Artificial Intelligence And Imaging For Oncology

Artificial Intelligence and Imaging for Oncology

Recent progress in artificial intelligence (AI) and advanced imaging techniques have brought about exciting diagnostic and therapeutic opportunities in oncology. More and more state-of-the-art CT, PET, and MRI imaging techniques are being applied to tumor imaging, and new interdisciplinary field such as MR-LINAC is booming. We feel there's an urgent need to organize a Research Topic to highlight the application of AI and imaging for various oncology branches such as radiation therapy, as we are witnessing an accelerated integration of imaging and therapy to improve the care of cancer patients. Imaging has also been the backbone of the oncology world. It is used for diagnosis as well as planning. MRI has a superior soft tissue contrast but lacks the electron density information for the dose calculation, alternative approaches use synthetic CT for dose calculation. The development of the MRI-guided system and online adaptation can be considered as the bridge between many specialties, starting from learning about MRI safety by the therapy physicist to the use of AI for robust planning. Active research is done to improve the images (advanced MRI techniques, Nuclear medicine and PET-MRI, synthetic images, CBCT for dose calculation) for clinical workflows. However, all these topics have a lot of room for exploration.

Machine Learning in Radiation Oncology

​This book provides a complete overview of the role of machine learning in radiation oncology and medical physics, covering basic theory, methods, and a variety of applications in medical physics and radiotherapy. An introductory section explains machine learning, reviews supervised and unsupervised learning methods, discusses performance evaluation, and summarizes potential applications in radiation oncology. Detailed individual sections are then devoted to the use of machine learning in quality assurance; computer-aided detection, including treatment planning and contouring; image-guided radiotherapy; respiratory motion management; and treatment response modeling and outcome prediction. The book will be invaluable for students and residents in medical physics and radiation oncology and will also appeal to more experienced practitioners and researchers and members of applied machine learning communities.

Contouring in Head and Neck Cancer