Shah Veeraj P, Lakshminarayanan Pranav, Moore Joseph, Tran Phuoc T, Quon Harry, Deville Curtiland, McNutt Todd R
Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
J Appl Clin Med Phys. 2018 Jul;19(4):58-67. doi: 10.1002/acm2.12353. Epub 2018 Jun 12.
The purpose of this research is to develop effective data integrity models for contoured anatomy in a radiotherapy workflow for both real-time and retrospective analysis. Within this study, two classes of contour integrity models were developed: data driven models and contiguousness models. The data driven models aim to highlight contours which deviate from a gross set of contours from similar disease sites and encompass the following regions of interest (ROI): bladder, femoral heads, spinal cord, and rectum. The contiguousness models, which individually analyze the geometry of contours to detect possible errors, are applied across many different ROI's and are divided into two metrics: Extent and Region Growing over volume. After analysis, we found that 70% of detected bladder contours were verified as suspicious. The spinal cord and rectum models verified that 73% and 80% of contours were suspicious respectively. The contiguousness models were the most accurate models and the Region Growing model was the most accurate submodel. 100% of the detected noncontiguous contours were verified as suspicious, but in the cases of spinal cord, femoral heads, bladder, and rectum, the Region Growing model detected additional two to five suspicious contours that the Extent model failed to detect. When conducting a blind review to detect false negatives, it was found that all the data driven models failed to detect all suspicious contours. The Region Growing contiguousness model produced zero false negatives in all regions of interest other than prostate. With regards to runtime, the contiguousness via extent model took an average of 0.2 s per contour. On the other hand, the region growing method had a longer runtime which was dependent on the number of voxels in the contour. Both contiguousness models have potential for real-time use in clinical radiotherapy while the data driven models are better suited for retrospective use.
本研究的目的是为放射治疗工作流程中的轮廓解剖结构开发有效的数据完整性模型，用于实时和回顾性分析。在本研究中，开发了两类轮廓完整性模型：数据驱动模型和连续性模型。数据驱动模型旨在突出那些与来自相似疾病部位的一组总体轮廓不同的轮廓，并涵盖以下感兴趣区域（ROI）：膀胱、股骨头、脊髓和直肠。连续性模型单独分析轮廓的几何形状以检测可能的错误，应用于许多不同的ROI，并分为两个指标：范围和基于体积的区域生长。分析后，我们发现检测到的膀胱轮廓中有70%被确认为可疑。脊髓和直肠模型分别验证了73%和80%的轮廓是可疑的。连续性模型是最准确的模型，而区域生长模型是最准确的子模型。检测到的所有不连续轮廓中有100%被确认为可疑，但在脊髓、股骨头、膀胱和直肠的情况下，区域生长模型检测到另外两到五个范围模型未能检测到的可疑轮廓。在进行盲法审查以检测假阴性时，发现所有数据驱动模型都未能检测到所有可疑轮廓。除前列腺外，区域生长连续性模型在所有感兴趣区域均产生零假阴性。关于运行时间，基于范围的连续性模型平均每个轮廓需要0.2秒。另一方面，区域生长方法的运行时间更长，这取决于轮廓中的体素数量。两个连续性模型都有在临床放射治疗中实时使用的潜力，而数据驱动模型更适合回顾性使用。
