Highlights
Every year millions of cancer patients depend on radiotherapy, yet treatment success increasingly depends not only on medical expertise but also on advances in software, imaging, AI, and precision engineering.
Radiation therapy or radiotherapy uses ionizing radiation to treat cancer with minimal side effects. The most important factors affecting the outcome of radiotherapy are the tumour type, location of the tumour, and the accuracy with which the appropriate radiation dose is delivered. It is true that higher doses of radiation can produce better tumour control. But collateral damage due to the accidental injury to healthy cells and organs located near or on the path of the radiation beam is a limiting factor. More than 60% of all cancer patients may need radiotherapy as the only treatment or along with surgery or chemotherapy.
Radiotherapy has evolved significantly in recent times. To avoid the collateral damage to healthy tissues and organs caused by the exposure to high doses of radiation, the radiation beam can be shaped to match the geometry of the tumour. Multileaf Collimators (MLCs) were developed to shape the beam so that it targets only the tumour. This is called conformal radiotherapy. It is also possible to offer Intensity Modulated Radiotherapy (IMRT). MLCs split the beam into beamlets that will vary the intensity of the radiation dose, matching the shape of the tumor. MLCs thereby make it possible to increase the dose only to the tumour and improve treatment outcomes.
Radiation therapy is software-intensive. This enables reliable and interoperable access to patient information and many advanced treatment techniques, improving efficiency and accuracy to avoid human errors. But new technology brings new challenges. In therapy planning, accurate structure contouring of organs of interest (OOI) and lymph nodes is a vital step. This imposes significant pressure on staff responsible for consistent contouring results, when they have to handle a large number of patients. Artificial intelligence can help automate tasks such as contouring, thereby alleviating workload and operator fatigue. Interoperability in radiology ensures seamless sharing and exchange of patient information and images. It plays a pivotal role in enhancing healthcare delivery and outcomes by fostering increased efficiency, accuracy, and collaboration among healthcare professionals.
In radiation oncology, the objective is not just treating the tumour, but to target it with sub-millimetre accuracy while protecting the organs and tissues that matter to ensure the patient’s quality of life. Engineering excellence offers precision and protection. Diagnostic imaging enables locating the tumour, identifying methods to protect vital organs from unwanted radiation, and ensuring that patient movement due to breathing and heartbeats is considered during radiation delivery. These factors must be taken into consideration while designing the therapy system. To put it briefly, it is important to have a good machine, but a precision ecosystem is vital to curing cancer while preserving quality of life. This is where an encompassing engineering perspective is needed to consider biology, nuclear physics, and mechatronic design orchestrated by software.
Protecting vital organs is the core focus of radiotherapy, which has driven technology initiatives in this area. Traditional X-ray (photon) radiation damages tissue both before and after hitting the tumour. A beam of protons destroys cancer cells in proton therapy. Protons have a physical property called the Bragg Peak. This means the beam travels to a specific depth, delivers most of its radiation directly inside the tumour, and then stops completely without leaving a significant exit. Proton therapy greatly reduces radiation exposure to vital organs and normal cells near the cancer site. The introduction of AI in radiotherapy can enhance the efficiency of the workflow and its reproducibility.
AI can enhance proton therapy by automating complex planning, accelerating dose calculations, and enabling real-time adjustments to account for daily bodily changes. These changes can indicate the variation in tumor size and also body movement due to breathing and heartbeats.
Radiation therapy faces numerous challenges today, including a rising number of patients, complex workflows, software integration challenges, AI interventions, and therefore the need for comprehensive training and technical support to ensure efficient delivery of radiotherapy. Accessibility barriers create significant gaps in patient care today. Addressing these technical challenges in radiotherapy requires expertise in motion management, image processing, and artificial intelligence integration to spare healthy tissue while destroying tumours.
Radiotherapy solutions that suit patients’ needs are critical for a successful outcome. Hence, an all-encompassing approach that links different domains of technology is the need of the hour for redefining radiotherapy of tomorrow.