NSCLC中SBRT的全自动危险区测定

T. Fechter, J. Dolz, A. Chirindel, Matthias Schlachter, M. Carles, S. Adebahr, M. Mix, U. Nestle
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引用次数: 2

摘要

肺癌是全世界癌症死亡的主要原因。最常见的肺癌类型是非小细胞肺癌(NSCLC)。立体定向体放射治疗(SBRT)已成为外周I期非小细胞肺癌患者手术治疗的良好选择,显示出良好的肿瘤控制和低毒性。由于与几个有危险的关键器官的空间关系,中心位置病变的SBRT与更严重的毒性相关,需要改变剂量应用和分离方法,目前正在临床试验中进行评估。因此,需要将肺肿瘤分为中枢性和外周性。在这项工作中,我们提出了一种新的、高度通用的、多模态的肿瘤分类工具,它不需要用户交互。此外,该工具还可以自动分割气管、近端支气管树、纵隔、总靶容积和内靶容积。在19个不同图像模式的案例中对所提出的工作进行了评估,评估了分割质量和分类精度。实验结果表明,该方法分割质量好,分类准确率达95%。这些结果表明,在临床试验中使用拟议的工具来协助临床医生的工作,并加快非小细胞肺癌患者治疗的工作流程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fully Automatic Danger Zone Determination for SBRT in NSCLC
Lung cancer is the major cause of cancer death worldwide. The most common form of lung cancer is non-small cell lung cancer(NSCLC). Stereotactic body radiation therapy (SBRT) has emerged as a good alternative to surgery in patients with peripheralstage I NSCLC, demonstrating favorable tumor control and low toxicity. Due to spatial relationship to several critical organs atrisk, SBRT of centrally located lesions is associated with more severe toxicity and requires modification in dose application andfractionation, which is currently evaluated in clinical trials. Therefore a classification of lung tumors into central or peripheralis required. In this work we present a novel, highly versatile, mulitmodality tool for tumor classification which requires no userinteraction. Furthermore the tool can automatically segment the trachea, proximal bronchial tree, mediastinum, gross target volumeand internal target volume. The proposed work is evaluated on 19 cases with different image modalities assessing segmentationquality as well as classification accuracy. Experiments showed a good segmentation quality and a classification accuracy of 95 %.These results suggest the use of the proposed tool for clinical trials to assist clinicians in their work and to fasten up the workflowin NSCLC patients treatment.
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