GE HealthCare Introduces Innovations to Improve Radiation Therapy Efficiency and… – Press Release

  • Showcasing Intelligent RT, Auto Segmentation and an updated MR Radiation Therapy Suite, designed to optimize the radiation oncology care pathway and help reduce the critical time between patient intake and first treatment dose

GE HealthCare GEHC is presenting three new global innovations – Intelligent Radiation Therapy (iRT), Auto Segmentation, and an updated Magnetic Resonance (MR) Radiation Therapy Suite (AIR Open Coil Suite) – that underscore the company’s commitment to enhancing the radiation oncology care pathway. Each of these novel solutions, which will be showcased at the European Society for Therapeutic Radiology and Oncology (ESTRO) 2023 Congress, were designed to help empower clinicians to provide the highest level of personalized care by enabling access to the diagnostics, technologies and data needed to make informed and confident decisions, with the hope of helping patients live their healthiest lives.

Nearly two-thirds of all cancer patients receive radiation therapy (RT), a type of treatment that can either be given alone or in combination with surgery and/or chemotherapy.1 However, it can take weeks for a patient to go from a cancer diagnosis to treatment – a delay that may have a significant impact on patient outcomes. According to research published by the British Medical Journal (BMJ), the mortality risk increases 6-13% for every month a diagnosis is delayed.2 GE HealthCare helps address this challenge by improving and accelerating the radiation oncology care pathway – a strategic initiative that focuses on broadening the company’s imaging portfolio, accelerating workflows through enabling seamless interoperability between radiation therapy systems, and empowering clinicians with the ability to choose the best technologies for their patients through an open, vendor-agnostic ecosystem.

The introduction of the following innovations is a testament to the company’s commitment to creating a best-in-class ecosystem that enables…