Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reference book compiles together different animal models in cancer research. It provides knowledge and a better understanding of the advancement of the molecular and cellular mechanisms associated with the progression, formation, and clinical results of various types of cancer from the evidence collected from animal models utilized for cancer research. It discusses animal models for screening anti-cancer drugs and exploration of gene therapy. It presents different methods used to construct cancer animal models and the progress of each animal model in tumor research. The book also highlights the applications of genetic engineering, including CRISP/Cas9, in designing and developing animal models for cancer research. Further, it discusses strategies for modeling animals for investigating growth, metastasis, tumor-associated inflammation and microenvironment, cancer stem cells, tumor heterogeneity, and therapeutic resistance. This book is s a valuable resource for basic and translational cancer researchers, clinicians, and health care.

Cancer—Animal models. --- Tumors—Classification. --- Tumors—Immunological aspects. --- Cancer Models. --- Cancer Staging. --- Tumour Immunology.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Tumors --- Neoplasms --- Classification --- classification --- 616-006 --- -Neoplasms --- Tumours --- Pathology --- Cysts (Pathology) --- Oncology --- Tumours. Neoplasms. Blastomata. Choristomata. Hamartomata. Oncology --- -classification --- 616-006 Tumours. Neoplasms. Blastomata. Choristomata. Hamartomata. Oncology --- -616-006 Tumours. Neoplasms. Blastomata. Choristomata. Hamartomata. Oncology --- Cancer --- Tumor coding --- Staging --- Tumors - Classification --- Neoplasms - classification

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Here's the multidisciplinary guidance you need for optimal imaging of malignancies. Radiologists, surgeons, medical oncologists, and radiation oncologists offer state-of-the-art guidelines for diagnosis, staging, and surveillance, equipping all members of the cancer team to make the best possible use of today's noninvasive diagnostic tools. Consult with the best. Dr. Paul M. Silverman and more than 100 other experts from MD Anderson Cancer Center provide you with today's most dependable answers on every aspect of the diagnosis, treatment, and managemen

Cancer -- Imaging. --- Tumors -- Classification. --- Diseases --- Diagnostic Techniques and Procedures --- Diagnosis --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Diagnostic Imaging --- Radiography --- Neoplasms --- Medicine --- Health & Biological Sciences --- Oncology --- Cancer --- Imaging systems in medicine. --- Diagnosis. --- Medical imaging systems --- Medical instruments and apparatus --- Diagnostic Imaging. --- Radiography.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

Medicine --- Pharmacology --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification --- n/a --- naïve Bayes classifiers

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.

Medicine --- Pharmacology --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification --- data sharing --- data management --- data science --- big data --- healthcare --- depression --- psychological treatment --- task sharing --- primary care --- pilot study --- non-specialist health worker --- training --- digital technology --- mental health --- COVID-19 --- SARS-CoV-2 --- pneumonia --- computed tomography --- case fatality rate --- social distancing --- smoking --- metabolically healthy obese phenotype --- metabolic syndrome --- obesity --- coronavirus --- machine learning --- social media --- apache spark --- Twitter --- Arabic language --- distributed computing --- smart cities --- smart healthcare --- smart governance --- Triple Bottom Line (TBL) --- thoracic pain --- tree classification --- cross-validation --- hand-foot-and-mouth disease --- early-warning model --- neural network --- genetic algorithm --- sentinel surveillance system --- outbreak prediction --- artificial intelligence --- vascular access surveillance --- arteriovenous fistula --- end stage kidney disease --- dialysis --- kidney failure --- chronic kidney disease (CKD) --- end-stage kidney disease (ESKD) --- kidney replacement therapy (KRT) --- risk prediction --- naïve Bayes classifiers --- precision medicine --- machine learning models --- data exploratory techniques --- breast cancer diagnosis --- tumors classification