In this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject\'s whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown.
The framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level.
The proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.
在这种情况下,本研究提出了一个框架,该框架能够使用受试者的全身18F-FDGSUV图像和正常对照数据库构建个体代谢异常网络.在肺癌患者中评估了开发的框架,患有Covid-19疾病后出院的人,还有一个原因不明的消化道出血.
该框架可以在系统和器官水平上成功地捕获这些患者与健康受试者的偏差。改变的网络边缘的强度揭示了器官之间异常的代谢连接。观察到网络节点的总体偏差与器官SUV测量高度相关。因此,葡萄糖代谢的分子连通性在单个受试者水平进行表征.
所提出的框架代表了朝着使用PET成像从系统角度识别代谢功能障碍迈出的重要一步。更好地了解本研究中确定的区域间联系的潜在生物学机制和生理解释,需要进一步研究。