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. Eventually, greater exploitation of radiation oncology big data could lead to more personalized radiotherapy worldwide. Potential topics include, but are not limited to:

Radiomics and quantitative imaging

• Knowledge-based treatment planning
• Treatment response prediction via machine learning
• Clinical decision support via machine learning
• Comparative effectiveness research in radiation oncology
• Bioinformatics for improved quality of care
• Motion compensation and correction via machine learning
• Automated image registration and contouring
• Radiogenomics
• TCP and NTCP modeling
• Cancer registries and classification
• Tracking big organ dose data for patient safety in radiation therapy
• Machine learning models for early cancer prediction and prevention
• Natural language processing of EMR data

Medical Physicists will contribute to maintaining and improving the quality, safety and cost-effectiveness of healthcare services through patient-oriented activities requiring expert action, involvement or advice regarding the specification, selection, acceptance testing, commissioning, quality assurance/control and optimised clinical use of medical devices and regarding patient risks and protection from associated physical agents (e.g. x-rays, electromagnetic fields, laser light, radionuclides) including the prevention of unintended or accidental exposures; all activities will be based on current best evidence or own scientific research when the available evidence is not sufficient. Medical physics is also called biomedical physics, medical biophysics or applied physics in medicine is, generally speaking, the application of physics concepts, theories and methods to medicine or healthcare. Medical physics departments may be found in hospitals or universities. Medical physics has a much wider scope and may include research in any applications of physics to medicine from the study of biomolecular structure to microscopy and nanomedicine. Insights in Medical Physics is an open access scientifc journal which publishes peer reviewed articles in the area of medical physics. The journal publish articles with theoretical and experimental contributions for publication covering all dimensions of medical physics such as application of radiation physics with relation to radiation therapies, nuclear medicine related physics applications, medical imaging, signal processing and signal output analysis for medical devices, application of physics in biomedical devise development, computer aided image analysis etc. Articles are welcome in the mentioned discipline along with other associated areas of the subject.

Authors are welcome to submit their manuscripts. Submit manuscript at https://www.scholarscentral.org/submission/insights-medical-physics.html (or) as an e-mail attachment to medicalsci@scholarlymed.com or medicalsci@medicinaljournals.com

Media contact

Eliza Miller

Managing Editor

Journal of Medical Physics and Applied Sciences