Automatic detection of patient identification and positioning errors in radiotherapy treatment using 3D setup images

The success of modern radiotherapy treatment depends on the correct alignment of the radiation beams with the target region in the patient. In the conventional paradigm of image-guided radiation therapy, 2D or 3D setup images are taken immediately prior to treatment and are used by radiation therapy technologists to localize the patient to the same position as defined from the reference planning CT dataset. However, numerous reports in the literature have described errors during this step, which have led to incorrect treatments and potentially significant clinical harm to patients. In addition, reported errors likely underestimate the true error rate, as many errors may pass by undetected or are simply not reported. The human factor has been shown to play a large role in these errors, where the setup and planning CT imaging registration is not interpreted or performed correctly as per standard practice.The hypothesis of the proposed study was to develop a workflow that can algorithmically compare 3D setup and planning CT imaging using image similarity metrics. The proposed system, intended to work in an automated and real-time fashion immediately prior to radiotherapy delivery, has the potential to act as a robust second-check safety interlock to prevent any identification or misalignment errors from reaching the patient. As no additional equipment is required in the treatment room or for patient setup, this system adds virtually no additional complexity, time, or cost to the treatment process. It can be applicable to countries around the world and is particularly relevant for developing nations, where higher error rates have been reported. We examined both 3D cone beam CT and 3D megavoltage CT images covering the head-and-neck, spine, and pelvis regions. Workflows were developed for detecting errors in patient identification and patient misalignment, including various pre-processing and feature selection steps. Classification models were trained and evaluated by comparing misclassification errors, ROC curves, likelihood ratios, and more. Our system can achieve high accuracy and sensitivity/specificity rates for error detection in both patient identification and alignment scenarios, and there are many possible avenues to further test and improve the system’s robustness