An Automated Treatment Planning Framework for Spinal Radiation Therapy and Vertebral-Level Second Check
Tucker Netherton, Callistus Nguyen, Carlos Cárdenas, Caroline Chung, Ann H. Klopp, Lauren E. Colbert, Dong Joo Rhee, Christine B. Peterson, Rebecca M. Howell, Peter Balter, Laurence E. Court
Abstract
PURPOSE: Complicating factors such as time pressures, anatomic variants in the spine, and similarities in adjacent vertebrae are associated with incorrect level treatments of the spine. The purpose of this work was to mitigate such challenges by fully automating the treatment planning process for diagnostic and simulation computed tomography (CT) scans. METHODS AND MATERIALS: Vertebral bodies are labeled on CT scans of any length using 2 intendent deep-learning models-mirroring 2 different experts labeling the spine. Then, a U-Net++ architecture was trained, validated, and tested to contour each vertebra (n = 220 CT scans). Features from the CT and auto-contours were input into a random forest classifier to predict whether vertebrae were correctly labeled. This classifier was trained using auto-contours from cone beam computed tomography, positron emission tomography/CT, simulation CT, and diagnostic CT images (n = 56 CT scans, 751 contours). Auto-plans were generated via scripting. Each model was combined into a framework to make a fully automated clinical tool. A retrospective planning study was conducted in which 3 radiation oncologists scored auto-plan quality on an unseen patient cohort (n = 60) on a 5-point scale. CT scans varied in scan length, presence of surgical implants, imaging protocol, and metastatic burden. RESULTS: The results showed that the uniquely designed convolutional neural networks accurately labeled and segmented vertebral bodies C1-L5 regardless of imaging protocol or metastatic burden. Mean dice-similarity coefficient was 85.0% (cervical), 90.3% (thoracic), and 93.7% (lumbar). The random forest classifier predicted mislabeling across various CT scan types with an area under the curve of 0.82. All contouring and labeling errors within treatment regions (11 of 11), including errors from patient plans with atypical anatomy (eg, T13, L6) were detected. Radiation oncologists scored 98% of simulation CT-based plans and 92% of diagnostic CT-based plans as clinically acceptable or needing minor edits for patients with typical anatomy. On average, end-to-end treatment planning time of the clinical tool was less than 8 minutes. CONCLUSIONS: This novel method to automatically verify, contour, and plan palliative spine treatments is efficient and effective across various CT scan types. Furthermore, it is the first to create a clinical tool that can automatically verify vertebral level in CT images.