Clinical Feasibility of Commercial Artificial Intelligence-Based Auto Contouring of Target Volumes and Organs-at-Risk in Breast Cancer Patients

Abstract

<h3>Purpose/Objective(s)</h3> Manual clinical target volume (CTV) and organ at-risk (OAR) segmentation for planning remains a significant component of physician workload. In addition, inter-physician segmentation variability can introduce uncertainty in planning process. While current atlas-based auto-segmentation has proven to be useful for contouring, it typically requires significant upfront time investment and is physician dependent. Recently, artificial-intelligence-based contouring have been made commercially available. These systems are physician-independent, and rely on machine learning rather than a user-selected atlas of patient datasets. In this study, we evaluated the potential benefits of a commercially available artificial intelligence-based auto contouring (AI-AC) system by comparing AI-AC generated and manually delineated contours for breast patients. <h3>Materials/Methods</h3> CTVs (ipsilateral breast) and OARs (contralateral breast, heart, lungs, esophagus, and spinal cord) were manually contouring (MC) on planning computed tomography scans of 10 breast cancer patients (5 patients received breast-conserving surgery). Commercial AI auto-contouring software (AI-Rad Companion Organs RT) was then used to generate the same target and risk volumes. Volumes were compared using quantitative metrics, including the Dice Similarity Coefficient (DSC), and mean distance agreement (MDA). <h3>Results</h3> Correlation between the AI-AC and MC generated volumes was strong for the evaluated OARs, with a mean DSC higher than 0.90 for all OARs except the esophagus (mean DSC 0.83 ± 0.09)). In addition, Comparison of CTV volumes also showed excellent results, with a mean DSC higher than 0.93. For the OARs, mean DSC values ranged from 0.98 ± 0.01 to 0.83 ± 0.09, while the mean MDA ranged from 0.68 ± 0.3 to 0.84 +0.54 mm to mm. [Similar results were achieved for the CTV, with the mean DSC ranging from 0.93 ± 0.03 to 0.94 ± 0.06 to 0.93, and mean MDA from 1.71 ± 0.91 mm to 1.76 ± 0.68 mm. AI-AC generated volumes were produced in approximately 2 min, while MC of the same volumes takes the physician an estimated 30 min. <h3>Conclusion</h3> The feasibility of commercial AI-AC in breast RT planning was demonstrated. Immediately on activation, the AI-AC system was able to produce volumes showing significant agreement with physician-generated volumes. While AI-AC-generated volumes may require minor edits by the physician, clinical efficiency is substantially improved. Furthermore, initial volumes created by AI-AC are physician-independent, and may help to improve contour consistency in clinic compared to strictly manual approaches.