Cancer Diagnosis and Prognosis: Role of Radiomics and Artificial Intelligence - by Heath Howard (Hardcover)

About the Book

Cancer is a complex and pervasive disease characterized by the uncontrolled growth and spread of abnormal cells in the body. It can affect any part of the body and has numerous forms, each with its own set of symptoms, treatments, and prognoses. Radiomics, coupled with Artificial Intelligence (AI) has revolutionized cancer diagnosis and prognosis. Radiomics extracts large amounts of quantitative data from medical images, capturing intricate details beyond human perception. AI algorithms then analyze these data to identify subtle patterns indicative of cancer presence, growth, and response to treatment. This synergy enables early detection, precise characterization of tumors, and prediction of patient outcomes. By integrating radiomics with clinical data, AI-driven models personalize treatment strategies, optimizing patient care. Moreover, AI aids in automating image analysis, reducing interpretation time, and enhancing diagnostic accuracy. As technology evolves, the role of radiomics and AI continues to expand, offering unprecedented insights into cancer biology and transforming oncological practices. This book elucidates new techniques of radiomics and artificial intelligence and their applications in cancer diagnosis and prognosis. It is compiled in such a manner that it will provide in-depth knowledge about the subject. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.

Book Synopsis

Cancer is a complex and pervasive disease characterized by the uncontrolled growth and spread of abnormal cells in the body. It can affect any part of the body and has numerous forms, each with its own set of symptoms, treatments, and prognoses. Radiomics, coupled with Artificial Intelligence (AI) has revolutionized cancer diagnosis and prognosis. Radiomics extracts large amounts of quantitative data from medical images, capturing intricate details beyond human perception. AI algorithms then analyze these data to identify subtle patterns indicative of cancer presence, growth, and response to treatment. This synergy enables early detection, precise characterization of tumors, and prediction of patient outcomes. By integrating radiomics with clinical data, AI-driven models personalize treatment strategies, optimizing patient care. Moreover, AI aids in automating image analysis, reducing interpretation time, and enhancing diagnostic accuracy. As technology evolves, the role of radiomics and AI continues to expand, offering unprecedented insights into cancer biology and transforming oncological practices. This book elucidates new techniques of radiomics and artificial intelligence and their applications in cancer diagnosis and prognosis. It is compiled in such a manner that it will provide in-depth knowledge about the subject. It aims to serve as a resource guide for students and experts alike and contribute to the growth of the discipline.