Assessment of Vascular Network Connectivity of Hepatocellular Carcinoma Using Graph-Based Approach

Area of Science:

• Oncology
• Medical Imaging
• Network Science

Background:

• Liver cancer angiogenesis is crucial for tumor progression, invasion, and metastasis.
• Understanding vascular network connectivity in hepatocellular carcinoma (HCC) is vital for therapeutic strategies.

Purpose of the Study:

• To investigate the vascular network connectivity of hepatocellular carcinoma (HCC) using a graph-based approach.
• To quantify topological parameters of the vascular network in HCC models.

Main Methods:

• Orthotopic HCC xenograft and healthy control models were established (n=10 each).
• Hepatic vascular casting and Micro-CT scanning were performed for angiography.
• Blood vessels were automatically segmented, and vascular network modeling was conducted to quantify topological parameters like clustering coefficient (CC), network structure entropy (NSE), and average path length (APL).

Main Results:

• The tumor region in HCC models showed significantly decreased CC and NSE, and increased APL compared to controls.
• The background liver in HCC models also exhibited significantly decreased CC and increased NSE compared to controls.
• No significant difference in APL was found between the background liver of HCC and control groups.

Conclusions:

• A graph-based approach effectively quantifies vascular connectivity in HCC.
• Disrupted vascular topological connectivity is a characteristic feature of both the tumor region and the background liver in HCC.