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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 : 2021-05-01
ISBN : 0128181028
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.

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

Neuroimaging Techniques in Clinical Practice

Neuroimaging Techniques in Clinical Practice Book
Author : Manoj Mannil,Sebastian F.-X. Winklhofer
Publisher : Springer Nature
Release : 2020-08-11
ISBN : 303048419X
Language : En, Es, Fr & De

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Book Description :

This book provides a concise overview of emerging technologies in the field of modern neuroimaging. Fundamental principles of the main imaging modalities are described as well as advanced imaging techniqes including diffusion weighted imaging, perfusion imaging, arterial spin labeling, diffusion tensor imaging, intravoxel incoherent motion, MR spectroscopy, functional MRI, and artificial intelligence. The physical concepts underlying each imaging technique are carefully and clearly explained in a way suited to a medical audience without prior technical knowledge. In addition, the clinical applications of the various techniques are described with the aid of illustrative clinical examples. Helpful background information is also presented on the core principles of MRI and the evolution of neuroimaging, and important references to current medical research are highlighted. The book will meet the needs of a range of non-technological professionals with an interest in advanced neuroimaging, including radiology researchers and clinicians in the fields of neurology, neurosurgery, and psychiatry.

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.

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 : Unknown
Release : 2017
ISBN : 0987650XXX
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.

Use of Radiomic Data to Improve Imputation of HPV p16 Status in Oropharyngeal Cancer

Use of Radiomic Data to Improve Imputation of HPV  p16  Status in Oropharyngeal Cancer Book
Author : Aleix Lascorz Guiu
Publisher : Unknown
Release : 2019
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

The incidence of oropharyngeal cancer has been steadily increasing during the past decades. This increase is linked with human papillomavirus, one of the most common sexually transmitted diseases in Canada and worldwide. Recent studies have shown the importance of using p16 testing to assess the HPV status of all oropharyngeal cancer patients on diagnostic. However, that practice was not common during early 2000, making historical data flawed. Many imputation models have been built to retroactively predict the HPV status of oropharyngeal cancer patients that were not tested. This models are based on clinical data, which is easy to store and analyze. However, recent advancements in the field of radiomics have enabled the use of CT scans obtained from patients to build models for cancer behavior. In this study, we take a novel approach to HPV status imputation by building machine learning models that utilize not only clinical data but also imaging features, aiming to show a significant improvement over classical models. The increase of performance between state of the art clinical models and our models will be assessed through the use of the RADCURE dataset from the Princess Margaret.

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro oncology

Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro oncology Book
Author : Seyed Mostafa Kia,Hassan Mohy-ud-Din,Ahmed Abdulkadir,Cher Bass,Mohamad Habes,Jane Maryam Rondina,Chantal Tax,Hongzhi Wang,Thomas Wolfers,Saima Rathore,Madhura Ingalhalikar
Publisher : Springer Nature
Release : 2020-12-30
ISBN : 3030668436
Language : En, Es, Fr & De

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Book Description :

This book constitutes the refereed proceedings of the Third International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2020, and the Second International Workshop on Radiogenomics in Neuro-oncology, RNO-AI 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020.* For MLCN 2020, 18 papers out of 28 submissions were accepted for publication. The accepted papers present novel contributions in both developing new machine learning methods and applications of existing methods to solve challenging problems in clinical neuroimaging. For RNO-AI 2020, all 8 submissions were accepted for publication. They focus on addressing the problems of applying machine learning to large and multi-site clinical neuroimaging datasets. The workshop aimed to bring together experts in both machine learning and clinical neuroimaging to discuss and hopefully bridge the existing challenges of applied machine learning in clinical neuroscience. *The workshops were held virtually due to the COVID-19 pandemic.

A Compendium of Multidisciplinary Medical Physics Research Updates 2020 Series 1

A Compendium of Multidisciplinary Medical Physics Research Updates    2020  Series 1  Book
Author : Dr.Hakim Saboowala
Publisher : Dr.Hakim Saboowala
Release : 2020-10-21
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

A Compendium of Multidisciplinary Medical Physics Research Updates… 2020. Series-1. I believe that updated reading is the best and simplest way for Medicos to derive and construct knowledge from a source at an affordable price and that too at one click! Moreover, inspired by a recent overwhelming response for publishing an E- Medical Booklet, titled: “A Compendium of Multidisciplinary Medical Physics Research Updates… 2020.”, I have opted, henceforth to compile and publish E- Medical Booklets in the form of several Series to update the Medicos, containing the Research Updates of Medical Physics. Thus, an effort has been made in this Series-1 to include the following research updates of Medical Physics: 1.Multifaceted radiomics’ predicts cancer metastasis risk. 2.Fractionated heating could improve cancer therapy with thermosensitive drugs. 3.Boron Neutron capture therapy is back on the agenda. 4.Virtual imaging trials aim to optimize COVID-19 screening. 5.What Lies Between Grey and White in the Brain. 6.New Strategies for Restoring Myelin on Damaged Nerve Cells. 7.Human Intelligence Just Got Less Mysterious. 8.Earwax Sampling Could Measure Stress Hormone. 9.Root Bacterium to Fight Alzheimer’s. 10.Why Low Oxygen Damages the Brain. I hope that this booklet will provide interesting knowledge and serve as a comprehensive ready reference for many medicos practicing in their respective fields at one click! …Dr. H. K. Saboowala. M.B.(Bom). M.R.S.H.(London).

