Frontiers in Oncology is delighted to present the Methods in series of article collections. Methods in Cancer Imaging and Image-directed Interventions will publish high-quality methodical studies on key topics in the field. It aims to highlight recent advances in the field, whilst emphasizing important directions and new possibilities for future inquiries. The Methods in Cancer Imaging and Image-directed Interventions collection aims to highlight the latest experimental techniques and methods used to investigate fundamental questions in Cancer Imaging and Image-directed Interventions. Review Articles or Opinion Articles on methodologies or applications including the advantages and limitations of each are welcome. This Research Topic includes technologies and up-to-date methods which help aim to help advance science.

This book constitutes the refereed proceedings of the 15th International Conference on Hybrid Artificial Intelligent Systems, HAIS 2020, held in Gijón, Spain, in November 2020. The 65 regular papers presented in this book were carefully reviewed and selected from 106 submissions. The papers are grouped into these topics: advanced data processing and visualization techniques; bio-inspired models and optimization; learning algorithms; data mining, knowledge discovery and big data; and hybrid artificial intelligence applications.

In this issue of Urologic Clinics of North America, guest editor Dr. Andrew J. Hung brings his considerable expertise to the topic of Artificial Intelligence in Urology. Alongside technological advancements in artificial intelligence (AI), applications of AI in urology have grown tremendously over the last few years. This special issue highlights areas of particular interest, such as radiomics, pathomics, genomics, and surgery. Top experts in the field cover the current status and also preview future applications, aimed at improved patient outcomes. - Contains 13 relevant, practice-oriented topics including radiomics, pathomics, and surgical AI; genomics and AI: prostate cancer and renal cell carcinoma; pediatric urology and AI; bladder cancer and AI; AI in urology: big data sets; and more. - Provides in-depth clinical reviews on artificial intelligence in urology, offering actionable insights for clinical practice. - Presents the latest information on this timely, focused topic under the leadership of experienced editors in the field. Authors synthesize and distill the latest research and practice guidelines to create clinically significant, topic-based reviews.

This book presents artificial intelligence applications that may help in detecting disease, defining tissue characterization (benign vs malignant), staging and correlation with molecular biomarkers. Originally positioned as a means for noninvasive molecular phenotyping and quantification in the 1970s, PET's technological improvements in the 2000s generated renewed interest in quantification, which has grown over the last five years. This progress is parallel with the development of Artificial intelligence (AI) systems for Oncology which aim at providing the best possible treatment to patients suffering from lung, breast, brain, prostate, liver and other types of cancer. The chapters provide an overview of the use of AI in PET/CT imaging for various types of cancer, and it will be an invaluable tool especially for nuclear medicine physicians and oncologists.