An Automated Treatment Plan Alert System to Safeguard Cancer Treatments in Radiation Therapy

In radiation oncology, the intricate process of delivering radiation to a patient is detailed by the patient’s treatment plan, which is data describing the geometry, construction and strength of the radiation machine and the radiation beam it emits. The patient’s life depends upon the accuracy of the treatment plan, which is left in the hands of the vendor-specific software automatically generating the plan after an initial patient consultation and planning with a medical professional. However, corrupted and erroneous treatment plan data have previously resulted in severe patient harm when errors go undetected and radiation proceeds. The aim of this paper is to develop an automatic error-checking system to prevent the accidental delivery of radiation treatment to an area of the human body (i.e., the treatment site) that differs from the plan’s documented intended site. To this end, we develop a method for structuring treatment plan data in order to feed machine-learning (ML) classifiers and predict a plan’s treatment site. In practice, a warning may be raised if the prediction disagrees with the documented intended site.