagnosis and treatment. Presenting extensive information on this growing field of AI, the book offers a valuable reference guide for professors, students, researchers and professionals who want to learn about the most recent developments and advances in the field.

Handbook of Medical Image Computing and Computer Assisted Intervention

Handbook of Medical Image Computing and Computer Assisted Intervention Book
Author : S. Kevin Zhou,Daniel Rueckert,Gabor Fichtinger
Publisher : Academic Press
Release : 2019-10-18
ISBN : 0128165863
Language : En, Es, Fr & De

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Book Description :

Handbook of Medical Image Computing and Computer Assisted Intervention presents important advanced methods and state-of-the art research in medical image computing and computer assisted intervention, providing a comprehensive reference on current technical approaches and solutions, while also offering proven algorithms for a variety of essential medical imaging applications. This book is written primarily for university researchers, graduate students and professional practitioners (assuming an elementary level of linear algebra, probability and statistics, and signal processing) working on medical image computing and computer assisted intervention. Presents the key research challenges in medical image computing and computer-assisted intervention Written by leading authorities of the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society Contains state-of-the-art technical approaches to key challenges Demonstrates proven algorithms for a whole range of essential medical imaging applications Includes source codes for use in a plug-and-play manner Embraces future directions in the fields of medical image computing and computer-assisted intervention

Molecular Imaging in Oncology

Molecular Imaging in Oncology Book
Author : Otmar Schober,Fabian Kiessling,Jürgen Debus
Publisher : Springer Nature
Release : 2020-06-27
ISBN : 3030426181
Language : En, Es, Fr & De

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Book Description :

This book discusses the most significant recent advances in oncological molecular imaging, covering the full spectrum from basic and preclinical research to clinical practice. The content is divided into five sections, the first of which is devoted to standardized and emerging technologies and probe designs for different modalities, such as PET, SPECT, optical and optoacoustic imaging, ultrasound, CT, and MRI. The second section focuses on multiscale preclinical applications ranging from advanced microscopy and mass spectroscopy to whole-body imaging. In the third section, various clinical applications are presented, including image-guided surgery and the radiomic analysis of multiple imaging features. The final two sections are dedicated to the emerging, crucial role that molecular imaging can play in the planning and monitoring of external and internal radiotherapy, and to future challenges and prospects in multimodality imaging. Given its scope, the handbook will benefit all readers who are interested in the revolution in diagnostic and therapeutic oncology that is now being brought about by molecular imaging.

Contribution of FDG to Modern Medicine Part II An Issue of PET Clinics

Contribution of FDG to Modern Medicine  Part II  An Issue of PET Clinics  Book
Author : Søren Hess
Publisher : Elsevier Health Sciences
Release : 2014-12-27
ISBN : 0323341993
Language : En, Es, Fr & De

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Book Description :

Part II of the important issue on Contribution of FDG to Modern Medicine. Articles will include: FDG in infectious/inflammatory diseases, FDG in cardiovascular disease, Assesment of treatment response using PET, PET based chemotherapy response assessment, PET based radiation therapy planning, PET based interventional radiology, PET/MRI, Evolving and upcoming applications of FDG-PET in medicine, and more.

Radiomics and Radiogenomics in Neuro oncology

Radiomics and Radiogenomics in Neuro oncology Book
Author : Hassan Mohy-ud-Din,Saima Rathore
Publisher : Springer Nature
Release : 2020-02-24
ISBN : 3030401243
Language : En, Es, Fr & De

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Book Description :

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.

Big Data in Radiation Oncology

Big Data in Radiation Oncology Book
Author : Jun Deng,Lei Xing
Publisher : CRC Press
Release : 2019-03-07
ISBN : 1351801112
Language : En, Es, Fr & De

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Book Description :

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.

