Computational Intelligence in Oncology: Applications in Diagnosis, Prognosis and Therapeutics of Cancers

This book highlights recent applications of computational intelligence (CI) methods in the field of computational oncology, focusing on cancer diagnosis, prognosis, and optimized therapeutics. CI methods, such as artificial neural networks, fuzzy logic, evolutionary computations, machine learning, and deep learning, are used to understand the hallmarks of cancer development, progression, and effective therapeutics. The book aims to provide state-of-the-art applications of CI methods derived from core computer sciences to back medical oncology, including multi-omics exploration, gene expression analysis, gene signature identification, genomic characterization, anti-cancer drug design, and drug response prediction.

Format: Paperback / softback
Length: 467 pages
Publication date: 03 March 2023
Publisher: Springer Verlag, Singapore

The cancer, a complex and diverse disease, is categorized into various subtypes, according to the World Health Organization's (WHO) latest report. In 2020 alone, cancer claimed the lives of over 10 million people, making it a leading cause of death worldwide. As a result, early diagnosis, prognosis, and classification of cancer to specific subtypes have become crucial for effective clinical management and the development of personalized therapeutics. Computational intelligence (CI) methods, including artificial neural networks (ANNs), fuzzy logic, evolutionary computations, various machine learning and deep learning algorithms, and nature-inspired techniques, have been extensively employed in various aspects of oncology research, including cancer diagnosis, prognosis, therapeutics, and optimized clinical management.

Significant advancements have been made in understanding the hallmarks of cancer development, progression, and the development of effective therapeutics. However, despite the presence of extrinsic and intrinsic factors contributing to the rising incidence of cancer cases, the detection, diagnosis, prognosis, and therapeutics remain major challenges for the medical community. The advent of CI-based approaches, including nature-inspired techniques, and access to clinical data from high-throughput experiments has provided renewed hope for developing and implementing CI in various aspects of oncology.

The primary objective of this book is to showcase state-of-the-art applications of CI methods that have been derived from core computer sciences and applied to support medical oncology. The book encompasses chapters on artificial neural networks, fuzzy logic and fuzzy inference systems, evolutionary computations, machine learning and deep learning algorithms, and nature-inspired techniques, among others. Each chapter presents detailed discussions on the latest research findings, theoretical frameworks, and practical applications in cancer diagnosis, prognosis, therapeutics, and optimized clinical management.

By bringing together experts from various fields, this book aims to provide a comprehensive and up-to-date resource for medical consultants, researchers, and oncologists interested in leveraging CI to advance cancer care. The editors have carefully selected contributions from leading researchers and practitioners in the field, ensuring that the content is both relevant and accessible to a broad audience.

In conclusion, this book represents a significant milestone in the field of computational oncology. It showcases the power of CI methods in addressing the challenges faced by cancer patients and researchers alike. By providing a comprehensive overview of the latest applications of CI in oncology, the book aims to inspire and empower the medical community to embrace and integrate CI into their practices, ultimately improving the outcomes and quality of life for cancer patients.

Weight: 741g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811692239
Edition number: 1st ed. 2022