UCLA

Although safety procedures are in place within the radiation therapy (RT) workflow, incidents are still occurring due to human errors. To enhance patient safety, it is critical to identify and consolidate the vulnerabilities present within the various processes performed prior and during treatment. One such domain is within the Cone Beam Computed Tomography (CBCT)-guided RT workflow where there is currently no built-in system to check for errors in the registration of the simulation Computed Tomography (simCT) to the setup CBCT performed during the patient positioning step prior to radiation beam delivery. This lack of safeguards poses a risk to the patient as an incorrect registration may go undetected, leading to a compromised patient setup and treatment. The overall objective of the proposed work is to develop a deep learning-based error detection algorithm (EDA) which can serve as a secondary safety check to the radiation therapist while also helping to consolidate the robustness of CBCT-guided radiotherapy treatments. Additionally, this work explores the feasibility of a fully-unsupervised anomaly detection framework (ADF), based on a CBCT inpainting technique using a variational autoencoder, which would highlight anomalies for human review during regular physics quality assurance chart checks.

Initial results show that the EDA has a strong error-catching ability with areas under the receiver operating characteristic (ROC) curves of at least 99.2% when tested on simulated translational errors. When assessed against expert observers in a qualitative assessment of patient setup registrations, the EDA’s predictions achieved statistically significant correlations to the observer scores. Additionally, during a retrospective error search on a multi-institutional dataset of 17,612 registrations, the EDA successfully flagged the three known patient-setup incidents, and additionally identified four previously unreported incidents, proving its effectiveness on real-life cases. Those results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of CBCT-guided RT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction. The ADF also demonstrated promising error detection ability when applied to a test dataset containing real patient setup incidents and simulated translational errors, with an area under the ROC curve of 98.1%.

The results described in this work validate the clinical utility and strong error-catching ability of both the EDA and the ADF when applied to real-world cases. We demonstrated that EDA and ADF can facilitate bulk image reviews, which can be useful for incident learning and can also expedite regular quality assurance chart checks performed by physicists or physicians. Additionally, if applied in real-time, EDA can consolidate the safety of CBCT-guided radiotherapy by serving as a secondary safety check to the therapist, thereby minimizing the risk of gross patient setup errors. Whether used prospectively or retrospectively, we believe that the proposed tools can add substantial value to the safety aspect of radiotherapy treatments, especially in low-middle income communities where the lack of workforce and safeguards often translates into a higher risk of treatment incidents.