Artificial Intelligence in Medicine

Artificial Intelligence in Medicine Book
Author : Lei Xing,Maryellen L. Giger,James K Min
Publisher : Academic Press
Release : 2020-09-16
ISBN : 0128212586
Language : En, Es, Fr & De

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Book Description :

Artificial Intelligence Medicine: Technical Basis and Clinical Applications presents a comprehensive overview of the field, ranging from its history and technical foundations, to specific clinical applications and finally to prospects. Artificial Intelligence (AI) is expanding across all domains at a breakneck speed. Medicine, with the availability of large multidimensional datasets, lends itself to strong potential advancement with the appropriate harnessing of AI. The integration of AI can occur throughout the continuum of medicine: from basic laboratory discovery to clinical application and healthcare delivery. Integrating AI within medicine has been met with both excitement and scepticism. By understanding how AI works, and developing an appreciation for both limitations and strengths, clinicians can harness its computational power to streamline workflow and improve patient care. It also provides the opportunity to improve upon research methodologies beyond what is currently available using traditional statistical approaches. On the other hand, computers scientists and data analysts can provide solutions, but often lack easy access to clinical insight that may help focus their efforts. This book provides vital background knowledge to help bring these two groups together, and to engage in more streamlined dialogue to yield productive collaborative solutions in the field of medicine. Provides history and overview of artificial intelligence, as narrated by pioneers in the field Discusses broad and deep background and updates on recent advances in both medicine and artificial intelligence that enabled the application of artificial intelligence Addresses the ever-expanding application of this novel technology and discusses some of the unique challenges associated with such an approach

18F FDG PET CT Based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer

18F FDG PET CT Based Radiomics for the Prediction of Radiochemotherapy Treatment Outcomes of Cervical Cancer Book
Author : Badereldeen Altazi
Publisher :
Release : 2017
ISBN :
Language : En, Es, Fr & De

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Book Description :

Cervical cancer remains the third most commonly diagnosed gynecological malignancy in the United States and throughout the world despite being potentially preventable. Patients diagnosed with cervical cancer may develop local recurrence in the cervix and surrounding structures (vaginal apex, parametrial, or paracervical), regional recurrence in pelvic lymph nodes, distant metastasis, or a combination of all. The management of such treatment outcomes has not been subject to rigorous investigation. Therefore, there is a need for studies and clinical trials that focus on decision making to support the choice of the best treatment modality that leads to the minimal number of adverse treatment outcomes.

Imaging in Clinical Oncology

Imaging in Clinical Oncology Book
Author : Athanasios Gouliamos,John A. Andreou,Paris A. Kosmidis
Publisher : Springer
Release : 2018-04-11
ISBN : 3319688731
Language : En, Es, Fr & De

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Book Description :

This is the second edition of a well-received book reflecting the state of the art in oncologic imaging research and promoting mutual understanding and collaboration between radiologists and clinical oncologists. It presents all currently available imaging modalities and covers a broad spectrum of oncologic diseases for most organ systems. Today, oncologic imaging faces the challenge of improving and refining concepts for precise tumor delineation and biologic/functional tumor characterization, as well as for purposes of creating individual treatment plans. The concept of radiomics has further advanced the conversion of images into mineable data and subsequent analysis of said data for decision-making support. Since the release of the book’s first edition, radiomics has been introduced in oncology studies and can be performed with tomographic images from CT, MRI and PET/CT studies. The combination of radiomic data with genomic features is known as radiogenomics, and can potentially offer additional decision-making support. This book will be of interest to clinical oncologists with regard to the diagnosis, staging, treatment and follow-up on various tumors affecting the CNS, chest, abdomen, urogenital and musculoskeletal systems.

Deep Learning and Radiomics of Breast Cancer on DCE MRI in Assessment of Malignancy and Response to Therapy

Deep Learning and Radiomics of Breast Cancer on DCE MRI in Assessment of Malignancy and Response to Therapy Book
Author : Natalia Antropova
Publisher :
Release : 2018
ISBN : 9780438083431
Language : En, Es, Fr & De

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Book Description :

The medical significance of this research is that it has potential to improve DCE-MRI-based breast cancer management. The developed deep learning methods and their fusion with conventional radiomics can reduce human burden and allow for more rapid and accurate analysis of the images.