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 : Unknown
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.

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.

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 : Anonim
Publisher : Unknown
Release : 2018
ISBN : 0987650XXX
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.

Auto Segmentation for Radiation Oncology

Auto Segmentation for Radiation Oncology Book
Author : Jinzhong Yang,Gregory C. Sharp,Mark J. Gooding
Publisher : CRC Press
Release : 2021-04-19
ISBN : 1000376303
Language : En, Es, Fr & De

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Book Description :

This book provides a comprehensive introduction to current state-of-the-art auto-segmentation approaches used in radiation oncology for auto-delineation of organs-of-risk for thoracic radiation treatment planning. Containing the latest, cutting edge technologies and treatments, it explores deep-learning methods, multi-atlas-based methods, and model-based methods that are currently being developed for clinical radiation oncology applications. Each chapter focuses on a specific aspect of algorithm choices and discusses the impact of the different algorithm modules to the algorithm performance as well as the implementation issues for clinical use (including data curation challenges and auto-contour evaluations). This book is an ideal guide for radiation oncology centers looking to learn more about potential auto-segmentation tools for their clinic in addition to medical physicists commissioning auto-segmentation for clinical use. Features: Up-to-date with the latest technologies in the field Edited by leading authorities in the area, with chapter contributions from subject area specialists All approaches presented in this book are validated using a standard benchmark dataset established by the Thoracic Auto-segmentation Challenge held as an event of the 2017 Annual Meeting of American Association of Physicists in Medicine

The Basic Science of Oncology Sixth Edition

The Basic Science of Oncology  Sixth Edition Book
Author : Lea Harrington,Robert E. Bristow,Ian F. Tannock,Richard Hill
Publisher : McGraw Hill Professional
Release : 2021-01-08
ISBN : 1259862089
Language : En, Es, Fr & De

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Book Description :

Complete coverage of the basis of cancer and molecular biology – from globally recognized experts The Basic Science of Oncology is an accessible and thorough introduction to cancer causation, cancer biology, and the biology underlying cancer treatment. You’ll find everything you need to know about the latest critical thinking in oncology, as well ready to apply information about state-of-the-art science and therapeutic applications. Written by leading oncology researchers and clinicians, this is an essential resource for health professionals, students, advanced undergraduates and graduates in biological sciences, and clinicians needing an understanding of cancer cells. Presented in full-color, The Basic Science of Oncology reflects the latest research and developments in the field. Features NEW chapters: Epigenetics and Principles of Genome Regulation and Targeted Cancer Diagnosis and Treatment Thoroughly revised content, with expanded coverage of key topics such as immune system and immunotherapy, tumor growth and metabolism, vaccine development, methods of molecular analysis, tumor environment, and more The most current, evidence-based oncology primer—one that encapsulates the science of cancer causation, cancer biology, and cancer therapy Key insights into molecular and genetic aspects of cancer familiarize you with cancer biology as applied to prognosis and personalized cancer medicine In-depth focus on the discovery, evaluation, and biology of anti-cancer drugs, immunotherapy, and molecularly-targeted agents Up-to-date coverage of the basic science of radiation therapy

Artificial Intelligence Deep Learning in Oncological Radiomics and Challenges of Interpretability and Data Harmonization

Artificial Intelligence  Deep Learning in Oncological Radiomics and Challenges of Interpretability and Data Harmonization Book
Author : Dani Wade
Publisher : Unknown
Release : 2021-04-09
ISBN : 0987650XXX
Language : En, Es, Fr & De

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Book Description :

AI in oncologyHealthcare is expected to be highly impacted by machine learning (ML)-based artificial intelligence (AI). As deep learning (DL) relying on neural networks trained with large datasets has demonstrated state-of- the-art performances in numerous applications, massive structural changes in information and data processing in this sector are expected. Oncology is especially targeted by these developments, cancer being a major worldwide issue (18.1 million cases and 9.6 million deaths in 2018, respectively 22 and 13 million projected for 2030) [1]. Regarding predictive modeling based on multimodal medical imaging such as CT (computed tomography), PET/CT (positron emission tomography / CT) or MRI (magnetic resonance imaging), both academic and private research rely on ML/DL methods, however their clinical implementation and acceptability are currently lacking.