Hematology / Oncology: Artificial Intelligence

Application of Artificial Intelligence in Early Detection of Lung Cancer by Madhuchanda Kar; Jhilam Mukherjee; Amlan Chakrabarti et al, 2024 Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients' outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.

Artificial Intelligence and Precision Oncology by Zodwa Dlamini, 2023 This book highlights the use of artificial intelligence (AI), big data and precision oncology for medical decision making in cancer screening, diagnosis, prognosis and treatment. Precision oncology has long been thought of as ideal for the management and treatment of cancer. This strategy promises to revolutionize the treatment, control, and prevention of cancer by tailoring tests, treatments and predictions to specific individuals or population groups. In order to accomplish these goals, vast amounts of patient or population group specific data needs to be integrated and analysed to be able to identify key patterns or features which can be used to define or characterize the disease or the response to the disease in these individuals. These patterns or features can be as varied as molecular patterns or features in medical images. This level of data analysis and integration can only be achieved through the use of AI. The book is divided into three parts starting with a section on the use of artificial intelligence for screening, diagnosis and monitoring in precision oncology. The second part: Artificial intelligence and Omics in precision oncology, highlights the use of AI and epigenetics, metabolomics, microbiomics in precision oncology. The third part covers artificial intelligence in cancer therapy and its clinical applications. It also highlights the use of AI tools for risk prediction, early detection, diagnosis and accurate prognosis. This book, written by experts in the field from academia and industry, will appeal to cancer researchers, clinical oncologists, pathologists, medical students, academic teaching staff and medical residents interested in cancer research as well as those specialising as clinical oncologists.

Artificial Intelligence in Cancer by Smaranda Belciug, 2020 Artificial Intelligence in Cancer: Diagnostic to Tailored Treatment provides theoretical concepts and practical techniques of AI and its applications in cancer management, building a roadmap on how to use AI in cancer at different stages of healthcare. It discusses topics such as the impactful role of AI during diagnosis and how it can support clinicians to make better decisions, AI tools to help pathologists identify exact types of cancer, how AI supports tumor profiling and can assist surgeons, and the gains in precision for oncologists using AI tools. Additionally, it provides information on AI used for survival and remission/recurrence analysis. The book is a valuable source for bioinformaticians, cancer researchers, oncologists, clinicians and members of the biomedical field who want to understand the promising field of AI applications in cancer management.

Artificial Intelligence in Radiation Oncology by Seong K. Mun; sonja Dieterich, 2022 The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy by Ganji Purnachandra Nagaraju; Venkatesan Amouda et al, 2023 Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research as well as computational approaches for drug discovery and repurposing for cancer therapy. The book also provides detailed descriptions about target molecules, pathways, and their inhibitors for easy understanding and applicability. The book discusses tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers, and transcriptome; those are discussed in the context of different cancer types, such as colon, pancreatic, glioblastoma, endometrial, and retinoblastoma, among others. This book is a valuable resource for researchers, students, and members of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor the treatment for cancer patients.

Machine Learning and Artificial Intelligence in Radiation Oncology by Barry S. Rosenstein; Tim Rattay; John Kang, 2023 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.

Overcoming Breast Cancer Therapy Resistance: From Mechanisms to Precision and AI-Powered Approaches by Zodwa Dlamini, 2024 This book addresses the pressing problem of drug resistance in the treatment of breast cancer. It provides a comprehensive understanding of the complex mechanisms underlying drug resistance and presents the latest advances in therapeutic approaches to overcome this enormous challenge. The book begins with an introduction to the biology of breast cancer, treatment modalities and the mechanisms of drug resistance. This includes the principles of drug resistance in targeted, immune-, endocrine and chemotherapy. In the following sections, the authors present innovative strategies to overcome drug resistance and new research directions, among others the role of the microbiome in drug resistance, molecular profiling of patients for personalized medical care, and new drug delivery systems. Finally, the book concludes with a discussion of the differences in the prevalence and treatment success of breast cancer between high- and low-income countries. This book is an informative resource for students, researchers, and clinicians in the field of breast cancer research and oncology in general.