Diffusion Weighted Imaging of the Gastrointestinal Tract

Diffusion Weighted Imaging of the Gastrointestinal Tract Book
Author : Sofia Gourtsoyianni,Nikolaos Papanikolaou
Publisher : Springer
Release : 2018-12-13
ISBN : 9783030065249
Language : En, Es, Fr & De

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Book Description :

This book explains how diffusion weighted imaging has been incorporated in routine MRI examinations of the abdomen and pelvis: though its clinical role is still evolving, it is already considered an important tool for the assessment of rectal cancer treatment response, as was confirmed in recent ESGAR consensus statements. The standardization and clinical validation of quantitative DWI related biomarkers are still in progress, although certain efforts have been undertaken to establish imaging guidelines for different clinical indications/body parts. The book reviews the technical aspects and clinical applications of DWI in imaging of the GI tract, and provides specific technical details (imaging protocols, artefacts, optimization techniques) for each GI tract division. This volume is mainly intended for radiologists who are interested in abdominal radiology, as well as radiology residents. Given that magnetic resonance physics is complex and can be cumbersome to learn, the authors have made it as simple and practical as possible.

Radiomics based Features for Pattern Recognition of Lung Cancer Histopathology and Metastases

Radiomics based Features for Pattern Recognition of Lung Cancer Histopathology and Metastases Book
Author : N.A
Publisher :
Release : 2018
ISBN :
Language : En, Es, Fr & De

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Book Description :

Highlights: Shape features presented greatest potential on nodal metastasis pattern recognition. Gray-level cooccurrence matrix texture features presented greatest potential on distant metastasis and histopathological pattern recognition. Our radiomics model may provide additional information for therapy decision support based on metastases prediction and aid the histopathological subtype diagnosis. Abstract: Background and Objectives: lung cancer is the leading cause of cancer-related deaths in the world, and its poor prognosis varies markedly according to tumor staging. Computed tomography (CT) is the imaging modality of choice for lung cancer evaluation, being used for diagnosis and clinical staging. Besides tumor stage, other features, like histopathological subtype, can also add prognostic information. In this work, radiomics-based CT features were used to predict lung cancer histopathology and metastases using machine learning models. Methods: local image datasets of confirmed primary malignant pulmonary tumors were retrospectively evaluated for testing and validation. CT images acquired with same protocol were semiautomatically segmented. Tumors were characterized by clinical features and computer attributes of intensity, histogram, texture, shape, and volume. Three machine learning classifiers used up to 100 selected features to perform the analysis. Results: radiomics-based features yielded areas under the receiver operating characteristic curve of 0.89, 0.97, and 0.92 at testing and 0.75, 0.71, and 0.81 at validation for lymph nodal metastasis, distant metastasis, and histopathology pattern recognition, respectively. Conclusions: the radiomics characterization approach presented great potential to be used in a computational model to aid lung cancer histopathological subtype diagnosis as a "virtual biopsy" and metastatic prediction for therapy decision support without the necessity of a whole-body imaging scanning.

Precision Radiation Oncology

Precision Radiation Oncology Book
Author : Bruce G. Haffty,Sharad Goyal
Publisher : Rutgers University Press
Release : 2018-05-24
ISBN : 0813592542
Language : En, Es, Fr & De

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Book Description :

Precision medicine is a rapidly-evolving field in the management of cancer. The use of novel molecular or genetic signatures in local-regional management is still in its infancy. Precision Radiation Oncology demystifies this state-of-the-art research and technology. By describing current existing clinical and pathologic features, and focusing on the ability to improve outcomes in cancer using radiation therapy, this book discusses incorporating novel genomic- or biology-based biomarkers in the treatment of patients moving radiation oncology into precision/personalized medicine. Precision Radiation Oncology provides readers with an overview of the new developments of precision medicine in radiation oncology, further advancing the integration of new research findings into individualized radiation therapy and its clinical applications.

Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI

Learning to Predict Clinical Outcomes from Soft Tissue Sarcoma MRI Book
Author : Hamidreza Farhidzadeh
Publisher :
Release : 2017
ISBN :
Language : En, Es, Fr & De

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Book Description :

Soft Tissue Sarcomas (STS) are among the most dangerous diseases, with a 50% mortality rate in the USA in 2016. Heterogeneous responses to the treatments of the same sub-type of STS as well as intra-tumor heterogeneity make the study of biopsies imprecise. Radiologists make efforts to find non-invasive approaches to gather useful and important information regarding characteristics and behaviors of STS tumors, such as aggressiveness and recurrence. Quantitative image analysis is an approach to integrate information extracted using data science, such as data mining and machine learning with biological andand recurrence. Quantitative image analysis is an approach to integrate information extracted clinical data to assist radiologists in making the best recommendation on clinical trials and the course of treatment. The new methods in "Radiomics" extract meaningful features from medical imaging data for diagnostic and prognostic goals. Furthermore, features extracted from Convolutional Neural Networks (CNNs) are demonstrating very powerful and robust performance in computer aided decision systems (CADs). Also, a well-known computer vision approach, Bag of Visual Words, has recently been applied on imaging data for machine learning purposes such as classification of different types of tumors based on their specific behavior and phenotype. These approaches are not fully and widely investigated in STS. This dissertation provides novel versions of image analysis based on Radiomics and Bag of Visual Words integrated with deep features to quantify the heterogeneity of entire STS as well as sub-regions, which have predictive and prognostic imaging features, from single and multi-sequence Magnetic Resonance Imaging (MRI). STS are types of cancer which are rarely touched in term of quantitative cancer analysis versus other type of cancers such as lung, brain and breast cancers. This dissertation does a comprehensive analysis on available data in 2D and multi-slice to predict the behavior of the STS with regard to clinical outcomes such as recurrence or metastasis and amount of tumor necrosis. The experimental results using Radiomics as well as a new ensemble of Bags of Visual Words framework are promising with 91.66% classification accuracy and 0.91 AUC for metastasis, using ensemble of Bags of Visual Words framework integrated with deep features, and 82.44% classification accuracy with 0.63 AUC for necrosis progression, using Radiomics framework, in tests on the available datasets.

Healthcare and Artificial Intelligence

Healthcare and Artificial Intelligence Book
Author : Bernard Nordlinger,Cédric Villani,Daniela Rus
Publisher : Springer Nature
Release : 2020-03-17
ISBN : 3030321614
Language : En, Es, Fr & De

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Book Description :

This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details.

The Use Of Textural Kinetic Habitats To Mine Diagnostic Information From Dce Mr Images Of Breast Tumors

The Use Of Textural Kinetic Habitats To Mine Diagnostic Information From Dce Mr Images Of Breast Tumors Book
Author : Baishali Chaudhury
Publisher :
Release : 2015
ISBN :
Language : En, Es, Fr & De

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Book Description :

The highest AUC of 0.91 was achieved for classifying tumors with no ALN metastases from tumors with 4 or more ALN metastases. For classifying tumors based on ER status the highest AUC of 0.87 was achieved. These results were acquired by utilizing the textural kinetic features from the tumor habitat with rapid delayed washout. The results presented in this work showed that the heterogeneity within the tumor habitats which showed rapid contrast washout in the delayed phase, correlated with aggressive cellular phenotypes. This work hypothesizes that successfully quantifying these prognostic factors will prove to be clinically significant as it can improve the diagnostic accuracy. This, in turn, will im- prove the breast cancer treatment paradigm by providing more tailored treatment regimens for aggressive tumors.