Streamlining Thoracic Radiotherapy Quality assurance: One-Class Classification for Automated OAR Contour Assessment.

IF 2.8 4区 医学 Q3 ONCOLOGY
Technology in Cancer Research & Treatment Pub Date : 2025-01-01 Epub Date: 2025-05-22 DOI:10.1177/15330338251345895
Yihao Zhao, Cuiyun Yuan, Ying Liang, Yang Li, Chunxia Li, Man Zhao, Jun Hu, Ningze Zhong, Wei Liu, Chenbin Liu
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引用次数: 0

Abstract

PurposeAutomating quality assurance (QA) for contours generated by automatic algorithms is critical in radiotherapy treatment planning. Manual QA is tedious, time-consuming, and prone to subjective experiences. Automatic segmentation reduces physician workload and improves consistency. However, an effective QA process for these automatic contours remains an unmet need in clinical practice.Materials and MethodsThe patient data used in this study was derived from the AAPM Thoracic Auto-Segmentation Challenge dataset, including left and right lungs, heart, esophagus, and spinal cord. Two groups of organ-at-risk (OAR) were generated. A ResNet-152 network was used as a feature extractor, and a one-class support vector machine (OC-SVM) was employed to classify contours as 'high' or 'low' quality. To evaluate the generalizability, we generated low-quality contours using translation and resizing techniques and assessed correlations between detection limits and metrics such as volume, Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD).ResultsThe proposed OC-SVM model outperformed binary classifiers n metrics such as balanced accuracy and area under the receiver operating characteristic curve (AUC) . It demonstrated superior performance in detecting various types of contour errors while maintaining high interpretability. Strong correlations were observed between detection limits and contour metrics.ConclusionOur proposed model integrates an attention mechanism with a one-class classification framework to automate QA for OAR delineations. This approach effectively detects diverse types of contour errors with high accuracy, significantly reducing the burden on physicians during radiotherapy planning.

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简化胸部放射治疗质量保证:自动OAR轮廓评估的一级分类。
目的对自动算法生成的轮廓线进行自动质量保证(QA)是放疗治疗计划的关键。手动QA是乏味的,耗时的,并且倾向于主观体验。自动分割减少了医生的工作量,提高了一致性。然而,在临床实践中,这些自动轮廓的有效QA过程仍然是一个未满足的需求。材料和方法本研究中使用的患者数据来自AAPM胸腔自动分割挑战数据集,包括左、右肺、心脏、食道和脊髓。产生两组器官危险组(OAR)。使用ResNet-152网络作为特征提取器,并使用一类支持向量机(OC-SVM)对轮廓进行“高”或“低”质量分类。为了评估可泛化性,我们使用平移和调整大小技术生成了低质量轮廓,并评估了检测限与诸如体积、Dice相似系数(DSC)、95% Hausdorff距离(HD95)和平均表面距离(MSD)等指标之间的相关性。结果OC-SVM模型在平衡精度和接收者工作特征曲线下面积(AUC)等指标上优于二元分类器。它在检测各种类型的轮廓误差方面表现出优异的性能,同时保持了较高的可解释性。检出限与轮廓指标之间存在很强的相关性。我们提出的模型将注意力机制与单类分类框架相结合,实现了对桨叶描述的自动化QA。该方法有效地检测了各种类型的轮廓误差,精度高,大大减轻了医生在放疗计划中的负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.40
自引率
0.00%
发文量
202
审稿时长
2 months
期刊介绍: Technology in Cancer Research & Treatment (TCRT) is a JCR-ranked, broad-spectrum, open access, peer-reviewed publication whose aim is to provide researchers and clinicians with a platform to share and discuss developments in the prevention, diagnosis, treatment, and monitoring of cancer.
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