http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Machine learning and artificial intelligence in radiation oncology
"Machine Learning and Artificial Intelligence in Radiation Oncology: A Guide for Clinicians is designed for the application of practical concepts in machine learning to clinical radiation oncology. It addresses the existing void in a resource to educate practicing clinicians about how machine learning can be used to improve clinical and patient-centered outcomes. This book is divided into three sections: the first addresses fundamental concepts of machine learning and radiation oncology, detailing techniques applied in genomics; the second section discusses translational opportunities, such as in radiogenomics and autosegmentation; and the final section encompasses current clinical applications in clinical decision making, how to integrate AI into workflow, use cases, and cross-collaborations with industry. The book is a valuable resource for oncologists, radiologists and several members of biomedical field who need to learn more about machine learning as a support for radiation oncology."--Provided by publisher.
Fundamentals and overview. Fundamentals of machine learning. Abstract -- Artificial intelligence and machine learning -- Capturing and quantifying experience -- Learning from experience -- Nature of automated learning -- Types of machine learning -- Summary -- References -- Artificial intelligence, machine learning, and bioethics in clinical medicine. Abstract -- Introduction -- Principles of bioethics and AI/ML in clinical medicine and research -- Ethical partnerships with AI/ML : recommendations for clinicians -- Conclusion -- References -- Machine learning applications in cancer genomics. Abstract -- Introduction -- Overview of genomic technologies -- Applications of genomics in oncology -- Common hurdles of machine learning in genomics -- Future directions -- References -- Radiomics : "unlocking the potential of medical images for precision radiation oncology." Abstract -- Introduction -- Radiomics workflow -- Potential pitfalls in the radiomics pipeline -- Recommendations for the standardization of radiomics research -- A practical roadmap for radiomics research in radiation oncology -- Translation of radiomics into the clinic -- Conclusion -- References -- Deep learning for medical image segmentation. Abstract -- Clinical need for automated image segmentation -- Rationale of using deep learning for medical image segmentation -- Typical deep learning framework -- Practical considerations for segmentation model learning -- Image pre-processing -- Image patch selection -- Data augmentation -- Model fusion and output uncertainty assessment -- Role of international competitions in medical image segmentation -- AI-based image segmentation and beyond -- Conclusion -- References -- Natural language processing in oncology. Abstract -- What is natural language processing? -- NLP in oncology -- Use cases for NLP in oncology -- The role of the clinician in developing an NLP system -- General NLP tasks and challenges -- Medical NLP tasks and challenges -- NLP approaches -- Tools and resources -- Clinical implementation of an NLP system -- Conclusions, limitations and future directions -- References -- Evaluating machine learning models: From development to clinical deployment. Abstract -- Introduction -- Model development -- Model performance evaluation -- Real world impact assessment -- References -- Research applications. Germline genomics in radiotherapy. Abstract -- Overview of germline genomic analyses -- Modern clinical genomics -- Artificial intelligence in clinical genomics -- Genome wide association studies (GWAS) -- Radiogenomics -- Machine learning and radiogenomics -- Detection of epistasis using ML -- Approaches to increasing statistical power using ML -- Filtering : Pre-processing independent of model -- Wrapper and embedded feature selection -- Multi-step feature selection in radiogenomics -- Conclusions -- References -- Tumor genomics in radiotherapy. Abstract -- Introduction -- Bioinformatics of tumor genomics -- Example applications -- Challenges and recommendations -- Conclusions -- References -- Radiotherapy outcome prediction with medical imaging. Abstract -- Introduction -- Image biomarkers from pre-treatment imaging and RT outcome -- Delta image biomarkers associations with radiation dose and outcome -- Future directions and challenges in the clinical implementation and application of image biomarker prediction models in RT -- Funding sources -- Conclusion -- References -- Causal inference for oncology. Abstract -- Introduction : background and history -- Counterfactuals -- Causal graphs -- Emerging uses for machine learning for causal inference in oncology -- References -- Machine learning in quality assurance and treatment delivery. Abstract -- Introduction -- QA in the treatment planning process -- QA in the treatment delivery process -- Conclusion -- References -- Clinical applications and future developments. Case study : Deep learning in radiotherapy auto segmentation. Abstract -- Clinical application of artificial intelligence (AI) to radiotherapy delineation -- Optimization of AI driven auto-segmentation in the radiotherapy workflow -- Clinical application of deep learning to radiotherapy -- Challenges and solutions to clinical application of deep learning in auto-segmentation -- Using federated learning to aid clinical adaptation -- Conclusion -- References -- Case study : adaptive radiotherapy in the clinic. Abstract -- Introduction -- Adaptive radiation therapy for head and neck cancers -- Adaptive radiation therapy workflows -- Conclusion -- References -- Case study : handling small datasets Transfer learning for medical images. Abstract -- Introduction -- Dataset size -- Data imbalance -- Data augmentation -- Transfer learning -- Case study : Sarcopenia segmentation -- Conclusion -- References -- Case study : lymph node malignancy classification for head and neck cancer radiation therapy. Abstract -- Introduction -- Initial model development using patients enrolled in the INFIELD trial -- Model fine-tuning and validation on surgical patients with nodal status confirmed by pathology -- Clinical implementation of the model for a prospective phase II study of involved nodal radiation therapy -- Further development -- Summary -- References -- Training the current and next generation in machine learning and artificial intelligence applications in radiation oncology. Abstract -- Introduction -- Learning to use, evaluate, and validate AI -- Workforce needs in oncology -- Case for informatics/ML integration into radiation oncology -- Training opportunities during residency -- Informatics fellowships -- AI residencies in industry -- Institutes, workshops, and conferences -- Grant funding -- Conclusion -- References -- Governance issues and commercialization. Abstract -- Introduction -- Developing commercial software -- The regulatory landscape -- A note on AI in clinical software -- Conclusion -- Index.
edited by John Kang, Tim Rattay, Barry S. Rosenstein.
Includes bibliographical references and index.