Results of an Artificial Intelligence-Based Image Review System to Detect Patient Misalignment Errors in a Multi-institutional Database of Cone Beam Computed Tomography-Guided Radiation Therapy

Dishane C Luximon; Jack Neylon; Timothy Ritter; Nzhde Agazaryan; John V Hegde; Michael L Steinberg; Daniel A Low; James M Lamb

doi:10.1016/j.ijrobp.2024.02.065

Results of an Artificial Intelligence-Based Image Review System to Detect Patient Misalignment Errors in a Multi-institutional Database of Cone Beam Computed Tomography-Guided Radiation Therapy

Int J Radiat Oncol Biol Phys. 2024 Mar 12:S0360-3016(24)00392-4. doi: 10.1016/j.ijrobp.2024.02.065. Online ahead of print.

Authors

Dishane C Luximon¹, Jack Neylon², Timothy Ritter³, Nzhde Agazaryan², John V Hegde², Michael L Steinberg², Daniel A Low², James M Lamb²

Affiliations

¹ Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California. Electronic address: dluximon@mednet.ucla.edu.
² Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, California.
³ Department of Medical Physics, Virginia Commonwealth University, Richmond, Virginia.

PMID: 38485098
DOI: 10.1016/j.ijrobp.2024.02.065

Abstract

Purpose: Present knowledge of patient setup and alignment errors in image guided radiation therapy (IGRT) relies on voluntary reporting, which is thought to underestimate error frequencies. A manual retrospective patient-setup misalignment error search is infeasible owing to the bulk of cases to be reviewed. We applied a deep learning-based misalignment error detection algorithm (EDA) to perform a fully automated retrospective error search of clinical IGRT databases and determine an absolute gross patient misalignment error rate.

Methods and materials: The EDA was developed to analyze the registration between planning scans and pretreatment cone beam computed tomography scans, outputting a misalignment score ranging from 0 (most unlikely) to 1 (most likely). The algorithm was trained using simulated translational errors on a data set obtained from 680 patients treated at 2 radiation therapy clinics between 2017 and 2022. A receiver operating characteristic analysis was performed to obtain target thresholds. DICOM Query and Retrieval software was integrated with the EDA to interact with the clinical database and fully automate data retrieval and analysis during a retrospective error search from 2016 to 2017 and from 2021 to 2022 for the 2 institutions, respectively. Registrations were flagged for human review using both a hard-thresholding method and a prediction trending analysis over each individual patient's treatment course. Flagged registrations were manually reviewed and categorized as errors (>1 cm misalignment at the target) or nonerrors.

Results: A total of 17,612 registrations were analyzed by the EDA, resulting in 7.7% flagged events. Three previously reported errors were successfully flagged by the EDA, and 4 previously unreported vertebral body misalignment errors were discovered during case reviews. False positive cases often displayed substantial image artifacts, patient rotation, and soft tissue anatomy changes.

Conclusions: Our results validated the clinical utility of the EDA for bulk image reviews and highlighted the reliability and safety of IGRT, with an absolute gross patient misalignment error rate of 0.04% ± 0.02% per delivered fraction.