Luximon Dishane C, Neylon John, Lamb James M
Department of Radiation Oncology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
Phys Imaging Radiat Oncol. 2023 Mar 5;25:100427. doi: 10.1016/j.phro.2023.100427. eCollection 2023 Jan.
Currently, there is no robust indicator within the Cone-Beam Computed Tomography (CBCT) DICOM headers as to which anatomical region is present on the scan. This can be a predicament to CBCT-based algorithms trained on specific body regions, such as auto-segmentation and radiomics tools used in the radiotherapy workflow. We propose an anatomical region labeling (ARL) algorithm to classify CBCT scans into four distinct regions: head & neck, thoracic-abdominal, pelvis, and extremity.
Algorithm training and testing was performed on 3,802 CBCT scans from 596 patients treated at our radiotherapy center. The ARL model, which consists of a convolutional neural network, makes use of a single CBCT coronal slice to output a probability of occurrence for each of the four classes. ARL was evaluated on the test dataset composed of 1,090 scans and compared to a support vector machine (SVM) model. ARL was also used to label CBCT treatment scans for 22 consecutive days as part of a proof-of-concept implementation. A validation study was performed on the first 100 unique patient scans to evaluate the functionality of the tool in the clinical setting.
ARL achieved an overall accuracy of 99.2% on the test dataset, outperforming the SVM (91.5% accuracy). Our validation study has shown strong agreement between the human annotations and ARL predictions, with accuracies of 99.0% for all four regions.
The high classification accuracy demonstrated by ARL suggests that it may be employed as a pre-processing step for site-specific, CBCT-based radiotherapy tools.
目前，在锥束计算机断层扫描（CBCT）的DICOM头文件中，没有可靠的指标来表明扫描上存在哪个解剖区域。这对于在特定身体区域训练的基于CBCT的算法（如放射治疗工作流程中使用的自动分割和放射组学工具）来说可能是一个难题。我们提出一种解剖区域标记（ARL）算法，将CBCT扫描分类为四个不同区域：头颈部、胸腹、骨盆和四肢。
在我们放疗中心接受治疗的596例患者的3802次CBCT扫描上进行算法训练和测试。由卷积神经网络组成的ARL模型利用单个CBCT冠状切片输出四个类别中每一个类别的出现概率。在由1090次扫描组成的测试数据集上对ARL进行评估，并与支持向量机（SVM）模型进行比较。作为概念验证实施的一部分，ARL还连续22天用于标记CBCT治疗扫描。对前100例独特患者扫描进行了验证研究，以评估该工具在临床环境中的功能。
ARL在测试数据集上的总体准确率达到99.2%，优于SVM（准确率91.5%）。我们的验证研究表明，人工标注与ARL预测之间具有高度一致性，所有四个区域的准确率均为99.0%。
ARL显示出的高分类准确率表明，它可作为基于CBCT的特定部位放疗工具的预处理步骤。
