Machine Learning With Radiation Oncology Big Data

• Hoofdauteur: Lei Xing (auth)
• Andere auteurs: Issam El Naqa (auth), Jun Deng (auth)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Frontiers Media SA 2019
• Reeks: Frontiers Research Topics
• Onderwerpen: Medicine; deep learning; precision medicine; Radiation Oncology; big data; machine learning; artificial intelligence; personalized medicine
• Online toegang: DOAB: download the publication; DOAB: description of the publication

Radiation oncology is uniquely positioned to harness the power of big data as vast amounts of data are generated at an unprecedented pace for individual patients in imaging studies and radiation treatments worldwide. The big data encountered in the radiotherapy clinic may include patient demographics stored in the electronic medical record (EMR) systems, plan settings and dose volumetric information of the tumors and normal tissues generated by treatment planning systems (TPS), anatomical and functional information from diagnostic and therapeutic imaging modalities (e.g., CT, PET, MRI and kVCBCT) stored in picture archiving and communication systems (PACS), as well as the genomics, proteomics and metabolomics information derived from blood and tissue specimens. Yet, the great potential of big data in radiation oncology has not been fully exploited for the benefits of cancer patients due to a variety of technical hurdles and hardware limitations. With recent development in computer technology, there have been increasing and promising applications of machine learning algorithms involving the big data in radiation oncology. This research topic is intended to present novel technological breakthroughs and state-of-the-art developments in machine learning and data mining in radiation oncology in recent years.