Bone metastases are one of the most dangerous consequences of breast cancer. Early diagnosis and treatment would slow down the development of the disease and increase the survival rates of patients. Bone micro-vasculature is believed to play a major role in the development of bone metastases. It could be used for both diagnosis and as a therapeutic target. Synchrotron radiation micro-computed tomography (SR-µCT) with a contrast agent of blood vessels has been used to analyze the bone vasculature both in healthy and in metastatic bone. However, few studies have investigated the local features of blood vessels around metastases so far. For this purpose, the metastases first need to be automatically segmented. This is a challenging task, however, since the metastases do not contribute a specific contrast to the three-dimensional (3D) SR-µCT images. Here, we propose a new method for the simultaneous segmentation of bone, blood vessels, and metastases from contrast enhanced 3D SR-µCT images based on the nnU-Net architecture. In this study, we showed that only minimal training data was required to achieve a high quality of segmentation. The proposed method allowed for the automatic segmentation of metastases and provided an improved segmentation of bone and blood vessels compared to previous methods while being much more efficient to apply once trained. Further, the automatic segmentation allowed for the measurement of vascular metastases interdistance and to restrict measurements to volumes of interest around the metastases. Finally, we quantitatively analyzed blood vessel parameters locally around metastases. This allowed for the demonstration that a combined anti-angiogenic treatment significantly decreased the volume and thickness of blood vessels close to metastases. The proposed method showed the capacity of the method to reveal new aspects of the blood vessel structure interaction with metastases. This could be further used to both define new targets for precocious detection of metastases as well as to study the kinetics of metastasis development in bone and the action of drugs on this process.
骨转移是乳腺癌最危险的后果之一。早期诊断和治疗可延缓疾病进展并提高患者生存率。骨微血管系统被认为在骨转移的发展中起主要作用，既可用于诊断也可作为治疗靶点。采用血管造影剂的同步辐射显微计算机断层扫描技术已用于分析健康骨骼和转移性骨骼的血管系统。然而，目前对转移灶周围血管局部特征的研究较少。为此，首先需要对转移灶进行自动分割。但由于转移灶在三维SR-µCT图像中不产生特定对比度，这成为具有挑战性的任务。本研究基于nnU-Net架构，提出了一种从增强三维SR-µCT图像中同步分割骨骼、血管和转移灶的新方法。结果表明，仅需少量训练数据即可实现高质量分割。该方法不仅能自动分割转移灶，相比先前方法还改善了骨骼和血管的分割效果，且训练后应用效率显著提升。通过自动分割实现了血管-转移灶间距测量，并将测量范围限定在转移灶周围感兴趣区域。最后，我们对转移灶周围血管参数进行了局部定量分析，证实联合抗血管生成治疗能显著降低转移灶附近血管的体积和厚度。该方法展现了揭示血管结构与转移灶相互作用新方面的能力，未来可用于确定转移灶早期检测的新靶标，并研究骨转移发展动力学及药物对该过程的作用机制。
肿瘤(癌症)患者之家