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This book provides a practical, comprehensive, state-of-the-art review of bladder cancer. A valuable resource for anyone with an interest in urothelial tumors, this text brings together a multidisciplinary team of experts who have distilled their vast years of experience and knowledge into a concise, easy to digest format. Topics covered range from importance of a pattern recognition in diagnosis and pathologic evaluation to ‘how I do it’ tips on patient selection for appropriate therapies such as chemotherapy, immunotherapy, surgery and radiation. Bridging the gap between a traditional textbook and hands-on experience, this book provides a practical guide to managing day-to-day issues and challenges and brings an algorithmic approach to avoid common pitfalls. Bladder Cancer: A Practical Guide provides a concise yet comprehensive summary of the current status of the field of bladder cancer treatment, guiding patient management and stimulating investigative efforts.
The standard-setting text in oncology for 40 years, DeVita, Hellman and Rosenberg’s Cancer: Principles and Practice of Oncology, 12th Edition, provides authoritative guidance and strategies for managing every type of cancer by stage and presentation. Drs. Vincent T. DeVita, Jr., Theodore S. Lawrence, and Steven A. Rosenberg oversee an outstanding team of expert contributing authors who keep you up to date and fully informed in this fast-changing field. This award-winning reference is also continually updated on Health Library and VitalSource platforms for the life of the edition.
BIOMEDICAL DATA MINING FOR INFORMATION RETRIEVAL This book not only emphasizes traditional computational techniques, but discusses data mining, biomedical image processing, information retrieval with broad coverage of basic scientific applications. Biomedical Data Mining for Information Retrieval comprehensively covers the topic of mining biomedical text, images and visual features towards information retrieval. Biomedical and health informatics is an emerging field of research at the intersection of information science, computer science, and healthcare and brings tremendous opportunities and challenges due to easily available and abundant biomedical data for further analysis. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by analyzing biomedical and healthcare data including patient’s data, electronic health records (EHRs) and lifestyle. Previously, it was a common requirement to have a domain expert to develop a model for biomedical or healthcare; however, recent advancements in representation learning algorithms allows us to automatically to develop the model. Biomedical image mining, a novel research area, due to the vast amount of available biomedical images, increasingly generates and stores digitally. These images are mainly in the form of computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients’ biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions relating to healthcare. Image mining in medicine can help to uncover new relationships between data and reveal new useful information that can be helpful for doctors in treating their patients. Audience Researchers in various fields including computer science, medical informatics, healthcare IOT, artificial intelligence, machine learning, image processing, clinical big data analytics.
Cancer immunotherapy, including immune checkpoint inhibitors (ICIs) and chimeric antigen receptor T-cell (CAR-T) therapy, has revolutionized the paradigm in cancer treatment. However, the clinical outcome of immunotherapy varies considerably among patients and only a minority of patients achieve long-term clinical benefits. This is largely attributed to the fact that existing cancer immunotherapies, which concentrate on several classical targets (CTAL-4, PD-1/PD-L1, etc.) and limited types of immune cell populations (T cells), are insufficient to cope with the complexity of highly heterogeneous tumor microenvironment (TME). This calls for more efforts to not only expand our toolbox for manipulating anticancer immunity but also diversify our combinational strategies. To this end, it is urgent to deeper our understanding of cancer immunotherapy by using both experimental and computational methodologies from multi-scale perspectives: 1) novel targets from either tumor cells or non-tumor cells within TME (e.g., tumor intrinsic resistance drivers, new immune checkpoints, neoantigens), 2) in-depth characterization of more immune cell populations (e.g., macrophages, Tregs, B cells) and their interactions and dynamics within TME, 3) landscape of actionable targets in patient populations for combination design. These efforts will open the avenue of rational design of combinational immunotherapies, allowing researchers and clinicians to design novel targeting therapeutics or to optimally orchestrate combinatory strategies aiming to surmount resistance mechanisms and improve clinical outcomes.