Coordination Funds

复杂的、扩大的肝切除术（（e）PHx）造成术后肝功能衰竭的高风险，特别是在既存肝病的情况下。LiPerF的目标是阐明手术引起的灌注和代谢功能变化之间未知的相互作用。我们将应用系统医学方法来生成肝脏灌注和功能的多尺度模型，这将前瞻性地改善对（e）PHx患者的功能和风险分层的预测。我们的项目目标是（1）量化和改善对所有相关生理尺度下肝脏灌注变化和选定代谢功能之间关系的理解;（2）生成经验证和整合的空间分辨动力学模型，用于切除后和再生期间肝功能的稳健预测;因此（3）为临床转化为手术计划模型奠定基础，以预测个体术后肝功能及其恢复过程。我们将通过以下方式实现这些目标：整合系统生物学方法和成像技术以及多模态、纵向个体患者数据。由于该项目的范围广泛，需要来自不同领域的专家的共同努力：实验学家，临床医生，生物信息学家，建模师和数据集成专家。我们的长期愿景是将描述灌注功能相互作用的多尺度模型转化为虚拟可视化工具。这将使一个精确的预测肝再生和手术后的发病率和死亡率时，实际上已经计划手术。

Complex, extended liver resections ((e)PHx) pose a high risk of postoperative liver failure, especially in case of preexisting liver disease. The goal of QuaLiPerF is to elucidate as yet unknown interactions between surgery-induced changes in perfusion and metabolic function. We will apply a systems medicine approach to generate a multi-scale model of hepatic perfusion and function, which will prospectively improve prediction of function and risk stratification in patients subjected to (e)PHx.Our project aims are (1) to quantify and improve understanding of the relationship between changes in hepatic perfusion and selected metabolic functions at all relevant physiological scales; (2) to generate validated and integrated spatially resolved dynamical models for robust prediction of liver function after resection and during regeneration; and thus (3) to create the basis for clinical translation into a surgical planning model for prediction of the individual postoperative hepatic function and its course during recovery.We will achieve these goals by integrating systems biology methods and imaging techniques as well as multimodal, longitudinal individual patient data. Due to the broad scope of the project, a joint effort of experts from different fields is needed: experimentalists, clinicians, bioinformaticians, modelers and experts for data integration.Our long-term vision is the translation of the multi-scale model describing perfusion-function interactions into a virtual visualization tool. This will enable a precise prediction of liver regeneration and post-surgical morbidity and mortality already when virtually planning the surgery.